“Will companies vibe-code systems of record” is the wrong question

Most enterprises intrinsically understand:

• It was always the “service” portion of “software as a service” that was valuable. An MVP is about 5% of the work. Even a production-grade product is only about 20% of the work. The rest is maintaining it: new integrations, features, UX improvements for new users, whack-a-moling edge cases... and a dozen other things.

• The risk (not to mention maintenance cost) of reinventing a core system is not worth the 0.1% of OpEx they’ll save.

Of course smart CIOs won’t make this tradeoff. But just because enterprises, broadly, won’t vibe-code internal systems... doesn’t mean no-one will.

We are seeing a massive surge of new-age SaaS startups who are coming for the systems of record. The pot of gold in each of these categories is huge. Jury’s still out, but they have better odds of winning than they ever did. The giants are vulnerable.

Moat #1: “SoRs are extremely flexible — they work for 100- and 100,000-person teams — which is really hard from an engineering perspective”

• Why it’s fading: Generating a LOT of code to cover every possible edge case is something Codex & Claude are exceptional at.

Moat #2: “Decades of regulatory requirements, compliance rules, and domain expertise are baked into millions of lines of code”

• Why it’s fading: The models are actually *phenomenal* (not perfect, yet — still need to be tested) at subject matter expertise. A week ago, I was building a product from scratch in a domain I’ve spent perhaps 3,000-4,000 hours in. The agents came up with domain-specific insights that were novel enough that I was genuinely surprised.

Moat #3: “Once they’re deployed, the encoded configuration of ‘here’s how my company works’ creates astronomical switching costs”.

• Why it will fade: This is probably one of the last remaining moats... Until it becomes possible for browser-use agents to reverse-engineer and reimplement the specific business processes and policies at your company. This only works for very basic things right now... which means we are ~2 model generations (i.e., 6-8 months) away from it working flawlessly, for highly complicated use cases.

Moat #4: “There is a tremendous ecosystem of Salesforce and Workday experts.”

• Why it’s fading: Legacy SaaS needed a cottage industry of implementation specialists. But agent-native SaaS companies are running an accelerated version of the Palantir playbook with “forward deployed engineers” doing implementations in weeks.

• Why it might disappear altogether: My bet is that newer age software will not require “certified specialists” because they will increasingly “just work”. You don’t need to onboard and train a human on a conversational interface. 12-month deployments will be a thing of the past when “implementation agents” can fully provision an instance in two days.

I believe this is why the markets are repricing legacy SaaS

“System of record” companies typically carry a big premium, because they are very sticky. And the big ones — Salesforce, Workday, ServiceNow, Netsuite, Atlassian — have historically commanded 50-100% higher multiples than less “core” systems.

The 30-50% drop we’ve seen is simply a reversion to the mean; their moats are no longer a mile wide. The fall accelerated in recent weeks, but it actually started 9 months ago.

1. The start of Q2 was the point when Cursor had begun penetrating enterprise, Claude Code was released, Codex was released, and Sonnet 4.0.

2. The fall steepened about 2-3 months ago, which coincided with the release of Composer-1, Codex-5.1-Max, Opus 4.5, and Codex-5.2.

These two moments (Q2 2025 and Q4 2025) were breakpoints. There will be more, likely at an accelerating cadence.

I’m a believer in efficient markets. This isn’t the effect of a single event or a single market participant; it’s just that the market has become bullish on foundation models, agentic applications (more on this soon), data centers, and hard tech... and legacy SaaS gradually fell out of favor.

Cheaper code means more challengers, and buyer leverage

Price is set by the marginal (comparable) supply.

When a barrel of oil fell from $106 to $26 / barrel in 2014-15, what do you think the oversupply was in the market? 5%? 10%?

It was 1%.

Software is not a commodity like oil, but all pricing is market-driven. We are already seeing an explosion of software especially in the major system of record categories, even if none of them have unseated the 800lb gorillas.

Yet.

Give it a couple of years. Rippling and HiBob are already giving Workday a run for their money in mid-market / small enterprise. What do you think will happen when there are TEN such challengers in 2030? We’re already seeing this in the GTM stack with probably 5-10 upstarts challenging Salesforce and Hubspot.

You still think Workday, ServiceNow, and Salesforce will command the same pricing power they do today?

All it takes for them to lose a few deals here and there... and their hand will be forced. They’ll start discounting. They’ll hesitate to increase prices during a renewal. The sales narrative of “where else are you gonna go” will unravel, and customers will smell blood in the water. The moat is drying up, and the fortifications are showing their age.

This potential weakening is already factored into the reversion to the mean, but it will also show up directly in fundamentals. A System of Record company with

• 15% growth will trade at ~10x revenue (exactly where was trading in May last year)

• 8% growth will trade at ~8.5x revenue ( was at 8x)

But an application layer company (something that is more hot-swappable) growing at 5% with 70% gross margin due to pricing pressure trades at ~4x revenue. This is a hypothetical bear case, so we haven’t seen a drop this steep. But if any of these companies see falling renewals, gross margins, or pricing power... that’s where the multiples will go.

And so... a Heat-Death

If this weren’t enough, there’s a big “why now”. The ace up the sleeves of the AI-native companies is that they will be good at building agentic, augmented experiences, and incumbents will not. I’ve backed several companies based on this thesis — Monaco (totally different vibe to Salesforce), Puzzle vis a vis Quickbooks, or Rillet against Netsuite.

However: I’m not saying these companies will go quietly into the night. Every single one of these companies now lists themselves as an “AI-native X” or “Agentic Y”, but I’m yet to see a single example of how Workday is actually “The enterprise AI platform for people, money, and agents.” (???)

But few (if any) will make the transition into being a market leader in the AI era, because what it takes to succeed as an “AI Native system of record” is totally different from what it took to succeed as a “cloud system of record”.

The companies that will win this next era will need to serve as an Agent Substrate. This does not mean slapping a chatbot on the legacy interface. (If you’re wondering what the hell I’m talking about... Part II coming tomorrow: the Rise of the Agent Substrate).

I do not believe legacy SaaS companies will make the leap to the new paradigm of selling output + outcomes, instead of software... any more than IBM made the leap to being a “cloud” company successfully — their Red Hat acquisition notwithstanding.

“But wait!” you might say. “IBM has 9xed in the last 40 years!”

But the Nasdaq has 70xed in that time. Legacy SaaS companies will not go to zero, but they will gradually become irrelevant. And it will look and feel a lot worse, because everything is faster now; this decline won’t happen over 20-40 years, but rather over the next 5-10 years.

The heat death of the universe is the projected final state where maximum entropy is reached, resulting in a cold, dark, and empty cosmos.

Software ate the world, and now agentic coding is eating software. That’s how legacy SaaS companies will go. Not with a bang, but with a whimper.

First, we should acknowledge Dan Primack’s reasonable analysis: he observes that no statement of fact in the article is false, but it’s also nothing new and nothing nefarious.

But his analysis forgives the four pieces of journalistic malpractice on display:

1. People have short attention spans, and journalists have a duty of care not to imply things that aren’t there. This care is not exercised.

2. In fact, the writers do the opposite — the phrasing intentionally twists the facts to suit the narrative.

3. Articles should educate readers on technical topics with nuance (Special Government Employees, private market divestitures). Here we see the opposite: the arcaneness is used to mislead the public.

4. Throughout the piece — and in fact, the headline and subheading — the article insidiously implies wrongdoing that appears clearly false.

I say that with confidence because none of the facts in the article substantiate the headline. If you have 3,000 words (backed up by months of work from 5 journalists) to prove something… but then you don’t prove it… then it means you can’t back up your accusations.

DISCLAIMERS

I’ve met David Sacks a handful of times, when Craft invested in a company I founded. We are an inconsequential position to Craft (<0.1% AUM) and I am not particularly beholden to Craft or him. My company is not trying to raise money. No one asked me to write this. I write this because the journalistic malfeasance bothers me. I’m a registered independent and have routinely criticized both the Trump and Biden administrations.

Okay, now on to everything that’s wrong with the NYT article.

OUT THE GATE, SWINGING WITH ALLEGATIONS

[David Sacks] had convened top government officials and Silicon Valley executives for a forum on the booming business of artificial intelligence. […] Almost everyone in the high-powered audience — which included the chief executives of the chip makers Nvidia and AMD, as well as Mr. Sacks’s tech friends, colleagues and business partners — was poised to profit from Mr. Trump’s directives.

So…

• the AI + Crypto czar actually knows people / has worked with other people in AI + Crypto

• the campaign that ran on the promise of being tech-friendly convened a forum to deliver on the promise

Got it.

Since January, Mr. Sacks, 53, has occupied one of the most advantageous moonlighting roles in the federal government, influencing policy for Silicon Valley in Washington while simultaneously working in Silicon Valley as an investor.

This is the very definition of how a Special Government Employee role is supposed to operate.

1. SGEs allow the government to access highly specialized skills or domain knowledge without the need for long-term or full-time hiring.

2. SGEs are still subject to ethics laws, but some conflict-of-interest rules are relaxed given their limited duties.

3. SGEs on advisory committees can participate in matters of general applicability even if their employer is part of the affected industry.

If the NYT has an issue with the rules of the role, they should take it up with Congress.

Mr. Sacks has recommended A.I. policies that have sometimes run counter to national security recommendations, alarming some of his White House colleagues and raising questions about his priorities.

Mr. Sacks is not the national security advisor. He’s the AI advisor. His job is to recommend AI policies, and it’s the administration’s job to decide what is the best path across all factors.

Also — any competent official who has never caused concern is probably not very good at what they do. Real policy is bold.

Mr. Sacks has positioned himself to personally benefit. He has 708 tech investments, including at least 449 stakes in companies with ties to artificial intelligence that could be aided directly or indirectly by his policies, according to a New York Times analysis of his financial disclosures.

Note the tense: “Mr. Sacks has positioned himself,” implying that he has taken actions to benefit. In fact, he has provably taken several actions to reduce his benefit.

You’re probably asking “why is that benefit not zero?” Fair question — this has to do with how private markets work (an area I have some expertise in). We will talk about his incomplete divestiture below.

In the meantime, this statement can be boiled down to “we appointed someone who has an incredible track record in the areas he’s been asked to provide advice.” That… seems like a good thing.

His public filings designate 438 of his tech investments as software or hardware companies, even though the firms promote themselves as A.I. enterprises, offer A.I. services or have A.I. in their names, The Times found.

This is probably the worst offender in an overall shoddy article.

“AI” is not an industry category. Go look at SIC codes, or the S&P industry categories, and tell me where “AI” appears as a classification. AI is an underlying technology. Almost every software company today is repositioning itself to be an AI company. It makes complete sense that the terms are converging.

The New York Times has a comments section on articles. Should we start referring to them as a social media company?

Just like the comments section on the NYT is a way to enhance the core value prop (media and publishing), AI is a way to augment the core value proposition of software or hardware.

Mr. Sacks has raised the profile of his weekly podcast, “All-In,” through his government role, and expanded its business. […] No event better illustrates Mr. Sacks’s ethical complexities and how his intertwined interests have come together than the July A.I. summit. Mr. Sacks initially planned for the forum to be hosted by “All-In,” which he leads with other tech investors. “All-In” asked potential sponsors to each pay it $1 million for access to a private reception and other events at the summit “bringing together President Donald Trump and leading A.I. innovators,” according to a proposal viewed by The Times. […] The plan so worried some officials that Susie Wiles, the White House chief of staff, intervened to prevent “All-In” from serving as the sole host of the forum, two people with knowledge of the episode said.

When reading this, I thought this was a serious concern if true. It’s certainly worthy of questioning and clarification. But Sacks’ (PR + legal) rebuttal certainly provides that clarification:

• The sponsorships were solely to defray event costs

• Susie Wiles’ intervention was precautionary, not reactive to misconduct

• There was no financial gain for Sacks or All-In; the event operated at a loss

• The hypothetical private reception was a non-profit endeavor which didn’t materialize

Nevertheless, this might be one of the only reasonable concerns raised by that article. I would still argue that if this were the standard, no public figure could ever hold government office because it “raises their profile.” Reasonable minds can disagree, though.

Steve Bannon, a former adviser to Mr. Trump and a critic of Silicon Valley billionaires, said Mr. Sacks was a quintessential example of ethical conflicts in an administration where “the tech bros are out of control.” “They are leading the White House down the road to perdition with this ascendant technocratic oligarchy,” he said.

Oh, NOW you’re buddy-buddy with Bannon? “The enemy of my new enemy is my friend,” eh?

Bannon is clearly annoyed that his race-focused agenda has been overruled by a more techno-optimistic one. I think this is a huge step in the right direction by the administration.

APPARENTLY WE’RE NOT OVER THE WHOLE “BE ASHAMED OF SUCCESS” THING

Mr. Sacks stands out as a special government employee because of his hundreds of investments in tech companies, which can benefit from policies that he influences. […] His public ethics filings, which are based on self-reported information, do not disclose the value of those remaining stakes in crypto and A.I.-related companies. They also omit when he sold assets he said he would divest, making it difficult to determine whether his government service has netted him profits. […] Mr. Sacks has complied with special government employee rules and the Office of Government Ethics determined that he should sell investments in certain types of A.I. companies but not others, she said.

Remember: “SGEs are still subject to ethics laws, but some conflict-of-interest rules are relaxed given their limited duties.”

This is a judgment call for the OGE to make, which appears to be exactly what happened. I’m not an expert in OGE disclosure forms, but a quick review indicates that these are in-keeping with requirements for an SGE. If this is improper, I assume the NYT would have said so, plainly.

At a White House dinner for tech executives in September, Mr. Sacks said he was grateful to work in both technology and government. It was “a great honor to have a foot in each one of these worlds,” he said.

OK. This just describes how an SGE works: one foot in their industry, and one foot in government. The industry expertise is the point.

After eBay bought PayPal for $1.5 billion in 2002, [David Sacks, Peter Thiel, and Elon Musk] invested in one another. [...] In 2017, Mr. Sacks began Craft Ventures, a firm that has invested in hundreds of start-ups, including some owned by his friends. He also started the “All-In” podcast three years later with friends and fellow investors Jason Calacanis, Chamath Palihapitiya and David Friedberg.

This boils down to: David Sacks likes working with his friends, and his friends like working with him. This is stated in an accusatory fashion, but this is actually Just How Things Work when you like your friends and colleagues.

Last year, Mr. Sacks hosted a $12 million fund-raiser for Mr. Trump at his San Francisco mansion. The dinner made an impression on the presidential candidate. “I love David’s house,” Mr. Trump said on “All-In” two weeks later. “What a house.”

Mr. Trump has also been known to say that Mar-a-Lago has the most “beautiful piece of chocolate cake.” Are we going to get an NYT feature on that too?

Mr. Sacks opened the door of the White House to Silicon Valley leaders. Among the most prominent visitors was Jensen Huang, Nvidia’s chief executive. Mr. Sacks and Mr. Huang, who had not met before Mr. Sacks joined the administration, forged a tight bond this spring. [...] And spreading Nvidia’s technology would expand the A.I. industry, aiding the A.I. investments owned by Mr. Sacks and his friends.

Nvidia is the most valuable company in the United States and the world. They are a cornerstone of the most important technological revolution of our lifetimes — and a matter of critical national security.

I’m pretty sure Jensen Huang would be able to get an audience without David Sacks’ intervention.

Along the way, the AI czar advocated for the AI industry.

I’m curious: is it also considered wrongdoing when a teachers’ union meets with the President to advance their views on education policy?

In White House meetings, Mr. Sacks echoed Mr. Huang’s ideas that the best way to beat China would be to flood the world with American technology. Mr. Sacks worked to eliminate Biden-era restrictions on Nvidia and other American chip companies’ sales to foreign countries. […] Mr. Sacks flew to the Middle East in May and struck a deal to send 500,000 American A.I. chips — mostly from Nvidia — to the United Arab Emirates. The large number alarmed some White House officials, who feared that China, an ally of the Emirates, would gain access to the technology, these people said. […] Mr. Sacks trumpeted the Emirati deal on “All-In” in May. “I would define winning as the whole world consolidates around the American A.I.” companies, he said. […] In the White House, Mr. Sacks promoted the idea that the ban inadvertently helped China by diverting chip sales to Huawei, a Chinese rival to Nvidia, four people familiar with the discussions said. […] In July, Mr. Sacks and Mr. Huang took their argument to an Oval Office meeting with Mr. Trump. Before the meeting ended, Mr. Trump cleared Nvidia to sell its chips to China.

Look, I think it’s fair to have a diametrically opposite policy perspective. Geopolitics is complex!

But again: David Sacks is the AI advisor. If you have a problem with this foreign policy, that’s a valid policy decision to litigate with President Trump or Secretary Rubio.

Sacks’ spokeswoman specifically clarified that none of his holdings benefited. I don’t know what else one can reasonably ask for, here.

TO DIVEST, OR NOT TO DIVEST

The White House has praised Mr. Sacks, saying he minimized his financial conflicts of interest. The ethics waivers that Mr. Sacks received said he and Craft Ventures had sold more than $200 million in crypto positions, including investments in Bitcoin, and were divesting stakes in A.I.-related companies including Meta, Amazon and xAI. Mr. Sacks had started or completed sales of “over 99 percent” of his “holdings in companies that could potentially raise a conflict of interest concern,” the White House said. […] What is clear is that Mr. Sacks, directly or through Craft, has retained 20 crypto and 449 A.I.-related investments, according to the Times analysis.

For an “elite” newspaper, this is either hilariously ignorant or willfully ignorant.

These are not public market positions that you can simply sell. A co-GP cannot leave without borderline-blowing-up the firm.

I don’t know the specifics, but here are the entanglements one can infer from the context:

1. Every VC firm has a share of the fund that the General Partners are putting in themselves. For a firm with several billion dollars in AUM, a typical GP’s commit would be eight figures (easily). Marked to market, we are probably talking about >$100M in holdings. It’s hard to find buyers for this sort of position, and when they’re sold, it’s usually at a 10-40% discount.

2. David Sacks is no doubt a key person at the firm. You can’t simply “divest” without massive governance, legal, financial, and operational implications for Craft — not just David Sacks but all the other people who work there.

3. He has dozens of angel investments and LP positions from before he started Craft Ventures. These tickets are probably smaller, but there will be a TON of them. Imagine trying to sell a $200K position in a Series B company. There is essentially no secondary market for a position like this.

4. There is no way to put this stuff in a blind trust! it’s not like he can forget all the companies he’s invested in.

The fact that David Sacks has hundreds of direct + indirect positions makes him uniquely qualified to serve this role. But that very qualification cannot become a handcuff.

You can disagree with his worldview; but treating him as a Bond villain whose every action is a cash grab is unserious. People with hundreds of pre-existing, illiquid positions cannot magically divest from their entire life’s work, nor should we pretend that every policy preference is secretly a personal payday.

Mr. Sacks has stakes in defense tech start-ups such as Anduril […] Anduril announced a $159 million contract with the U.S. Army to build a new type of night vision goggles with A.I. Shannon Prior, an Anduril spokeswoman, said that the company had a relationship with the Army before the A.I. Action Plan and that it received the contract because its founder, Palmer Luckey, is “the world’s best virtual reality headset designer.” Ms. Hoffman said it was an “obvious idea” to include the military use of A.I. in the policy plan.

If I were the recipient of these questions, I would have been a lot less patient.

This spring, Mr. Sacks also backed a bill called the GENIUS Act to regulate stablecoins […] Mr. Sacks promoted the legislation on CNBC and worked to advance it through Congress. […] One of Craft’s crypto investments is BitGo, a company that works with issuers of stablecoins. […] In September, BitGo filed for an initial public offering. Craft owns 7.8 percent of the company, according to financial filings, which would be worth more than $130 million at BitGo’s 2023 valuation.

I feel like I’m beating a dead horse here.

• The AI & Crypto czar advocated for pro-AI and crypto policies.

• He divested positions according to proper filings and ethics reviews.

• SGEs on advisory committees can participate in matters of general applicability even if their employer is part of the affected industry.

BitGo declined to comment. Ms. Hoffman said the GENIUS Act’s passage “contained no specific benefit for BitGo.”

I believe this is called “general applicability”.

Since Mr. Sacks joined the White House, A.I. companies have continued to announce new investments from Craft. […] The funding was secured before Mr. Sacks joined the administration, said Mac Liu, Vultron’s chief executive. […] Mr. Sacks had left corporate boards before joining the Trump administration, but stayed on Glue’s because “it was allowed,” Ms. Hoffman said. The funding was completed last year, she said. Glue did not respond to a request for comment.

I’m trying really hard not to use all caps here.

Deep breath

Why are we talking about financings that happened before he joined the administration?

Nice try with the clever use of “continued to announce new investments.” Because it would be false to claim Craft made these investments since. But by citing these examples anyway, it makes it seem like Craft is making conflicting investments.

But wait! Even if they did! Craft doesn’t have to shut down the entire damned firm! They’re allowed to keep operating! And David Sacks is allowed to maintain those stakes!

Mr. Sacks’s government work has boosted the profile of the [All-in] podcast, which is downloaded six million times a month. Its annual conference in Los Angeles generated roughly $21 million in ticket sales this year, up from $15 million last year, based on its $7,500 ticket price and public attendance estimates. In June, the podcast introduced a $1,200 “All-In”-branded tequila. Mr. Sacks has forgone A.I. and crypto-related revenues, such as from sponsorships, but can share in sales from tequila and event tickets, Ms. Hoffman said.

What are we even talking about here.

Is David Sacks the tequila czar?

No?

Cool. Let’s move on.

Mr. Sacks’s personal business and policy work came together at the July A.I. event in Washington, which he tapped “All-In” to host. [… literally a repeat of the ostensible conflict from earlier in the article]

Ha. I missed this during the first read, but it’s nearly a word-for-word rehash.

I guess when you’re filling a 3,000 word nothingburger column that talks in circles, you can repeat (baseless) allegations without people noticing.

In the keynote speech, Mr. Trump described Mr. Sacks as “great” before signing executive orders to speed the building of data centers and exports of A.I systems. Then he handed Mr. Sacks the presidential pen.

You mean… one of the hundreds of pens Presidents give away every year?! Gasp! Say it ain’t so! Are we worried that David Sacks might sell it on eBay to enrich himself?!

This entire article presumes that rich people only care about money. David Sacks has a lot of money. He’s allowed to have opinions and philosophy too. You can disagree with those ideologies… without painting him as a cartoon capitalist in a bowler hat twirling his mustache.

———

Imagine a local newspaper that wrote:

Headline: “Local Surgeon Endangers Patients With Sharpened Scalpel Use.”

And then in the body of the article, you learn:

• the surgeon used the right scalpel, the way surgeons always do

• the hospital’s chief of surgery supervised the surgery

• “some staff expressed concern because it was touch and go”

• the surgery was successful and the patient was healthy

Every sentence in the body is technically true. It would also be outrageous to imagine a respected outlet publishing anything like it. Yet… the NYT writes something like this, routinely.

I don’t know why we’ve all collectively decided we’re okay with this from (what is ostensibly) the leading American publication.

We deserve better.

I blurted out “Oh for me, that’s software. I fucking LOVE beautiful software.”

(I know how bougie that sounds. This isn’t a value judgment — just an observation about how any discipline that blends form and function evolves. While I’m a techno-optimist, you don’t need to be one to see the pattern.)

The earliest era of the existing functional arts / crafts — furniture, clothing, food, architecture — were essentialist. They just checked off the basics. Furniture must be usable. Clothing must keep you warm. Dining must nourish you. Architecture must shelter you.

Since the medieval era, these forms have become increasingly a blend of form and function; and in some cases form for its own sake. Increasingly inventive, and in some cases utterly breathtaking.

In the modern era, we’ve taken these to incredible heights: materials science in furniture, impossible fabrics and designs in clothing, molecular gastronomy in fine dining, 3D printing in building... these sort of third-wave developments either redefine the boundaries of utility, or strike emotional chords (or both).

More modern forms of art / craft went through similar waves. Photography started with taking a picture of your family, then iconic photo shoots blended form and function, and now we have breathtaking possibility (HDR photography, DSLR-quality photos in your iPhone). Automobiles went from “it will get you from point A to B” to the rise of beautiful, powerful European vehicles… to breathtaking innovation both in the form of modern EVs and luxury supercars.

All of these forms eventually reach a natural steady state:

1. The barriers to entry have collapsed: anyone can 3D print, invent flavors, or shoot professional-grade photos on their phone.

2. A new hierarchy emerges: the elite know each other’s work (Michelin-starred chefs, Ferrari engines, bespoke clothing...). Luxury develops its own set of tells.

Or to quote Ratatouille:

We’re similarly speedrunning this with software:

• 1940 – 1990: the engineering era (get it working)

• 1990 – 2020: the usability era (form + function)

• 2020+: the breathtaking era (functional art)

In the last few years we’ve started seeing the rise of truly beautiful software (a non-exclusive list of examples: Linear, Notion, Mercury visually; Ramp, Cursor, Superhuman in reinventing intuitive workflows; Rippling, Zapier in ambitious architecture). I think this is just the initial wave. All these companies were formed in the last ~10 years.

It’s pretty clear to me that software is making that leap from functional → form + function → art form.

As with all functional art forms, the frontier is defined by software that expands what’s possible, and by software that makes you *feel*. And every so often… you find one that does both.

Catalysts inspire a wave of customers or users to change how they operate. Airtable, Notion, and Figma are recent examples of this. There were “relational spreadsheets” like Microsoft Access before Airtable; “company wiki” products before Notion; and product design tools like Sketch before Figma. But these new players were each so gamechanging in their capabilities that users didn’t consider the competition to be viable alternatives.

Second acts

Since Catalysts are supercharged Product companies, their only lock-in is their product quality (prone to attracting copycats), so a second act is essential. Fortunately, they have…

1. so little competition they have the mental space to build (not just brawl over sales)

2. so much credibility and mindshare with customers they’ve “earned the right” to do more

Many durable companies have made the leap from Catalyst to Compound over the years (Datadog, Adobe, Workday, Stripe). All of them added product lines: Workday’s HRIS, Financial Management; Adobe’s Illustrator, Photoshop, Lightroom; Datadog’s performance monitoring, log management. Stripe had some false starts (e.g., Stripe Treasury), but other products such as Issuing and Connect have rapidly scaled into large businesses.

Product line acquisitions can also keep pushing this: Workday acquired Adaptive Planning; Adobe acquired Marketo, EchoSign, Behance, Frame; Stripe acquired TaxJar, LemonSqueezy.

• Figma and Notion → partway in going Compound (adding several additional product lines).

• Airtable → initially bet on a Composite approach (Blocks / Apps) → shifted more compound recently, with Interfaces (Retool-like), Automations (Zapier-like), and Omni (AI builder).

• Slack → went Composite over time (Slack Connect, Slack Apps) successfully

• OpenAI → going Composite didn’t quite take (custom GPTs, integrations) → went Compound with Codex CLI, Sora, Whisper → trying Composite again with AgentKit and Apps in ChatGPT.

Failure modes

Catalysts rarely fail for going too narrow. The most likely reasons they fail are the product a) had too steep a learning curve, or b) wasn’t differentiated or magical enough for users to care.

Miscellaneous observations

While Catalysts used to appear in any category, in the last decade or two, Collaboration and Engineering are categories that most often produce Catalysts; mostly because their early adopters tend to be technical, creative, or other founders.

Between Airtable and OpenAI: do these anecdotes indicate that Catalysts should probably go Compound, not Composite? Even Slack’s acquisition wasn’t enough to stave off an acquisition.

It’s often necessary for Catalysts to create their category. But they often spawn a community of early adopters and evangelists, and benefit from viral, product-led, and community-led growth.

Compound Startups

When operating in a mature industry with well-understood needs, a Compound startup can raise a lot more money, lay shared foundations, and simply rebuild table-stakes very quickly. Rippling did this with payroll, benefits, PEO, HRIS, ATS, performance, EOR, SSO, device management.

I don’t think it’s an accident that most of those products are acronyms; that’s how well defined the product categories are.

Any of these products stands alone, but they usually share a customer or industry which allows them to be effectively cross-sold, with better lock-in — creating product network effects.

SMB/mid-market is much better suited to a Compound product, because enterprises are notoriously prone to fragmenting their software stack. Enterprises are happy to purchase software that solves that one tiny need better than an all-in-one can: “our team uses 16 different tools, I don’t mind buying a 17th if it solves a problem.” When you have hundreds of people on a team, there are plenty of people to operate 10+ tools. Not so at 50- or 200-person company, where an all-in-one, Compound solution is preferable.

Other recent Compound startups

• Ramp, Mercury, Brex (cards, banking, expenses, AP, travel, procurement…)

• ClickUp (tasks, docs, forms, automations…)

• Vercel (compute, rendering, observability…)

• PostHog (product analytics, surveys, tutorials, feature flags…)

Every single product in each of those categories is well-trodden ground.

Failure mode: the ambition trap

It’s easy for a Compound startup to fall into the trap of trying too much, too soon.

Zenefits didn’t fail due to “compliance”; It failed because its operations couldn’t keep pace with its ambition. There were too many product lines too soon, and too little coherence. Before too long, the wheels came off. This no doubt played a role in Parker Conrad deciding to approach the multi-product problem in a more methodical, patient way with Rippling.

Miscellaneous observations

Interestingly, a Compound Startup still needs to have a terrific first act, or wedge; even with all the money in the world, it still takes a while to build out more than 2-3 categories. Rippling built several products from day one, but the value proposition was still very targeted for the first 5+ years (“employee onboarding” from 2017 through 2020; “PEO” from 2020 through 2022).

In order to do this successfully, the company probably needs to raise a lot more money. This is why serial founders are more likely to build a compound or “fat” startup, versus a “lean” startup.

Financial services is intrinsically a Compound industry. A checking account, credit card, or auto loan is extremely well-defined; and they can be cross-sold very effectively. So every bank offers every product, and tries to grow the relationship with customers to consolidate in one place.

The decay from Compounding can happen over time, too. Symantec, Dell, and Apple all struggled due to bloat, lost focus, and disjointed products — leading to PE exits or turnarounds.

The Composite Startup

This is often relevant for well-developed categories in the mid-market and enterprise. These companies already have a bunch of tools; the biggest problem is not doing an isolated task effectively (most of those problems were solved by Catalysts or Compound companies years or decades ago), but rather in stitching together data or workflows across multiple systems.

A Compound startup wins by providing better cross-functional and cross-system:

• visibility (intelligence, insight, reporting), and

• control (approvals, anomaly detection, risk management, decision-making)

Typical Characteristics

They grow in value as additional partners or applications are plugged in, producing ecosystem network effects.

For the same reason that Compound Startups are less suited to enterprise but can dominate SMB (fragmented, specialized teams and buyers), Composite Startups thrive in enterprise use cases but will find it harder to tackle SMB (where buyers will pull them to divert focus into lots of product lines). Both styles overlap and intersect in mid-market.

Prominent examples

Salesforce is probably the most famous Composite company. Their service-oriented architecture and App Exchange produced an ecosystem that has made them a hard-to-unseat owner of the customer record, for decades.

Categories where we see a lot of companies go Composite are data warehouses, business intelligence, and workflow automation.

• Snowflake and Databricks are a data integration play at the core; they unify, transform, and clean to enable multiple use cases. Segment is similar, specific to the customer data pipeline.

• Looker and Tableau are similar, as visualization layers.

• In workflow automation: Zapier’s very existence was prompted by fragmentation of data across thousands of applications. ServiceNow, Zip, and Clay are similar in their value proposition (albeit vertical-specific: IT, procurement, and GTM respectively).

Failure mode

The failure mode for a Composite Startup is the lack of a good wedge. There are exponential combinations when combining data from multiple sources; they’re brimming with possibility, so it’s easy to drown in all that possibility, too — when there isn’t a clear first use case.

The (hard) way to solve this is by providing the user the tools to build, themselves. This works for technical users; otherwise, users get overwhelmed in the absence of pre-built use cases.

For example, Airtable was a successful Catalyst, but their attempt to build a Composite approach (with extensions) didn’t take off because there were too many things a user could do; their typical user was not technical enough to build their own solutions. On the other hand: Retool tailored to technical buyers (data engineering / internal apps teams). They also had a great set of initial use cases: customer operations and billing. (We still await Retool’s second act.)

Next up

In the final post tomorrow, we’ll look at patterns: which combination of market environment and industry are most suited to each style of building? How does each type of startup typically stage its second and third acts?

Sneak preview below. Thanks for reading!

• Customers like everything in one place

• You can build a product that is top tier (but not obviously #1); and still win the deal because you offer centralization

• Being multi-product this way is a more efficient way to build (certain types of) companies — cross-selling makes go-to-market more efficient, and building shared infrastructure and components makes product & engineering more efficient

  

This perspective flies in the face of traditional wisdom, which is to focus and to build a wedge that customers gravitate to. This “lean” strategy also lends itself to iteration; building a compound startup is a much bigger risk. If the founder’s vision is wrong, the company is probably done. So why do a Compound startup?

1. Generational companies are usually platforms.

2. Building a product (contrasted with a platform) runs the risk of getting trapped at a local maximum — and never graduating to a platform.

3. And if you really want to swing for the fences, targeting the untapped customer need to solve several problems in one place is a very good way to build a category-defining platform.

However, it’s not the only way.

Not-Platforms (= Product Companies)

Before we get to the different ways to build a platform, I should clarify the distinction between platforms and not-platforms.

Conventional wisdom:

• Start with a “wedge,” or initial product that is quick to get adoption.

• Build a “better mousetrap” — a new solution to an existing market, or identifying a narrower, better sliver to unbundle an existing solution.

• If the new solution solves some critical aspect of the problem 10x better, it will get adoption.

The risk here is that the low barrier to entry. If you can do it, someone else can, too. The first-mover’s success will draw others, and probably result in a feature arms race. This makes a massive success harder. The most probable result, then, is a Product Company.

Additional product lines are negligible to their revenue, and don’t necessarily compound or cement each other in a way that defends well against competition. As a result, Product Companies typically operate in a competitive market.

If the category is large enough, a product company can still be big; Dropbox, Box, Mailchimp, and Qualtrics are good examples.

Cloud storage is a particularly good example of a category that induces product companies.

• Hard technical problem to solve well → which consumes all their development attention (meaning no bandwidth to build multiple products).

• Feature arms race on the core product (specifically, storage and latency).

• Shoe-horning a platform in is always hard; so, unsurprisingly, later attempts to extend into a platform are harder because they didn’t start out that way (e.g., Dropbox Paper).

• The result is a competitive category: Dropbox, Box, Google Drive (and OneDrive, I guess).

This is not forever; product companies will seek repeatedly to make the leap to a platform. In the case of file storage, recent attempts include a) acquisitions (e.g., HelloSign & Docsend for Dropbox) and b) allowing users to run LLM workflows on top of documents.

Platforms

Contrasted with a Product Company, a Platform Company is a business that achieves durability through multiple reinforcing products or integrations

There are three paths to achieving this: Compound, Catalyst, and Composite. Each path implies different day 1 choices, core competencies, buyer personas, and failure modes.

• A Catalyst Startup solves a novel problem, creating a “paradigm shift”

• A Compound Startup build a multi-product suite from the get-go

• A Composite Startup builds connective tissue across fragmented data / workflows

These archetypes are intermediate, not final states. Just as Product Companies seek to become Platform Companies, each flavor of Platform will naturally seek to nail multiple of these over time.

For example, a company may start as a Catalyst, then build network effects in the market to become more Composite over time. Another may start by building Composite use cases, then adding additional modules to go Compound.

Enduring technology companies (e.g., Microsoft, Google, Salesforce) have done all 3 in some order; and the best companies in the last 10 years are onto their 3rd act (Stripe, OpenAI).

Next up

Tomorrow, we’ll dive into each of the three types of companies, and the day after, we’ll analyze how market environments are more or less hospitable to each style of building.

God-complex CEOs

God-complex CEOs see themselves as the embodiment of the purpose. They are the purest carriers of the mission, culture, and essence of the company. Their word is absolute and their means are sometimes ruthless. They are obsessed with winning — and usually inspire and attract those who are similarly type-A and competitive. These CEOs are typically charismatic and personally compelling, which leads to a cult of personality. Other talented people, especially those who prize autonomy and agency, struggle in god-complex-driven companies. These CEOs tend to win… but sometimes also flame out. God-complex organizations tend to falter due to hubris and paranoia.

Travis Kalanick is probably the most iconic recent god-complex CEO. Adam Neumann is probably not far behind.

Steve Jobs, Jensen Huang, and Mark Zuckerberg are probably a mix of god-complex CEOs and the second type…

Messiah-complex CEOs

For messiah-complex CEOs, mission comes first. There is some greater purpose — a change they wish to effect in the world — and they see themselves as simply a vessel for that mission. Their organizations often feel more like movements than companies. These CEOs and their cultures are often customer-obsessed (more so than the other types, that is), and tend to attract people who are drawn to purpose, autonomy, and talent-density. Messiah-complex teams tend to stumble due to delusion and drift.

Elon Musk is the most prominent exemplary messiah-complex CEO. Others include Brian Armstrong, Brian Chesky, and Alex Karp.

Jeff Bezos, Marc Benioff, Tobi Lutke, Patrick Collison, and Melanie Perkins are mostly messiah-complex CEOs, with shades of the next type, which is…

Emperor-complex CEOs

These CEOs are company-first; they care about building large, meaningful businesses. This is not to say that they don’t care about customers or winning; every good CEO cares about those things. However, what they care about the most is creating value and solving problems — they’re relatively agnostic about which problems they solve. They’re good at building systems and machines that compound as they scale. Unlike the other two types, they see succession as part of success — they often step down after a good run. The best non-founder CEOs are likely to have the emperor-complex, too. Emperor-complex companies tend to decay due to bureaucracy.

Larry Page, Warren Buffett, Bernard Arnault are all emperor-complex CEOs.

Other observations

It’s fascinating to see which early-stage investors self-select into each type of founder.

Benchmark is overrepresented in god-complex CEOs; Founders Fund and YC in messiah-complex CEOs; and Sequoia in emperor-complex CEOs.

Intuitively, it feels like these three founder axes map to similar axes for investors: high-variance versus ideological versus systematic.

Investors who like high-variance (like Benchmark) hunt outlier founders with explosive beta; those seeking ideological founders (like Founders Fund) back those motivated by their worldview; and systematic investors (like Sequoia) favor disciplined builders of enduring institutions.

A curious trend: the correlation between these types and “fireable” founders. Benchmark picked up a reputation for “firing founders” over the last 10 years… but it’s possible that there was a confounding factor. As a high-concentration, high-conviction fund, it’s possible Benchmark needs to seek out high-variance founders because they get fewer at bats. This means they simply tended to be attuned to hyper-compelling, competitive, charismatic founders… who win big or flame out (sometimes both).

On the other hand, I’d hypothesize that Founders Fund avoids investing in these founders, because they have an explicit ethos that they will never, ever fire a founder — so they’d have to develop a spidey sense to avoid god-complex CEOs who often need-to-be-fired.

Finally, it appears many of the most iconic CEOs and enduring companies live at the intersection of the God x messiah or messiah x emperor complexes: Jobs, Zuck, Jensen, Bezos have all founded $T+ companies. It would seem even unusual, exceptional people mellow and find balance over time.

Here’s my story of 11 years in startups — first as an early employee, then as a founder. Mostly focused on personal finance (equity, salary) with a sprinkling of the non-monetary stuff.

Betting big

I joined a startup in 2015 as the first employee. This meant a pretty big haircut on base salary, from the ~$180K / year that I’d just made as a management consultant in Chicago, to $100K / year in San Francisco.

$100K doesn’t go far in San Francisco; I had 2 roommates and didn't live in an expensive area, and I was still barely breaking even.

My original offer was 0.5% equity. I negotiated that up ~2x, to ~1%. The founders said the company was currently raising at $25-30M, so this translated to ~$300K of equity over 4 years (approximately compensating for my paycut).

In the years that followed, I loaded up on equity. In fact, I asked for a 50% paycut in 2016, to get the difference in more shares. Hilariously: I did this when the company was in the middle of an SEC subpeona, and our revenue had gone to ~$0 because we had suspended trading operations. That's how bullish I was. A little while later, I also scrounged up my ~$50K in life savings to buy secondary shares from one of my colleagues who was in a cash crunch.

After the SEC stuff was sorted out, the company grew rapidly. Between 2016 and 2018, we grew from ~$0 to ~$30M in annualized revenue, with a team of ~20. I didn’t believe in commissions for early employees in a brokerage business. We weren’t building a boiler room, we were building an exchange; so I always pushed back on the idea. This meant that none of the employees benefited from the hundreds of millions of dollars in deals we closed (except for one; unknown to me, one of the team members had negotiated a secret deal with the founders). At any rate, I’m not sure if I’d have let myself benefit from the ~$10M in revenue I was responsible for closing; I was the COO and I really believed — even if we were to do commissions — that executives should eat last.

Many of these choices feel naive in retrospect, but I don't regret a thing.

I left in 2018, after... irreconcilable differences with the founders. I left with a sense of peace, knowing that I couldn’t affect the future of the company anymore, but I gave it my all when I was there.

This is why it rankles me sometimes when people idolize founders... I was “just an employee,” but I embodied founder mode.

Founder mode is a mindset, not a birthright. This experience inspired another piece I wrote, An Ode to Early Employees.

Looking back, I wish I’d understood some of the personal sacrifices better:

• Crazy hours: I averaged one all-nighter a week, and slept in the office all the time. 996 would have sounded like a vacation.

• Psychosomatic conditions: excruciating back pain, shout out to Dr. Luck in SoMa for fixing it; waking up in a cold sweat so often I started putting a towel under myself when I went to sleep; routine panic attacks by 2018.

• Strain on personal relationships: my wife said on our honeymoon that she never wanted to go on a vacation with me again. (Over the years since, I’ve figured out some semblance of balance and sanity.)

How it played out

I think most early employees understand that, in almost all cases, it doesn't work out. Most early stage startups shut down, or get acquired for a nominal amount which doesn't clear the preference stack.

In our case, it "worked out" — the company went public for $2B in 2022. Hooray!

Object lesson: dilution is often an overrated boogeyman, but in some cases it can have a massive impact. Acquisitions, founders cashing out a lot of secondaries, large executive grants, and a healthy IPO offering… those things can kill the multiple. What was, on paper, a 80x in the valuation turned into a 12x in the share price.

Compare the black line and the yellow area below — they start in lockstep; the divergence over time is the impact of dilution:

But wait! For a brief while, it spiked to nearly a $10B market cap in the public markets. Woo!! This is how it's supposed to go. Maybe it's not going to last at $10B (60x), but at least it's healthily above the IPO price! Maybe it'll settle at $4-5B (25-30x).

Not so fast. This was in 2022. By the time the lockup expired, the stock was down 97% from the peak (and down 87% from the listing price), trading at $20 / share:

That's right: in a highly improbable scenario where I joined a seed stage company, slogged for years to get it to $30M in revenue, and then a bunch of other people work hard for several more years to scale it past $100M in revenue...

The share price grew ~67%. It turned out that I’d have been better off taking that money and putting it in the S&P 500 (SPY grew 90% from $200 to $380 in those 7 ½ years).

Here’s the whole story, including all my purchases and sales, grouped by quarter (bar height = purchase price, not quantity):

Does that mean you shouldn’t join an early stage startup?

Not at all. I’ve also seen it play out amazingly well, too. I’ve worked closely with loads of early employees in the secondary markets. There are a lot of people who made $10-200M from joining an amazing company very early, growing it and growing with it. A lucky few have even done it a few times in a row!

Not so much in this case, ha. But honestly, it ended up being fine — better than most:

• My weighted average total comp went from ~$175K → $225K → $250K over my tenure, roughly corresponding to VP Ops at seed → Head of Biz Ops at Series A → COO at Series B.

• All along, I was drawing a $100K salary, so it was only the equity that increased along the way.

• Across my original grant and the modest refreshers (kicking myself for not fighting for a COO promo grant), I got ~55k shares. In total, these grants represented $500-600K in equity comp over 4 years at the company’s preferred price when they were issued.

• This felt low to me, so I made up for it with my secondary purchase + voluntary paycut, which added another ~25k shares (at a cost of ~$100K out of pocket).

• In total, I had vested and purchased ~80k shares. I sold ~20k shares along the way in 2019 and 2021. Since then, I’ve bought & sold more at varying prices post-IPO. I currently still hold ~20k shares.

• My weighted average exit price (marking shares I still hold at today’s market value) was ~$32 / share.

• Fortunately, those seemingly reckless moves to double down on the company actually ended up being a decent decision because they were done on a very low basis ($4 → $32).

But overall, it was a very bad risk-reward equation. I gave up ~$700K in opportunity cost + out of pocket cash for a 3-4x payoff, which — when you consider the extreme improbability of a company going from early stage to IPO (5%? odds) — makes zero financial sense.

A notable benefit was the better tax treatment in this path. The majority the equity was tax-free thanks to QSBS (it can be a hugely impactful tax break for those taking startup risk). The 3-4x improves to 5-6x when that is factored in. Still makes no sense though (6x * 5% is still a 0.3x EV!).

But I still strongly believe that asymmetric bets are still the name of the game. I’ve made lots of decisions that carried limited upside and uncapped upside, and I’m really glad that I have. For example, the same risk-prone mentality has paid for itself many times over with angel bets.

(Fortunately, my wife has highly marketable skills, so we’re able to sustain a stable lifestyle off her career; marrying up is a secret weapon when you’re building startups.)

Some things aren’t just about money

I’ve now been a founder for the last 7 years since I left that company, which has amplified a lot of those tradeoffs (albeit with control over my company, and hence my destiny). The majority of that time has been no-salary. Across my 11 years in startups, my *total W-2* has been ~$600K (<$60K / year). I’m not in this to get rich quick, I'll tell you that much 😂.

Startups are like playing blackjack... so "winning" might mean a big payday, or simply having the dealer bust a face card against your 17 (i.e., a scenario where the win is expected but modest).

But if you’re playing blackjack right, you’re not doing it for the money, you’re doing it for the entertainment, for love of the game, and to have a good time with friends. The journey is the destination.

But that journey… I’ve learned so much. Grown so much. Gained so much.

I’ve felt the magic of true PMF: your customers want to rip the thing out of your hand faster than you can make it. It stressed me the fuck out. But when I don’t have it, it’s all I crave.

I’ve gained some street cred. Secondaries were a nascent, taboo industry when I was building in the space, and it’s since become sexy. I’ve lucked into some great angel investments. Sometimes, I get messages from strangers telling me that they love the work I’ve poured myself into (Forge, AbstractOps, Fair Offer, Autograph, the Weighing Machine, or my writing). It genuinely gives me warm fuzzies (my love language is words of affirmation).

I’ve gained an incredible network, joined amazing communities, worked with awesome people, formed lifelong friendships, and gotten some incredible investing dealflow.

My biggest regrets: I wish I’d learned to sell better. I wish I’d learned how to code earlier. I wish I’d focused more. I wish I’d held a higher bar for talent and culture, rather than making compromises because I felt like I had to grow headcount faster. I wish I’d raised less money at lower valuations. I wish I’d understood that 10x people are very real: they can actually flip a binary no to a yes, or produce such insane leverage that you wouldn’t think it was possible.

I’m glad I’ve taken on the occasional risk of being screwed over in order to be vulnerable and open with people and I don’t regret a thing. Because when I did get screwed over, it taught me the kind of person and the kind of founder I wanted to be. How to build the sorts of culture I wished every company had. How to be better in each year than the last.

And finally, I’ve come to understand what I care about, in order:

1. Winning (= building something big)

2. Working with brilliant people who are good humans

3. Doing right by stakeholders (employees, customers, investors)

4. Having fun along the way

This is the only way I can imagine it. This is my life. There are many like it, but this one is mine.

The year is 2004. Google has just gone public, and the revolutionary PageRank algorithm prioritizes websites based on quality of content and backlinks — leading to a massive improvement in signal to noise, and discoverability.

The year is 2012. There has been an explosion in company blogs (including startups). A massive cottage industry springs up to help companies with their SEO. Everyone tries to game the system; but Google’s regular algorithm updates penalized hacks, keeping publishers of content, and the SEO industry, on their toes. This generally proves effective; while everyone has a blog, most of them are of decent quality.

The year is 2019. Phones have now surpassed desktops in pageviews. Accordingly, mobile experience and page loading speed have become a critical aspect of SEO. Technologies are imagined and companies are formed to help businesses with load times (Vercel, Netlify). There has been a spike in slop, with clickbait, listicles, mass-production of SEO content, etc. But domain authority, load times, and backlinks still allow companies to stand out in a crowded market.

The year is 2025. Multi-modal content has become far more common thanks to the pandemic. Hiring offshore talent took content production to a whole other level… just in time for the advent of LLMs and AI-generated content. Right around this point of time, GEO / LEO (Generative / LLM Engine Optimization) emerges, to help companies “rank” in answers in the LLM chat interfaces (which are rapidly replacing search).

The state of user acquisition today

AI has taken “slop” to an art form — it used to mean that slop was low substance / low style; but now takes on the form of low substance / high style, fooling users and hence the PageRank algorithm.

Google is stuck between a rock and a hard place… even identifying LLM content has become a really hard problem (and hence, expensive… Google can’t possibly afford to analyze every page it scrapes); also: if the goal is good content (measured by user perception), who’s to say this is even a problem!

In parallel, the rise of GEO/LEO means that, instead of humans producing content and humans consuming them… LLMs are now producing and consuming the content. The best answer from Google (and SEO experts) is: “if your content is still helpful and high quality, then it will still help your domain authority.”

And finally, various digital means of finding customers has also faded:

• Community building was all the rage during the pandemic, but was overdone; there are now too many Slack communities (and a graveyard of community management startups).

• Performance marketing has gotten increasingly expensive, especially due to privacy regulations and Apple’s ATT.

• Cold email is completely dead; open rates have plummeted and response rates round to zero.

• There is now a glut of podcasts, and LinkedIn / TikTok content is gradually becoming slop, too.

Large companies have lots of ways to generate leads: their brand is a moat in this era, and they have capital to show up in force at conferences and host in-person events.

Attention is all you need…

There is always a push and pull, always a cycle: when a signal emerges, it gets gamed. This leads to massive quantity, inevitable to degraded quality, and the signal loses way to noise. We saw this above: with meta tags, then content, then loading speeds, then programmatic SEO… Each cycle brings with it a “proof of work” — a way for companies and content creators to stand out from the pack. A way to produce quality content that is not just a lead magnet, but an enduring brand artifact.

What edge does a high-quality startup still have today? In other words, what breeds quality, but is weakly or inversely correlated to scale?

1. Hard-won expertise

2. Taste (hopefully)

3. Speed

Fortunately — and just in the nick of time — there is now a way for a startup to package all of this together, in a way that forms a new proof of work.

The rise of vibe coding — quickly building expressive mini-apps using AI-assisted development and intuition — has lowered the cost of building software to nearly zero.

In 2025, someone with negligible coding skills but with deep, hard-won insight and strong product instincts can build powerful mini-apps on their own… apps that would have taken an engineer weeks or months to build even a couple of years ago.

What does that mean for the market?

On the one hand, we will no doubt be a massive wave of “fast fashion” apps, just as we saw in the wake of the iOS App Store in 2008. The indie hacker community is ready and waiting, and we’re seeing new products spin up left and right.

On the other hand, the App Store also saw the birth of massive companies like Uber, Snapchat, TikTok — all of which thrived in a mobile first environment.

While vibe coding is not (yet) at a point where it can build true enterprise grade software, it is absolutely perfect for offering true value to users, gaining goodwill, and building credibility by demonstrating expertise in the market. (These are exactly the things SEO was great at in the early days, before it got sloppified!)

This means that the first movers are in prime position to demonstrate their authority and taste to build a suite of (free, publicly available) tools to create brand new top-of-funnel.

Vibe Magnets: Software is the New Content

SEO has become synonymous with blogs and text, but there’s no actual reason this needs to be the case. Here’s how lead magnets measure up on some of the critical criteria for domain authority (disclaimer: heavy input from my friend Chad Gippity below):

Tools go toe to toe with text & AV content on every dimension except effort. With the cost of software going to zero, we should expect this to flip (if it hasn’t already).

We’ve been leaning into this, with Autograph Tools

We started in…

• April: a rebuild of the Fair Offer, 5M+ offer combinations via an algorithmic approach — for any title, any stage, anywhere in the world

• May: the Culture Compass, a Myers-Briggs-style personality test, but for companies

• July: the Weighing Machine, a back-of the envelope DCF to quick (and very accurately) estimate a company’s fair enterprise value

• August: the Org Designer, giving finance execs / founders the ability to quickly design their dream organization in 15-30 minutes, without mucking around in spreadsheets

It’s early days, but it certainly appears that a) people find them useful and b) they endure in utility over time. Despite no attention to keyword management…

Fair Offer gets thousands of visitors a month.

The Weighing Machine gets hundreds of visitors and thousands of uses per month over the last 2 months.

If you want to play this game on hard mode, there’s no reason a vibe magnet needs to be an “applet.”

We recently did this with The Org Designer. It lets a founder, CFO, or department leader model out their ideal organization in ~15 minutes.

A starter kit for building vibe magnets

Building an entire app like the Org Designer — not an applet like the Fair Offer or the Weighing Machine — is more involved, has security considerations, and is out of the scope of this post.

But if you’re building “applets” (i.e., a public-facing tool with no login or persistent user data), you only need three things:

a) An app builder: Vercel v0, Replit, Lovable, Bolt, etc. all have pros and cons, and “personality.” Replit is more “full-stack” (comes with built-in servers and databases), Lovable does landing pages and simple apps really well, Bolt is good at mobile-native support as well… but — for this use case — especially given my choice of “database” below — I find Vercel’s v0 the easiest to work with.

I found that it is better than the others at both a) spinning up the first version really quickly, and b) being quicker to make fine-tuning edits to get what I want.

b) A “database”: This is barely a database; it only needs to do two things: a) record entries for what people type in (to understand how people use it), and b) collect leads / emails.

This is why I recommend Airtable: it’s not as “robust,” but it’s extremely user friendly and easy to connect to other systems (e.g., you can easily set up an automation to pipe notifications into your Slack instance, or use the Zapier integration to push leads to your CRM). I think this is a better choice than a more robust DB like Supabase or Neon; they are overkill for “applets,” and non-technical builders will find them more complicated to work with.

v0 and Airtable don’t integrate out of the box, but it turns out it’s super easy.

1. Spin up your app in v0

2. Configure the basic tables where you want to store a) the output of your users’ entries and b) their contact info from the lead form

3. Ask v0 to connect to the Airtable API, and provide your Personal Access Token in the chat; v0 will store it in your environment variable and you’ll be all set!

c) Hosting: Most app building platforms handle this for you; but you’ll still need to have it show up correctly on your website. The simplest way to do this is to set up with a subdomain (e.g., widget.mywebsite.com) but arguably this is worse for your SEO than using a subfolder / page (e.g., mywebsite.com/widget). This is a bit gnarly but very doable with a little help from an LLM.

How to run the vibe magnet playbook

Here are a few things we’ve learned in our 6 months building these:

1. Leverage your expertise and hard-won insights. We tried very hard to build genuinely useful things — things meeting the bar that other companies charge for, but which we were able to build quickly because we just understand the space that damned well.

2. Building something is easy. Building something good is hard. If you put out software slop, it’s not really much better than putting out SEO slop. Spend the time to build something incredible. If it’s successful, this will be the first impression that many prospects will have of your brand.

3. 90%+ of carpentry is sanding. If you’re doing it right, the quick-and-dirty version might only take an hour… but the polish and care required for a really great resource takes 40-50 hours, and a really good app takes 100+ hours.

4. Just because it's free to your customers doesn't mean it's free for you — especially in human capital. Quality products take time and care to build, and maintain. Ensure that you can spare the bandwidth.

5. Charging for it might be more trouble than it’s worth. We had a serious debate over whether to charge for the Org Designer — we’re really proud of it. But if we charged for it, we’d have to provide proper support, keep improving it, get on sales calls… so we decided to price all of them at $0.

6. Be careful about accepting sensitive user data. In order to limit distractions to the engineering team, we involved them only minimally when building our magnets. We took security precautions, but even so we were very cautious to avoid collecting sensitive information from users. E.g., the Org Designer doesn’t ask for employee info or personally identifiable info; just high level information about number of roles.

7. Build for your persona, not for your product. Just like with content, don’t try to make things that are only directly relevant to your product. It’s okay to build adjacent things, as long as they still speak to your ICP.

8. Your magnets should ideally make each other better. Ideally, the IP from one product can be woven into another. For example, we’re going to reuse the Weighing Machine algorithm to create a fundraising strategy guide for CFOs & Founders. We’re going to combine Fair Offer and the QSBS Calculator into a net new employee resource.

9. Distribute it, constantly. We regularly share the tools where it’s organic and relevant… on X, on LinkedIn, and woven into in-person conversations with customers or prospects.

10. Capitalize on traffic. When you get high intent traffic, gather their lead forms and guide prospects to your product effectively. (Note: we’re really bad at this at Autograph…)

Final Notes

We might see the invention of a new job function. Just as “SEO Specialist” and “Growth Engineering” became job functions over the last 20 years, it’s likely that a new role will emerge to fill this role: someone with a terrific sense of product taste, quick at iteration, and effective at automation (to convert leads). That said — I think it’s likely that the best of these products will be built by founders. The farther it gets from the center of gravity for the company, the sloppier it will feel. Founders closest to the problem, and tend to be particularly good at taste and speed.

This too will turn to slop. Just as every wave in the past, we’ll see this gamed, too. But we’ll probably have a few years before everyone gets around to it.

Shoutouts to the trailblazers who did it before it was cool: Well before the days of vibe coding, there were lead magnets. The first ones to do this well systematically were LTSE Tools (sadly, their tools are no longer around). Many companies have done this in one-off ways that are powerful (Hubspot’s Website Grader, Canva’s Resume Builder, Grammarly’s Grammer Checker), or clever (Zapier’s “connect X to Y” programmatic SEO that blurred content and tooling). But when it’s not just a basic tool but something that provides genuine, sometimes unbelievable value, it can have an outsized impact. If a lead magnet is simply a free resource given to potential customers without signing up, the most effective lead magnet of all time is also the most successful product of all time: ChatGPT. So… yeah. One of these could be so effective that it drives your whole company forward. And the more relevant it is to your core audience, the closer it is to your actual product, the more impactful it can be.

It’s not easy to build a vibe magnet. But it’s easier than it’s ever been. It’s gone from “hard and expensive” to “very accessible with some time and elbow grease” — suddenly, in the last six months. That’s a massive unlock. Now, these can be built by someone (even someone who can’t code) in a few days. We should be excited to see what the most clever, scrappy companies produce in the months and years to come.

It’s so cool to live in the future. Just like any other AI-powered activity that has emerged in the last couple of years, it’s worth reminding ourselves: this is the worst it will ever be.

All successful startups are alike; each failed startup is a failure in its own way
– Tolstoy if he lived in 2023, probably

AbstractOps is the company I founded in 2019, and this is a retelling of the ups and downs over the next 5 years.

This was one of the more frustrating and painful posts I’ve written lately, so I apologize that it doesn’t have the quirkiness or joy that I usually aim for. I needed to share it anyway. I hope it helps others, but more importantly I want the story to be told.

This is not a post-mortem; AbstractOps is still alive and doing well, and will even be profitable shortly. Rather, it’s a retrospective — of what was, and what could have been.

The things that didn’t work are roughly grouped into: product decisions, go-to-market decisions, and company decisions. But first…

The rise

AbstractOps was an incredibly cool premise, and one that we had a real shot at — if many more things had gone our way.

We were building the OS for the back-office. Every founder desperately wants this. It’s just a really great problem. If we’d solved it, it could have changed how early stage companies are started and built. The promise still gives me goosebumps, sometimes.

The early days — especially fundraising — were hard, because I was a solo, non-technical founder, building hybrid product + services (when this was still uncool). Plus the company I’d previously helped build (Forge) wasn’t well-known yet, and I was founding team (not founder) there so I didn’t have the same street cred.

But after some early customer traction, a clearer pitch, and some no-code prototyping, I managed to raise ~$600K at $5.5M postmoney. I brought on a couple of cofounders, Adam (still one of my closest friends and a wonderful person), BK (ex-Superhuman, to lead engineering). COVID hit and we went fully-remote; we added several talented founding team members over the course of the year — Charles, Loubna, Anthony. Based on a combination of promising product prototypes and Adam’s great sales skills, we grew to nearly $100K in ARR in a couple of months, catalyzing a seed round (~$3M at $16M postmoney) led by David Sacks and Lainy Painter at Craft.

By this point the software vision had come nicely into focus — the business & operating graph of your company, powered by an (actually very powerful) internal no-code engine. This enabled a wide variety of workflows:

By popular demand, the AbstractOps offering was a hybrid product / services offering. The product had broad (as we found out later, too broad) capability, but the hands-on operator would fill in the gaps, and be a founder’s consiglieri (with the goal of becoming an "account manager" or CX over time, as the product automated more of the manual work).

This meant we could charge services revenue, not just SaaS. Our monthly fee per customer went from $200 to $400 to $800 to $1,500 to $2,000+… with no real impact on demand. And customers really liked working with us. We helped companies scale, fundraise, hire, fire, pay, comply, and everything in between. A client once — unprompted — recorded a video of their whole team thanking and cheering their point of contact (and AbstractOps) for playing a critical role in enabling their fundraise. I cried with joy when I saw that video.

We even raised a small angel fund for Adam and I to invest in AbstractOps clients — because why not?! (or so it seemed at the time. Our customers loved us so much we got into several oversubscribed rounds because we were a strategic ally.

So we rapidly scaled 5x to $500K in ARR by mid-2021 and brought on some awesome execs (Pooja; and later Amar, Ray, and Rebecca). This led to us raising a “Pre-A” as we called it in that brief, crazy time: ~$4M at $60M postmoney. Overall, we had an insane cap table — while fundraising isn’t indicative of success, I’m pretty darn proud of our all-star lineup:

The fall

That fundraising announcement was in early 2022. We kept growing — peaking at ~$1.5M in ARR in March 2022, and we raised $1M at $100M postmoney. Gosh, this was absolutely absurd in retrospect; I felt embarrassed enough about this in the months that followed, that I went back to the investors to proactively recap it back down to the previous $60M valuation. My friend John (and angel investor in AbstractOps) recently called this sort of thing a suicide round. There were all together too many of these rounds in the crazy days between early 2021 and mid 2022.

When the market correction happened in Q2 2022, it exposed the cracks in the foundation — in AbstractOps and every other company. In our case, these were:

• our ARR had ~35% in contribution margins, but closer to 20% in gross margin, which simply cannot work for a scalable business

• this meant that while our revenue multiple was high, our gross profit multiple was wild (I’ve since come to realize this is the most correct way to value a company); if ~70x revenue seems bad, 300x+ gross profit is worse. This was comparable to a software business that had raised at a 250x revenue multiple.

• our culture was a constant challenge. We made the mistake of setting the expectation of rapid promotion and a consensus-based decision structure which resulted in frustration and gridlock far more than was necessary. It was easier to resolve these when everything was working, but when the rocketship sputtered, both the expectation of upward mobility and the desire for consensus made things a lot harder.

• attrition was a huge issue, and the attention to hire / train / manage / fire people occupied more than 50% of the founders' day-to-day bandwidth

• my cofounder and I each had valid but divergent visions; I was more product-motivated (even if it delayed growth), and he was more revenue-motivated (even if it was achieved through services)

• the product had simply not delivered on the promise yet, in part because it tried to do too many things

• the things the product did well, people didn’t want to pay for (e.g., contract lifecycle management, recordkeeping); and the things people wanted to pay for, we did a mediocre job of (e.g., hiring workflows, payments, state compliance)

• fundraising was no longer a viable strategy to “make it work”

In hindsight all of these things are crystal clear, but in the day-to-day none of them are obvious. Each individual decision made sense at the time, but in totality painted us into a cornered situation where the company simply couldn’t work.

Around late June, it was clear to me that we had to make a set of drastic changes. In the 12 months after July 2022, the company saw 2 cofounder breakups, 3 rounds of layoffs (from 38 to ~5), a hard pivot and a couple of soft ones, a company spin out, a ~90% drop in revenue... and finally a soft recovery.

At this point in the fall of 2023, it was abundantly clear that this would be a healthy “lifestyle” business but not a venture scalable one, requiring a CEO and leadership whose strength was in tactical ops and demand generation; not 0-to-1 product, which is my passion. So, I decided to step down as CEO, hand over the reins to Kristin (AbstractOps’ current CEO, who had risen rapidly through the ranks), moved into a supportive chairman role, and terminated my vesting.

Today, AbstractOps provides state compliance automation for companies operating in multiple states. It’s a healthy, growing business with 200+ customers, strong NPS (80+!), and nearly breakeven. It will continue on as a cashflowing, dividend business (or eventually, a strategic acquisition for a payroll company).

This is by no means a “failure” but… I certainly failed to lead the company to the promised outcome (a venture-scale business with a big exit). There were thousands of small decisions that led to this, but a couple of dozen critical things that didn’t break our way, about which I still wonder “what if”.

Product decisions

Tech-enabled services are very very hard to build in a venture-scalable model. I wrote a whole other post just on this topic. It’s one that’s resonated with a lot of people but the main factors are: the people overhead really impedes the company’s ability to scale effectively; revenue is higher than software but customer expectations are sky-high too (and hard to execute on reliably); cultural factors dominate (and culture is hard and slow to build well); and gross margins are very hard to get up to venture-expectations (60%+ at an absolute minimum, ideally closer to 70-80%).

I was a novice product leader. While I’d worked at a fintech startup before, it was much “fin” than “tech,” and the lack of product intuition at the time resulted in a bunch of rookie mistakes. The CEO has to be the product visionary, but my instincts hadn’t yet caught up with the needs of a true software product CEO.

1. We tried to do too many things. I remember one internal memo had the following line: “we’re building lightweight Rippling + lightweight Bill.com + lightweight Ironclad.” At the time, it seemed simply ambitious (it was 2021, everyone thought they were going to take over the world!), but given the nature of product development at the time, it was simply absurd.

2. I didn’t understand that the [cost to build] vs. [the cost to build something delightful and maintain it] was roughly 1:5. I really didn’t internalize that >>50% of the engineering cost (in resourcing, specifically) — maybe even 80% — is after the v1 is shipped.

3. We were too early, and to paraphrase Howard Marks, too early is the same as being wrong. Our clever document vault concept? We spent months of engineering time building document AI in 2020-21, to get the classification to >80% accuracy on >80% of documents. We were so proud! Today, that’s a day’s work with an OpenAI API call. And the sort of surface area we were signing up for was impossible with the engineering capacity we had at the time; today, I think it’s possible with just a few 10x engineers leveraging Claude Code.

Subject matter expertise is double-edged — too often, we built based on knowledge not users; so we built things that were often technically correct, but didn’t map to our users’ understanding or mental models. Unless you’re building a tool for power users (we were doing the opposite), the product has to guide you through what needs to be done. E.g., we had 50+ types of documents a user could send for signature. We should have picked the top 5 and left it at that.

GTM decisions

Defining a new category is insanely hard, even if it sounds exciting. You never, ever want to have to educate users on the value of your product. The pain points were obvious to us, but we couldn’t describe it in 5 words or less, and the need for good process / automation is not easy or pressing for our customers — which were founders (who, correctly, only cared about PMF). The only aspect they did know and care about was operations capacity / know-how to simply “deal with it”; but capacity and knowledge require a person, not software, to solve (until AI, anyway).

General purpose tools (Airtable, Zapier, etc.) are hard to do well. Every single such platform that succeeded, did so with specific (1-3) use cases. We didn’t know what this was, with clarity; by the time we arrived at the right ones (state compliance, payments), it was too late — the ACVs weren’t big enough, and we didn’t have enough time or money to reorient the product around a supersized version.

We picked the wrong ICP — we should have built for mid-market / enterprise, not early stage startups. The main reason is very simple: ACV, and as a result sales efficiency. Thiel’s SaaS “dead zone” is spot on; it’s hard to scale products with an annual contract value between $500 - 5,000. Early-stage companies of <10 simply do not pay >$5,000 / year for software. The only exceptions:

• computing (cloud, and now AI)

• professional services

This makes it quite obvious why we were pulled into services.

The only alternative is if you onboard an early stage company to the product early, because it clearly scales to mid-market. But since we had such a wide surface area, our product lines weren’t robust enough that we could scale with a company to 50-100+ people.

Legal products are hard to sell. Non-lawyers don't really want to think about or touch it. Lawyers are very risk-averse to adopting your products.

Company decisions

As a company were obsessed with process — possibly because of the operational DNA. One of our execs joked that we had more SOPs than the number of times we'd run the SOPs. In retrospect, we shouldn't even have written something down until we had already done it at least 10 times.

We talked way too much about culture. We spent more time talking about values than reinforcing them through behavior. Our focus should have been on scalable product-market fit because without that, the company would die.

A company that dwells on work-life balance is not going to make it. This was an unfortunate and common theme in 2020-21, due to the chaos and stress of the pandemic, followed by the talent wars. To be clear: burnout is real, and people do need downtime in order to recover and recharge. But making a big deal about work-life balance was a disaster. My cofounder and I worked many weekends, but we literally hid it from the team with scheduled Slack messages, to “set a good example” on work-life balance. In retrospect, what the fuck. We should have sought people who wanted to work crazy hard on important problems, and who were thirsty, even desperate to get to product-market fit.

Constraints breed creativity. We should have raised a lot less money at much more conservative valuations. This is probably the only thing I will give myself a bit of a pass-on: everyone lost their minds in 2021, due to low interest rates, historic growth rates, and easy money.

The “cost” of capital is not just dilution, but also increased expectations. Once we’d raised >$5M, there is an implicit expectation to scale revenue. This isn’t something that was imposed by our VCs, to be clear; it’s just a market level expectation to seem like a startup that is “killing it” vs. one that’s treading water. If we hadn’t raised that capital, we could have stayed in “exploration” mode. Which means…

We scaled something that wasn’t ready for scalable product-market fit. The services had PMF but weren't scalable. The product was scalable, but didn't have PMF. We thought we'd figure out the combination and repeatability "later" — this was obviously a mistake. Once you take on customers, you have an obligation to keep serving them and exercise duty of care. We never let anyone down; but if we had had fewer customers, the transitions and pivots would've been a hell of a lot easier.

Price's Law was very real for us (50% of work is done by sqrt(team)). When we went from ~38 to ~12 after 2 layoffs, our aggregate (not per capita) productivity was higher than it was before the cuts. This was due to stripping out consensus-based decisions, and eliminating make-work.

But today, AbstractOps serves hundreds of customers with an extremely efficient and lean team and an incredibly high NPS. Whatever the decisions were, getting to that point is not something I take for granted.

Conclusion

Gosh. Just… so many things that could have been done differently. I suppose all we can do is not to make the same mistakes again. After all, there are so many new mistakes to make and learn from!

When I stepped down from AbstractOps, I started a new company. I’m trying to take every lesson to heart. Here’s my cheat sheet:

• Stay away from services (except implementation)

• Stick to existing categories or well-understood pain points if at all possible

• Pick specific use cases and jobs to be done, not a generic / horizontal platform

• Pick a ICP (segment/buyer) that has the ability and willingness to pay >>$10K ACV

• Seldom bring up “culture” unless there is a clear deviation from desired behavior

• Deprioritize work-life balance; work hard, and recruit people who take pride in doing the same

• Raise as little money as possible, at incredibly conservative valuations

• Stay scrappy and very lean (one XL pizza team)

I wonder if Lina Khan knows this story.

This last weekend, 200+ employees at Windsurf were left stranded, when Google hired away some of the Windsurf team. The founders and a number of the key researchers / engineers (a ~tenth of the ~300 person team) were hired into Google.

This left the remaining 250+ people caught between a rock and a hard place: they did not participate in the exit, they were gutted of key leadership and engineering talent in an intensely competitive environment, and would soon start bleeding revenue. They couldn’t even shut down the company; it would strengthen a potential FTC case that Google effectively acquired the company. Fortunately, Cognition came to the rescue and purchased Windsurf — paying out the employees and hiring them into new roles.

But wait: why didn’t Google just buy Windsurf outright?

This was a “blitzhire” — a transaction where the purchaser surgically removes core talent, leaving the corporate shell and the majority of its employees behind. This has happened often, recently:

• Inflection AI by Microsoft

• Character.ai by Google

• Scale AI by Meta

• Adept by Amazon

This phenomenon can be traced directly back to FTC (and EU commission) antitrust policy, and accelerated by the FTC “shadow veto” instituted by Lina Khan (The Wrath at Khan).

Here’s why:

• Historically, FTC and EU review usually took a year. This limbo kept both the acquirer and target company from being able to operate and advance with any sort of clarity. This is a non-starter with AI acquisitions; a year might as well be a decade.

• This got a lot worse with the Lina Khan regime: aggressive merger challenges and threat letters killed most deals before they even started, specifically for big tech (Google, Microsoft, Meta, Amazon, Apple, etc.).

In an already-uncertain business environment (a pandemic, followed by interest rates hikes), this had a chilling effect on M&A.

On the surface, this seems aligned with the FTC’s mandate: these companies all have a market cap of more than a trillion dollars. They shouldn’t be allowed to get any bigger!

But that’s a misunderstanding. The primary goal of antitrust is improving consumer welfare by promoting competition.

In a hypergrowth environment like tech, the best source of competition is new entrants. And in Silicon Valley, new entrants are fueled by exits. Startups are founded and funded by ex-founders, ex-early employees, and angel investors.

Which brings us to…

Second-order effects

Big tech is the buyer of last resort for a lot of tech startups. In a tough IPO environment, this limitation on exits stifles startup liquidity, and hence angel capital to reinvest back into the ecosystem.

Nobody wants a hard landing (shutting down the company); and without a soft landing, companies to just limp on, as zombies.

Fewer M&A exits means less founder / employee freedom, and liquidity across the board. Less freedom and liquidity means fewer startups. Fewer startups means less competition.

Third-order effects

Given the choice between a year-long review to buy the revenue + product + team, versus acquiring just the team overnight... a big tech buyer would be silly not to pick the latter option. And so, they figured out a workaround: hire away key members of the team, for massive sums ($500M - $15B).

Fourth-order effects

These blitzhire shenanigans have resulted in a major breach of the “social contract.”

Founders are the linchpin of the trust ecosystem. They stake their reputation on their startup, signing up for implicit moral obligations to every stakeholder (employee, investor, customer, partner…). When this contract is voided, the delicate balance unravels.

Why would any employee sign up for a slog — below-market offer, long hours, foregone vacations — if the founders can just go get blitzhired, instead of working shoulder-to-shoulder in the trenches?

The founders took the easy way out here, because the money was probably just too good; I estimated that the founders each made $400-700M (between the acquisition price and a new retention grant from Google).

Some toxic founders I know once said “we’re majority shareholders, so what’s in our best interest is in the shareholders’ best interest.”

I wonder if the founders of Windsurf thought that too.

Every time another founder makes this compromise, it dismantles the startup ecosystem a bit further. If you take away the village — not just the founders, but everyone around them that make it happen — then the precious magic of Silicon Valley dissipates, which clears the way for big tech to cement an unassailable oligopoly. And just like that… the FTC has accomplished the opposite of its mandate, just like the British Raj’s cobra control.

Our instinct is to condemn the founders, but they're just playing rationally (if selfishly) by the rules of a broken game.

The best founders will honor the social contract... but if even 10% breach it (and they will — $500M is a lot of money), it poisons the well.

To be clear, it's both: we need to expect better of founders, and hold them to it. But also... we need to expect better of our government, and hold them to it. We need a better, clearer, and faster regulatory regime that takes weeks, not years — one that doesn't make the "blitzhire" the most logical path to an exit.

So my question for Andrew Ferguson: you recently reaffirmed Lina Khan's legacy. Look at the wreckage, the gutted companies, and the stranded employees. Is this what protecting the market, what championing the American Dream looks like?

We need to update both our definition and mental models.

Why does this matter?

Slop is high-fructose corn syrup: addictive, filling, and nutritionally empty. It’s tasty in the moment, but is it worth obesity and an eviscerated microbiome?

Lack of care and lack of substance degrade society the same way.

Corporations prioritizing shareholder value. American consumer products offshoring production and losing brand value. Mainstream media devolving into clickbait & bias, losing public trust. Politicians prioritizing special interests / lobbyists post-Citizens United. These are all cases where major institutions lost broad societal trust because they prioritized the short-term, low=nutrition, high-calorie hit.

For each individual player, their actions were rational. But that’s prisoner’s dilemma for you: “defecting” — choosing the short-term, local maxima — is the rational choice, but leaves everyone worse off and sacrifices the global maxima.

I’m all for rational, self-interested, high-agency choices. “You can just do things” is very empowering! But… just because you can just do things, doesn’t mean you should. Society is better off when actors make thoughtful holistic decisions that create win-win outcomes: when players are “long-term greedy,” solving for multi-turn games.

It’s Silicon Valley’s turn. We’ve already come dangerously close to losing societal trust due to thoughtless, unintended consequences: tech’s impact on labor markets, social media addiction, Cambridge Analytica. We will start recovering some of it with things like a refocus on American Dynamism, financial freedom, etc. But the real test is going to kick in based on how we handle the arrival of AI, and in big part: what do we do about slop?

Slop is accelerating across domains, thanks to AI

“Thought leaders” can now pose ostensibly sophisticated ideas which are actually ridiculously stupid, and distribute at scale by ruthlessly targeting receptive people across a massive audience.

• This first wave started a decade ago with social media algorithms and SEO, but it really started accelerating a year or two ago thanks to LLMs.

• This has now been brought down to a science by motivated — sometimes malicious — actors.

• At worst, LLMs can also hallucinate apparently interesting things which aren’t real at all. At best, they’re empty calories, with no substance to the content.

Content creation has become sloppified over the last 20 years: step by step, it’s become possible to create ostensibly polished content in just a few minutes

• Algorithmic juicing of viral posts and videos with Facebook, Vine, and Instagram

• The TikTokification of video content, which further shortened attention spans

• The advent of editing tools (especially with AI) the last 2 years — polishing a turd has become an order of magnitude easier

• The latest image and video models (e.g., Veo3) make it easy to blurt out a few words and make something that would have taken a 10-person team a week, even a few years ago

There has been a massive spike in ostensibly high-growth companies with horrendous cracks in the foundation:

1. With the rise in ability to build software really quickly and acquire customers (on both a prosumer subscription and enterprise openness to AI) rapidly, we’ve seen hypergrowth like never before

2. But many of the products have no moat, really bad churn masked by even faster growth (for now), and — worst of all — sketchy metrics.

3. We see some variation of this during every big hype cycle (sharing economy in 2015-18, crypto in 2020-22), but the rate today thanks to AI today is unmatched.

Let’s look at examples of these in politics, AI content, and startup hype.

Political slop = lopsided [style : substance] ratio

You’ve probably seen these videos:

Zohran's policies are incredibly problematic — rent freezes will result in degrading housing stock, city-run grocery stores are a pipe dream (“they won’t have to pay rent?!”) which will probably hurt local small businesses (the main source of groceries in NYC is bodegas, not Walmart).

But his brand has terrific cinematography. Incredible, tasteful production value, and “rizz.” I actually respect that.

Hyper progressive administrations have failed catastrophically in San Francisco and elsewhere. But Zohran’s ideas sure sound nice, and he’s saying them in a softly lit video with a charismatic, empathetic voice. And he just won the democratic primary.

AI slop = power wielded without thoughtfulness or duty of care

I talked to a founder last year who was building a “purely AI TikTok — we won’t be bottlenecked by human creators, so we can create 100x as much content.”

I said, “oh that makes sense, so much of TikTok is algorithmic anyway. I have one request: can you at least make it higher-nutrition rather than the slop on there now?”

He replied, “actually we’re going to focus on the lowest-nutrition stuff because that tends to be easier to produce and is more likely to go viral anyway.” I’ve never encountered a clearer origin story for AI slop.

All technology — from the wheel to the steam engine to the internet to AI — is simply a source of leverage. Its good or bad effects stem mostly from the extent of care exercised by the wielder.

The internet gave people a 10-100x point of leverage. AI is a 10-100x multiplier on top of that. So someone who’s very good at AI, and very good at the internet, is possibly 10,000x as effective as someone who’s good at neither. And when that happens… AI-aided craftsmanship, when paired with taste and care, is a sight to behold.

But just like a 10,000-lb elephant can accidentally squish someone, a human with 100x+ leverage can cause incredible harm. None of this is to say “don’t reach for the holy grail” or gatekeep in any fashion. It’s merely to say the present moment should instill in us — both the builders and the witnesses — an indelible duty of care.

Startup slop = the potent combination of content slop, AI slop, and hype slop

Warp thought they were being clever by giving out sponsor badges on X to affiliates. Turns out one of them was racist (like, really bad).

Series is apparently using their VC capital to rent out a mansion in the Hamptons, hiring “interns” who’ll fly in on first class flights, to participate in an intern reality show, “get lit on weekends”. Their CMO (who’s still in college, of course), promises “i'll bunk bed with my fav applicant.” What could possibly go wrong.

Molly O’Shea recorded a great interview with Roy Lee. His worldview roughly boils down into “you have to do outrageous things to get millions of views,” and “virality is the most important thing in the world” and “if you can do it consistently, you’re untouchable.” (Emphasis mine.)

And then he goes on add this bit:

If I make it, it will be, like, the death of professionalism.

I don’t actually dislike Roy. He seems incredibly sharp and talented, and I want smart founders to win. But things like this, encouraging other founders that professionalism (aka being civilized in the workplace) doesn’t matter, is simply a flippant way of saying “I’m burning down the scaffolding that holds up trust in the ecosystem.” Saying “cheat on everything” sends the wrong message: that founders don’t (and shouldn’t) care about ethics. This worries me. With the immense power of AI just being realized, thoughtfulness and integrity is more important, not less.

Besides, I’m not sure the impulsiveness is in Cluely’s best interest; building a durable company requires forethought and duty of care. If he’s supposed to be like Zuck, then I hope his allies become his Thiel and Sandberg — challenging (not indulging) impulsiveness, and encouraging thoughtful decisions with long-term implications.

What is “good” distribution in the age of slop?

“If a tree falls in the forest and no one hears it, did it even fall?” It may as well not have. Distribution is an essential important force multiplier, and I do think many founders (myself included) underestimate its importance.

The old ways of doing it no longer work:

1. The pace that the world is operating now is no longer conducive to 1:1. We’ve got to go many : 1.

2. There is too much noise, and things like cold email no longer work.

3. There’s now a lot of decent software out there. This is good! The bar is higher! But it makes it harder than ever to differentiate on product.

So what’s left?

I’m by no means an expert. I have all of 7k followers on X. My dog has more followers than me on Instagram. I’ve never downloaded spyware TikTok.

But I do know a good bit about startups and this is my best guess.

Build hard things

Making hard things, in a thoughtful way is harder than ever, in an era with TikTok attention spans keep people from truly finishing and polishing their work.

1. In a startup, this means building hard technical things that can’t be Sherlocked in a couple of weeks by a motivated team

2. In Mr. Beast’s world, it means orchestrating even larger, more ambitious productions

3. In investing, this is about avoiding the herd and finding the diamonds in the rough

These are not a way to get rich quick, or succeed quick. They’re a way to get rich slow.

There is a different bar for stuff you build vs. stuff you ship

It’s amazing that there’s now a lower barrier to entry with building stuff. I’m literally exhilarated by it. I believe people should build lots of stuff, especially to learn and experiment.

But if you ship everything you build / write / create you’re probably shipping slop. Photographers take 20 photos for every one they use.

So even if you make lots of garbage, don’t ship garbage to consumers / users / customers. (I’ve been responsible for shipping a lot of shit in the past, and I’m constantly trying to do better.)

Build with cleverness and authenticity

Clever ways of building and growing things is awesome. Viral and memeable initiatives are super powerful. Brex mailing their early customers a bottle of champagne was a spectacle, but thoughtful. Ramp’s (and let’s not forget Wendy’s) social media accounts are full of personality.

Build with authenticity. If you’re shipping lighthearted / playful stuff, that’s not meant to be held to a high bar, that’s fine! Intent matters.

My favorite example of this is Pieter Levels (@levelsio). He’s an incredibly prolific builder. He moves on from the stuff that doesn’t work, and makes the stuff that works really good. And it’s very obvious how authentic he is.

And I want to shoehorn in Sarah’s post since it’s so eloquent:

If you want people to buy your stuff, trying being useful

Cold email just doesn't work anymore. Funnily enough, AI-slop-outreach was the final nail in the coffin. Maybe it should have never worked, but it’s certainly dead now.

But people will always respond to generosity, utility, authenticity, and social proof. I’m trying to exercise this by a) building or producing useful / fun things every week, b) investing / giving back to the ecosystem, and c) being an active, contributing member of relevant communities. If this later ends up being effective “business development,” great — but if not, these sorts of activities feed my soul regardless.

Try to create some positivity along the way

I talked to a founder last week, who’s also taking a viral marketing approach. It’s rooted in positivity, not ragebait: they interview immigrants, HONY style. 75k+ followers, and building a business on that foundation that’s already growing really fast. And most importantly: it was clear how ambitious yet humble, viral yet thoughtful their approach was.

I’m not saying everything has to be warm and fuzzy. But there should be a point to virality beyond just eyeballs.

Your reputation is all you have

As AI increasingly takes over more capability, I think humans will retain a monopoly (for a while) over original thinking, kindness, thoughtfulness, and positive-sum games.

Do this over and over, and you establish the kind of brand that people want to surround themselves with.

I think slop in all forms — degrading quality, misinformation, poor signal to noise, bot armies — is the greatest threat to our cognitive health in the coming decade. I hope we can resist the low-calorie, short term hit… and fight to make things that are hard, thoughtful, tasteful, and authentic.

But all that said it’s a fun, wacky read, and I found it independently useful in understanding broad themes in how power shifts and collectives of people (countries, cultures) tend to behave. A lot of this might be elementary for people much better educated on the topic — but I found it interesting and thought others might too. Complex systems are hard, but fun to think about.

Here are a couple dozen things I found interesting from the book.

About the United States

Power swings between elites and populace every ~50 years in the US

This appears to be a recurring theme since the US became a country.

Founding Fathers (elites)

→ Power shifting to pioneers / farmers around ~1820 (Andrew Jackson elected, 1828)

→ Power shifting to wealthier class with gold Standard (Ulysses Grant elected, 1876)

→ Power back to the people post-Depression, with the New Deal (FDR elected, 1932)

→ Power back to capitalists and supply-side economics (Ronald Reagan elected, 1980)

→ Power back to populists, right on schedule; it’s been rising / brewing for ~10 years (as no doubt the prior shifts brewed for years prior to the catalyst presidential term). We’ll see whether the current Trump administration (elected 2024) is the culmination, or one soon after.

This (and the Strauss-Howe generational theory) reminds me of Isaac Asimov’s Foundation and psychometrics and — even if it only holds in the broad strokes painted above — it’s very spooky and cool.

One thing I haven’t yet been able to explain: why should the 50 year cycle also result in populism across the world? Is it simply due to globalization, that it doesn’t just result in correlation amongst just economies, but also emergent correlations in culture (in broad strokes, this is true) and politics?

Leaders have limited power; they are only vessels for the zeitgeist

Leaders are subject to their environment. Politicians are rarely free agents. While we tend to assume leaders of a country have an enormous amount of power. But most leaders — for good and bad — cannot cause massive macro events. They can only tap into into a latent sentiment within the population. No single person is a needle mover in understanding how the entire world apparatus behaves — though of course a charismatic or strong-willed leader can serve a catalyst, ushering in the looming change (or possibly accelerating it by a few years).

And so no single administration changes the direction of culture and geopolitics. FDR was a visionary leader, but the New Deal was predictable. This explains why Clinton continued Reagan’s policies around deregulation, or why Obama and Trump continued the Bush administration’s hawkish presence in the Middle-East.

(There are probably some exceptions; perhaps the Cuban Missile Crisis could have gone very wrong if Kennedy or Khrushchev had put a foot out of line? We’ll never know.)

The United States is apparently a child, but this is a very good thing

I just found this amusing.

He describes the US as an adolescent country — simultaneously overconfident and insecure. Sounds about right. The US is simply “new” to its power (<250 years is a young country in historical terms!), and hasn’t learned how to use it yet; it’s clumsy, like an awkward teenager. The Vietnam war and war in the the ME were hugely consequential and devastating locally; but a massive power learning its own strength is sort of like an elephant accidentally squishing a human being by sitting down without looking.

The military might is economic might, the navy is critical to power

Most of us don't travel by boat a lot. So it's really easy to forget the extent to which global trade operates almost entirely based on shipping. Being able to control sea routes and trade lanes is critical to ensuring a good economic order and world order, and the only way to do this is by having serious naval might, with which to police those routes. This has been dominated by the US over the last several decades.

An interesting corollary: the nature of Eurasia is that it’s susceptible to constant wars — countries there frequently get devastated as a result. On the other hand, the US intervenes when it’s advantageous to its interests, but is itself really hard to invade. This appears to be why the US always ends up net ahead, after each global conflict.

This means a presence on two oceans is a massive advantage

This was one of my most eye-opening moments in the book. It naturally flows that if naval power is important, being on two major oceans is a huge edge.

The cost of maintaining carrier battle groups in the Persian Gulf is a greater outlay than the total defense budgets of most countries.

That’s insane. As a reminder, the US has 11 carrier groups, with 2-4 carrier groups active at any given time, and a couple on standby (just, you know, casually).

The US “Grand Strategy”

One of the things I like about the book is that it often lays out a sequence of logically-coherent points; at the end of which the ultimate conclusion is sort of self-evident (or at the very least, it makes an outlandish conclusion very plausible). This is what he argues the US’ grand strategy is (in ascending order of ambition and difficulty):

1. The complete domination of North America by the US Army

2. The elimination of any threat to the US by any power in the Western Hemisphere

3. Complete control of the maritime approaches to the US by the Navy in order to preclude any possibility of invasion

4. Complete domination of the world’s oceans to further secure US physical safety and guarantee control over the international trading system

5. The prevention of any other nation from challenging US global naval power

I mean, when you put it like that…

U.S.A.! U.S.A.! U.S.A.!

In all seriousness, the US knocked out #1 by 1850, #2 by 1900, and #3 by the back half of the 20th century (though this is complicated with the rise of nuclear weapons which don’t require an “invasion” to win). #4 was certainly true by around 2000. This explains why the core of US geopolitical strategy over the last 50 years is focused on #5, sometimes through direct intervention, and sometimes through proxy wars.

Conversely, it is the natural order of things For every other country or groups of other countries to band together to pose a sufficient resistance or bulwark to use US hegemonic power (e.g., BRICS)

Military action as a tool of disruption, not victory

Most of us probably have a fairly conventional way of thinking about war as having “winners” and “losers.” The framing below was instructive: if you’re a superpower, your goal isn’t to beat someone, it’s only to shake things up so that no real threat can emerge.

About China and Russia

Is the red sun a false dawn?

This is probably Friedman’s most incorrect take. He calls China a “paper tiger,” but by all evidence since, they’ve only continued to rise in power. I think this is a flat-out incorrect call.

He does have some interesting observations.

First, that China is an “island” which keeps them from being able to exert the same geopolitical influence easily. He discusses this in both a geographical and a strategic sense.

He also identifies their recent economic crunch, early — especially due to government intervention and policy. An interesting parallel he draws is to Japan: artificially low interest rates and market manipulation due to government intervention in Japan led to a massive debt crisis and then stagflation starting in the 1990s. The same thing has been happening in China in the early 21st century.

But the big difference that I think he's overlooking is China's sure willingness to take on short-term pain and impose its will on the economy and country in order to play the long game:

• Allowing Evergrande to default ($300B in liabilities) → plummeting home sales (-20%) and layoffs in real estate

• Clampdown on the tech sector (Ant Group, Alibaba, Didi, ed-tech ban) which led to $1T+ in market value wiped off and a sharp pullback from foreign investors

• Being okay with youth unemployment (>20% in 2023)

These are measures that Japan (and certainly not the US) would never be willing to take.

The same also translates to a single-minded commitment to growth and aggregate (not individual, and certainly not communist) prosperity. This is sustained and obvious in the speed at which China has built up its technological, military (about to ship its third aircraft carrier), and economic power — even if it means a lot of pain for a large number of people, including substantial loss of life (e.g., limited safety regulations in infrastructure projects means they get built a lot faster).

Geographic buffers dictate conflict way more than I previously realized

How far do you think St. Petersburg are from NATO territory? I might have guessed 500 miles.

Try 70.

This is how Friedman was able to predict that Russia would invade Crimea six years before they did so in 2014: they have little to no geographic buffer.

St. Petersburg is within spitting distance of both Estonia and Finland. More importantly, Moscow is <500 miles from NATO (Estonia). And if Ukraine were to join NATO, that number shrinks to <300 miles.

None of this is to justify an invasion of Ukraine. Putin’s a megalomaniac, and I can’t believe we’re letting Russia slide. But set in this context, Russia's actions are easy to fathom, even if they're not defensible. Ukraine making noise about joining NATO backed Russia into a corner to protect its own interests.

About demographics

More technology results in fewer children

I love how matter-of-fact he is in discussing all this. It’s refreshing because he’s not expressing value judgments. When stepping back to discuss things that happened over the course of centuries, it's very clear, he’s not saying something is good or bad, but simply that it is.

This is a killer quote: “Children went from being producers of wealth to its most conspicuous form of consumption.”

This has directly played a role in rise of feminism

All of this is fairly obvious — globalization and industrialization have all led to development, which leads to female emancipation. But it’s fascinating to see the chain of logic and events laid out so plainly:

An interesting corollary is that one could draw a straight line from vaccination to feminism: vaccination → survival rate of children is higher → then women need to have fewer children to get to the “equilibrium” number of children the family should have → then women spend less time in child birthing and nurturing → then women are able to work themselves and become economically independent.

The role of demographics in shaping economic and geopolitical power

While this has risen in our dialogue significantly, the coming demographic collapse was less-discussed back in 2008. Friedman nails it here:

The one thing I think Friedman is overlooking, though, is the role of technology and automation, especially with AI. Historically, labor was the only way to increase “capacity,” and that certainly took a backseat starting with the Industrial Revolution, we're about to see that take into a whole new level. To be fair, he does acknowledge this later in the book, and estimates the impact will land around 2060 with advanced robotics… but as we’ve seen since, that’s exploded in a way no-one can predict.

That said — there’s a very strong argument to be made that relative power between countries is very likely to be shaped by population trends. A country that is 100M+ people and stable or growing (either through birth or immigration) is a lot more likely to be dynamic than a country at 200M people but declining to 160-180M in 20 years based on a TFR of 1.6. At least, conceptually? I guess we’ll see how culture evolves.

This also poses a strong argument in the immigration argument that is currently raging in the US. If our population is decreasing and population is critical to economic influence and geopolitical strength… then supplementing the natural decrease in population with people from abroad — particularly the most skilled people from abroad — is not just a very good thing but a moral imperative for the future strength of the United States. And in fact, because of our identity as a culture of explorers, pioneers, and immigrants… the US is probably the only major power (or even the only country >100 million people) that can improve demographics via immigration, without diluting its identity.

This is not to diminish the risk of straining the social fabric, if there is a large influx of immigrants in a short span of time. If we prioritize the immigration of highly skilled and pro-social immigrants, and prioritize their assimilation and integration into American society… it makes us strictly better off as a country. Not just in a vacuum, but especially in the face of falling fertility rates.

The impact of population trends on real estate

Most countries and cultures assume that real estate prices will always go up. But is this still true in an environment where populations are stagnant or even decreasing? In fact: Japan and South Korea are early case studies in real estate prices flattening due to shrinking populations.

Final notes

Bring on the Dyson Sphere

“A single kilometer-wide band of geosynchronous earth orbit experiences enough solar flux in one year to nearly equal the amount of energy contained within all known recoverable conventional oil resources on earth today.” — good god, what?

Friedman spends a good 1/3 of the book discussing conflict in space, harnessing space-based energy, and moon bases (not kidding).

When he wrote this in 2007-08, SpaceX was a fledgeling company that had never launched a rocket; and in fact it seemed that space as a frontier was fading as a priority for the world as a whole.

The expansion of human attention to space is something that would have been much more contrarian at the time — less than 20 years after he wrote the book, this trend seems to be something Friedman got very right, and happening much earlier than he predicted. But to be fair to Friedman, I don't think anyone could have predicted the way in which SpaceX would be able to revolutionize launches, or the rate at which AI has exploded in the last five years.

This is a common theme in the book: Friedman underestimates the pace of technological development, and it appears he overestimates the pace of geopolitical developments. For example, he thought that Russian aggression would rise and fall over the course of the 2010s (which, as we know, is still playing out in 2025).

Underestimating American bureaucracy

He lists the major tendencies that influence human action as geopolitics, technology, demographics, culture, and the military. I think there’s a big sixth one, and that’s the bureaucracy and obstructionism that rises in late-stage societies. I suppose he would argue that the US is an adolescent, but if there were ever to be a counter to that argument, it’s how little we seem to be able to get done with infrastructure, (de)regulation, and fiscal prudence.

This might be the downside of one of the US system’s greatest strengths: checks and balances. It was designed to check the power of a dictator, but when an action can be blocked from several avenues, the safeguards and failsafes can (and will) be hijacked to limit any change, because people are typically afraid of change.

The other reason this might be happening so soon is precisely because things move a lot faster now. The pace of change (and life) is a lot different than a few centuries ago.

• Roman hegemony took 500 years to develop, reigned for 500 years, and fell over 200 years

• British hegemony took 150 years to develop, reigned for 200 years, and fell in <50 years

• American hegemony took ~50 years to develop (1990 - 1950), and ~75 years of joint or solo hegemony.

It’s not crazy to think this could unravel over the course of a couple of decades. There’s an argument that it might already be happening. This calls for, in the words of Mad-Eye Moody, constant vigilance.

All in all — this is one of two books that has significantly changed how I think about complex systems in society in the last few years (the other is What's Our Problem by the fantastic Tim Urban). It was a fascinating read — especially if we think of it as “plausible fiction” rather than trying to hold it to a predictive standard.

If you like this post, please forward it on to friends, or share on X :).

It might be useful to check out the context from prior parts below, but I’ve summarized a few key bullets in the “Refresher” section, so that this post can stand alone.

• Part I

• Part II

Refresher: an algorithmic approach to comp

All too often, companies design their compensation bands by only using survey data — to “snipe” compensation for every role in every job family, one by one.

Take someone designing compensation — it could be Total Rewards / People Ops, or a Finance / Biz Ops person, or even a VP figuring out how to compensate their team. They start with extremely messy “survey data,” which has been ostensibly parsed and cleaned by the survey data provider; but is usually

• full of gaps and inconsistencies (e.g., a Director getting paid more than a Sr. Director!)

• has a low sample size for most roles

• and has a very wide range with overlapping bands (especially outside of major metros)

• requires dozens of hours of work to triangulate compensation bands for each title, from these scattered and contradictory data points

• and of course, data on non-base salary (bonus, commission, or equity) is virtually non-existent

Some data sets are better than others — e.g., Pave has more data than most, and Carta tends to do equity quite well. But all “comp surveys” fundamentally suffer from this issue due to methodological constraints.

So when you want to set Staff Engineer comp at a Series B company in Memphis, you get a “🤷 Not Enough Sample Size”… or worse, misleading data points.

We think this is backwards. Your bands should be set based on a compensation philosophy / framework, but informed by benchmarks.

What we do think is useful, is a data science approach, which accounts for each factor. E.g., let’s say

• L6 vs. L5 is a progression of 117% in base salary

• zone 1 vs. zone 4 is a 20% cost of living delta

• and a L5 Sr. Software Engineer at Series B in NYC is $165,000

Then:

→ an L6 Staff Engineer at Series B in NYC is $193,000

→ an L6 Staff Engineer at Series B in Memphis is $154,000

In Part II we discussed…

• Determining the “entry-level” salary tied to minimum exempt compensation

• How to plot levels for each Job Family

• The idea of a fixed progression or multiplier across levels for total comp

• A single, simple formula to estimate total compensation

In Part III, we’ll discuss…

• how to take the same “progression” approach to setting base salary

• norms for bonus across levels

• calculating equity / profit share / other “skin in the game”

Progression for Cash Compensation

We found that it’s important that there is a consistent progression (say, 20%) between each level — not 7% or 30%:

1. it’s a simple, consistent approach that’s easy to explain to employees

2. avoids overlapping bands (to be avoided for fairness and unbiased comp)

3. no too-small or too-big jumps between levels — e.g., you don’t want one level promotion to result in a $2K bump but another to result in $20K

This is why Fair Offer takes as given that there should be a fixed progression between levels. In other words, if L5 makes 18% more base salary than L4, then the base salary for L6 should be roughly 18% higher than L5’s.

From our analysis, the Base Salary progression between levels tends to range between 15% and 20%. A good rule of thumb:

This has to be done with nuance; e.g., if you were to take investment banking, the jumps look a lot bigger (e.g., 40-50% not 20%) but this is really because they effectively combine levels because they’re much more aggressive about up-or-out — so getting from a first year associate to a third year associate at a bank is effectively a promotion even if your title is the same.

Structuring bonuses

Bonuses tend to be one of the simpler elements of compensation. Not every company pays bonuses; but every one that does, ends up following similar pattern. It’s normal to not have a different bonus percent for every level, but rather have it change every ~2 levels. Here’s a starting point for bonus that might help:

Unsurprisingly, the range of possible bonuses vary more as you get higher up the chain, and it’s dictated by the company’s performance-orientation (modest vs. aggressive).

The range is meant to illustrate both the variance by company due to culture, and target vs. top performer bonuses; e.g., the top end of this range of 80% for a Director might be for a 5-rating, while the 40% is for a 3/4-rating. It’s common for underperformers to get nominal or no bonuses.

Many companies have a company component and personal component. The above figures are meant to illustrate a business-as-usual scenario. When the company component is lowered because the company had a bad year and the bonus pool is underfunded, it gets extremely situation-dependent. Then, the appropriate bonuses are more a function of hard financial constraints balanced against the need for retention, rather than any sort of comp philosophy consideration. So, we don’t go into that here.

An overview of Skin in the Game (aka “SIG”)

Besides base salary and bonus, there’s a last component to total compensation: usually some form of “skin in the game” (or SIG) compensation. This is dictated by how ownership-oriented the company is, but can take many shapes

• Startups and tech companies tend to skew high on equity

• Professional services tend to only give any ownership to partners

• PE-backed run companies do profit-share, or profit-participation units not equity

• Others do bonuses that are tied to company performance which is effectively profit-sharing, even if they don’t call it that

Whatever shape it takes, a lot of companies pay salaried, exempt workers some sort of SIG compensation.

That said — many businesses don’t do any skin in the game at all (even if they’re massive and have 10k+ employees); this is simply a choice, all of which interplays with the sort of culture and talent they’re seeking to attract and retain.

Then, the (additional) aspects of Total Compensation can be broken down into

• If applicable, a reasonably standard bonus structure

• The annualized value of any SIG compensation (profit share, equity, etc.)

Since the headline numbers are Base Salary and Total Compensation, it’s better to treat Total Compensation as the top-down driver; SIG fills the gap after base and bonus.

So SIG should be derived from Total Compensation, not the other way around:

How to calculate and communicate equity compensation

We often see people trying to estimate the right amount of SIG compensation independently (how many shares should a VP Marketing get at a Series C company?).

If your chosen mode of SIG compensation is equity (whether Restricted Stock, Options, or RSUs), it is a cardinal sin to look up benchmarks that are only denominated in number of shares.

A share is not a share is not a share. A share of stock at Robinhood is $40 and Meta is $550. When you’re comparing offers, is 10,000 shares from Robinhood the same as 10,000 shares from Meta? Obviously not. Private companies work the same way.

Percentages of a company are slightly better, but even that is problematic (0.1% of a series B company is not the same as 0.1% of a seed stage company).

This can get complicated — and this is the reason equity compensation is so poorly understood and communicated. But here are 3 simple steps to translate equity into compensation (and, as a candidate, to understand how the best companies do it, so that you can expect the same for your offer).

• Step 1: Determine “Target Equity Value” (in $ / year)

• Step 2: Find “Target Value / Share”

• Step 3: Convert into Shares according to the vesting schedule

Step 1: Calculate the Annualized Target Equity Value in dollars, same as above

Step 2: Find or Calculate the “target value / share”

1. For RSUs, this is just the fair market value of each unit — which typically converges on a) public market share price for listed companies and b) 409a valuation for private companies.

2. For Options, this is more complicated.

   1. The 409a at the time of the grant is used to set the strike price (i.e., what employees pay to purchase the stock); so it would be exactly incorrect to consider that figure to be compensation of any sort. If anything, it’s the opposite.

   2. Instead, a standard practice is for a company to calculate the “spread” between the preferred price and the 409a valuation, as the “target price per share”.

   3. While this is not compensation, strictly speaking, it’s an illustration of what the equity grant could be worth if the company were to exit today, at the most recent headline valuation.

   4. The finance / legal team might sometimes determine a target price per share to be used in total comp calculations, which is different due to various factors (market conditions, time / growth since last funding round)

3. For Restricted Stock (which is only a form of equity granted very very early on — usually earlier a seed round), it’s a bit simpler; it’s normal to consider simply the preferred price to be the “target price per share” because the 409a effectively rounds to $0 at a company so early in the journey.

We uniformly refer to this as “target value” and not simply “value,” since the figure doesn’t always match “fair market” value which is a legal / tax definition.

Step 3: Translate into Shares

Finally, combine these inputs with the vesting schedule to determine the number of shares to be granted:

Let’s say Target Equity Value is $40,000 / year for a given candidate, and the Target Price / Share is $5.00.

Assuming there’s a vesting schedule (say, a standard 4-year vest), then this figure should be multiplied accordingly to calculate the offered number of shares.

$40K / year in target equity value ÷ $5/share = 8,000 shares / year
Multiply by a 4-year vest = 32,000 shares

Note: These calculations assume no refreshers or performance triggers — those should be layered after setting baseline equity compensation.

Cheat Sheet to design compensation bands (base, bonus, and equity)

1. Identify reasonable benchmarks for a single key role within each job family: pick one where there’s lots of data, typically in the middle of the leveling guide (e.g., ~L4-L6 — Sr. Software Engineer, Sr. Product Manager, Marketing Manager, Sr. Account Executive, Operations Manager, etc.).

2. Extrapolate base salary across levels: multiply or divide using your progression factor (e.g., 1.16–1.20 between levels)

   • Gut check against benchmarks and internal expectations

3. Set target bonuses for each level: apply reasonable bonus multipliers (consult the table below if helpful)

   

4. Derive Target Equity Value / year for each level from Total Comp, Base, and Bonus:

5. Convert into shares:

• Identify desired Target Price / Share (depending on whether you issue Restricted Stock, Options, or RSUs)

• Calculate the number of shares to issue as:

In our final post (Fair Offer Part IV: Beyond Borders), we’ll cover cost of labor adjustments as the final element of a well-designed compensation policy.

I joined a ~year in, as a founding team member at the start of 2015. The secondary market didn’t exist 10 years ago — secondaries were basically a dirty word then.

Knowing what I know now, Forge (which was known as Equidate at the time) was one of those rare companies that was born with product-market fit: we could go say to early employees “we can get you some long overdue liquidity,” and we could go to angels and family offices who (at the time) had very little access to private markets and say “we can get you exposure to some iconic companies” (Airbnb, Uber, Dropbox, Snapchat, SpaceX). The 2-sided demand was not the issue. It was just a complex legal, compliance, and operational execution game.

They were three scrappy guys in a living room in SoMa when I joined. The company had just closed its first backlog of trades, and we were starting with a fresh slate on some new deals. We tripled revenue QoQ in the first few months of that year, and it was very exciting.

About 4 months in, we got subpoenaed by the SEC. Fun story (/s) for a different time, but the TL;DR is that — while we worked through that issue — we couldn’t really transact via our proprietary trading instrument. So our revenue went nearly to zero for the rest of 2015. Gil Silberman, one of the cofounders (who recently passed away, rest in peace), said “we’ll sell broomsticks if we have to.” I remember thinking “I’m going to do whatever it takes, put this company on my back if I have to.”

At the start of 2016, we hired Rob, a new head of BD. A few months later, as legal bills from the SEC issue piled up, we nearly ran out of money. I think we had maybe a month of runway. The team (except Rob, since it was a bad look to ask a new hire to take a hit) took 50% pay cut. It looked dark.

Then in Q2, we closed an emergency (down) round and settled the SEC issue, back to back. After that, our growth was incredible. Our annualized net revenue graph looked something like:

We went from basically ~$0 revenue to ~$30M in about 2 years from early 2016 to early 2018. Back in the pre-AI era, that wasn’t really a thing.

We went from struggling to recruit good people, to a spot where incredibly talented people were eager to join us.

What’s wild is that — aside from an increased emphasis on institutional relationships — we weren’t doing anything that different between the start of 2016 and the start of 2018, but our revenue exploded >100x in that time and was actually accelerating as we built a brand and network effects. So what made things suddenly work?

Which brings me to…

Blackjack

Startups are very affected by luck, but they aren’t a lottery. It’s not like every action you take is random or a long shot. But you do actually take a lot of shots.

When we were making bets at Forge, it felt like a series of 50/50 decisions, coinflips. Do we work on this deal or that one? Do we build this feature or that one? Hire this candidate or that one?

Every time, you decide how much you want to invest in a given decision. Depending on how they go, you win, lose, or tie.

It turns out this is a lot like Blackjack.

Every player has a chip stack. The startup is the runt at the Blackjack table. The one with $100 when the table minimum per hand is $10. The incumbent 800 lb gorilla is the player with the $10,000 chip stack.

Competition

You're the startup, and you love the thrill of the game. Every hand matters. You're having fun. You look over at the $10K chip stack and see BigShot is betting the same $10 you are. What a joke. BigShot glances over and smiles to be friendly. Here's what you and they are both thinking:

The players aren’t playing each other. This isn’t poker. They’re both playing the dealer.

The startup is not really competing with the incumbent. If they are, they're playing the wrong game. And the incumbent doesn’t really care about the startup. Not yet, anyway. They’re both playing against the market.

Placing bets

As a startup, you obviously don’t get infinite coinflips. In fact, you actually get deceptively few. It turns out that most decisions sort of don’t matter. You work on a deal that closes, and another couple that don’t. You win one hand, lose one hand. That is execution -- it's essential work. You have to play every hand. But it's not a decision.

In fact, the only decisions that matter are a few big ones: you split the 8s, get a 3 and a 2. You have 11 and 10 against the dealer’s 5. You double down on each. You win big or you lose big.

BigShot on the other hand… the big hands aren’t actually that big for their chip stack. So after a wild swing they might go from $10,000 to $9,700 to $10,200. Whoop-de-fucking-doo.

A few rounds on the Blackjack table later…

The only way to win in Blackjack is to make lots of money against the dealer. And that can’t happen in one hand. It would be a terrible idea to bet your entire stack on a single turn, no matter the hand you were dealt.

Instead, you try to build on a series of modest wins. After the first 100 hands, if you’ve played the odds correctly and had some luck… let’s say you had 5 hands where you doubled down or split, and you won all of them. Now you’re at $250 instead of $100.

But you’re a risk-taker. So now you don’t bet $10 a hand, you bet $25 a hand.

In the meantime, someone comes up to you and says “Can I stake you another $150? You keep some 50% of my gains and give me back 25% of the losses.” (That’s a VC, obviously. And I know this is not really legal in a casino, this metaphor isn’t perfect okay?)

You have the same sort of run again. Now you’re at $1,000. Even net of what you owe your backer, you now have $925.

You raise the stakes a bit again. Not quite proportionally — you have to preserve some of your gains of course. Let’s do $50 a hand. BigShot is looking at you like you’re crazy. You’re loud, everyone’s rooting for you. BigShot starts to bet bigger, too — now they’re doing $100 a hand.

The dealer gets a few Blackjacks in a row. Now you’re back down to $750, and BigShot is down to $8,500. Ouch. (That’s a market downturn.)

Bitter, the whole table pulls back. You bet $30 a hand for a while. You get on your third streak, and you’re suddenly up to $2,000.

Rinse and repeat.

There are very few decisions every year that are company defining. Early on, there might only be 5 of these a year. The honest truth is that you often don’t know for quite a while if this was one of the defining decisions of the year. You just do the best you can, with the information you have at the time.

If the average new buy-in player has $100, maybe some people start with $50. Maybe that startup is from the midwest and this is their first time playing, or they don’t have jazzy credentials. Some others start with $300. That’s like someone with just the right credentials at the right time — they’re starting from second base. No one said life was fair. But every hand has the same chance against the House.

And just like with Blackjack, you can give yourself an edge. You can learn how to count cards. That’s like being a serial founder or operator with prior exits. They’ve played a lot before and they know when to double down when the count is high, or lower their bets when the count is low.

If you’re tipping the dealer, you’re dedicating resources to something that doesn’t matter for winning. The House doesn’t care if you tip the dealer. The Market doesn’t care if you’re donating some of your profits to the Red Cross. That’s not to say you shouldn’t do it — being a good corporate citizen is nice. But it also doesn’t change whether you win. And you certainly shouldn’t do it until you have a bigger chip stack. Survival is more important than generosity.

Blackjack tables sometimes have “side bets.” These are long-odds that you can place that pay off big (e.g., 50:1 or 200:1) if they win. They’re fairly bad expected value but feel good when you win. These are the “skunkworks” or side projects that a company might undertake. Putting $5 on these when you have $100 is stupid. But maybe putting $20 on these is one of the only low-risk ways to move the needle when you have $10,000.

Blackjack also has a few varieties: single deck, double deck, switch, Spanish 21. Many tables pay 6:5 on Blackjack and others pay 3:2. They all have very different odds and very different strategies. A strategy works in one usually falls flat on its face in a different game. As you probably guessed, this is the “market” you’re playing in: consumer vs. healthcare, B2B vs. hardware. Ideally, you want to avoid the markets with terrible odds and try to pick the good ones… unless, of course, you really enjoy Super Fun 21, in which case — go for it, I guess.

It usually doesn’t matter whether a table “looks hot.” You might have a table with lots of people cheering, and people hovering around to stake apparently-hot players. But this isn’t really an indicative signal of future results. By the time you get there, the high or low count has probably been arb-ed away. And it could have just been a brief spurt of luck... which led to every player over-extending their bets and busting in the end.

You also have to make sure you're properly incentive aligned with the backers. There's a fine balance in terms of how much leverage their capital can add for you, versus feeling like keeping them happy is more important than the right answer. And you should probably avoid loan sharks (aka venture debt).

It’s important to remember the dealer always has the edge. If you start with $100 and play perfectly, you lose all your money in ~100 hands. You have to stay ahead of the game just to survive. Maybe 10% of players leave a casino having made any money.

But the most important takeaway here this is a long game, and wins and losses compound. If you’re making smart bets every time, ideally while counting cards, and you’re patient and don’t make any reckless decisions (and you’re not putting away too many free drinks), your $100 becomes $500 and just might become $3,000 in a few hours.

Have fun. Just remember your limits, and try to quit while you’re ahead.

Much of antitrust case law is rooted in businesses that dealt in "atoms", in a localized world. A simpler framework is better for a digital, global economy.

IMHO: Anti-competitive behavior is when

• a large company

• throws its weight around

• and it results in materially worse outcomes for customers and competitors

• over the short or long term

That's it.

What anti-trust is not:

• When a large company simply exists (without explicitly wielding its influence to crush competition)

• A company becoming massive through sheer quality of products or execution

• When it results in strictly better outcomes for everyone (which is rare)

• When a company is making choices in its interest which simply disregards the needs of others (a company is not intrinsically responsible for doing social good)

• When a company makes a tradeoff that makes one segment (e.g., customers) better off at the expense of another (e.g., competitors)

• When there are "structural concerns" (e.g., market concentration, size) – IMHO these aren’t a problem unless a company actively abuses its position. It's easier than ever to compete against an incumbent, no matter how strong they are. Just look at OpenAI or Anthropic vs. Google DeepMind / Brain.

Not examples of anti-trust:

• Apple only allowing approved apps to be installed on iOS (tradeoff prioritizing consumer security)

• Apple's proprietary connectors (e.g., Lightning)

• Google's dominance in search

• Google building Chrome or Chromebooks

• Amazon buying iRobot (now basically bankrupt) or One Medical

• Meta buying Giphy

• Amazon / Meta / Google / Apple acqui-hiring small teams (which has been completely stifled)

• Amazon bundling Prime Video (the streaming market is *extremely* competitive, which indicates that competitors are certainly not getting pushed out of the market; and consumers certainly benefit)

Examples of anti-trust:

• Apple forcing a 30% cut of Spotify subscriptions and preventing linking out to a web payment page (or any other app which isn't iOS exclusive; consumers are searching for Spotify and happen to download it on iOS, they didn't discover it thanks to the App Store)

• Apple's ATT (App Tracking Transparency) -- consumers aren't "hurt" through better tracking; in fact, it's easy to argue that poorly targeted ads are worse. Importantly, Apple is not subject to any of these restrictions in serving its own ads.

• Google favoring its own reviews over Yelp ratings

• Google controlling both the buy-side and sell-side of the search ads market

• Amazon copying its merchants' products based on intel that should be proprietary to its merchants, and then ranking its own products higher

• Adobe buying Figma

• Visa buying Plaid

• Visa and Mastercard's stranglehold on merchants, effectively colluding to price-fix interchange

• Basically every business practice of Epic Systems

• The Live Nation / Ticketmaster merger (and subsequent cartel-like behavior)

• Predatory pricing by Uber to push Lyft out of the market

• Tech companies "price fixing" salaries and colluding on talent in the Bay Area with "no poach" deals

50/50 calls:

• Facebook buying Instagram or WhatsApp. Hindsight is 20/20; Facebook wasn't as dominant in 2014 so it was waved through; but obviously in retrospect it was dominant *enough*, and those acquisitions were crucial in giving them dominance over social media. But that's also not black & white: look at Tiktok's emergent success.

• Microsoft's acquisition of Activision; while the market still has serious players (Sony, Tencent), it definitely made the gaming market more oligopolistic.

• Monsanto protecting the IP of its genetically engineered seeds.

Matt Cohler wrote this thoughtful breakdown of the different kinds of building at a company:

All three of these skills are critical to building an enduring, large enterprise. But those are very different skills. A company probably doesn’t need to be (in fact, they probably cannot be) world-class at all of them. So what core competencies should you strengthen as a founder or early-stage exec?

An analogy might help with this conundrum: the most common way for an employee or leader to succeed is to be “T-shaped” with their skill: baseline competence at a lot of things (i.e., being a generalist) with deep expertise on a given topic (i.e., a specialist).

A second way to succeed professionally, less often discussed, is to be elite (but perhaps not world class) at a number of things. This versatility means that the “matrix multiplication” of skills ends up being world-class. For example, if you are top 5% at verbal communication and top 10% at creating visualizations, you are probably in the top 1% at giving presentations.

Product-building

Being world-class at building product is tremendously high-leverage. It is also exceedingly rare; this is why very early stage investors are obsessed with technical capability and the ability to build product well.

This is also why first-time founders are obsessed with product (note: I’m using that word in a generic sense… software for a B2B SaaS, or physical goods for an e-commerce company). Most companies win or die on the strength of their product. It’s the very first failure-mode you encounter.

It goes without saying, but you cannot build this as a differentiator without having a 10x technical person on your founding team.

Companies known to have succeeded off their product quality include some of the most iconic names: Facebook, Stripe, Airtable, Notion, Figma, Linear…

As you might expect, this does not mean you can do zero work in the remaining elements of a company's journey. To be T-shaped as a product-first company, you have to be very competent at sales, basic corporate hygiene, etc.

Business-building

What do you do if your product vision isn’t particularly unique or defensible, but it still doesn’t exist (for some reason)? You will probably need to be world-class at distribution (be it sales, marketing, vitality, or something else).

Many, many successful companies have been built without a transcendent or defensible “product” but rather a unique GTM or business model that captured the world’s attention. This is often / especially the case with marketplaces that carry network effects.

Airbnb, Uber, DoorDash, Robinhood, Rippling, Deel are companies that are known to excel at distribution — even if many of them are also strong on other dimensions of product- or company-building.

Here, I have an object lesson: what happens if you don’t end up building competence at other aspects beyond one? I was an early exec at Forge, which was a marketplace for private company stock. That company’s “product” was born with product-market fit… we were going to early employees of unicorns and offering them millions of dollars in liquidity, and on the other side bringing hard-to-access private shares (with big upside) to family offices or institutions. But the “product” was something every competitor could access, too — so this was a distribution-first company. We were terrific at GTM and closing deals, and scaled very rapidly from $0 to $20M+ in revenue within ~2 years between 2016 and 2018. However, the company never built software or services with true lock-in, faced challenges with culture, and had a lot of turnover for many years — so it did not achieve the same level of success as the other companies listed above.

This is also a major distinction between Zenefits and Rippling — Parker Conrad is a generational marketer and fundraiser. But with Rippling, Parker appears to have built more product early on (there are still mixed experiences but it’s arguably good enough), and notably the company has avoided cultural issues that plagued Zenefits.

Company-building

This is a more nebulous area so I’ll characterize this to mean “being exceptional at recruiting and fundraising.” Ramp, AngelList, Opendoor, and of course PayPal are examples of companies that are known for being “talent vortexes” with extremely strong early teams, and raising a lot of money at favorable terms. These companies often spawn “mafias,” where alumni go on to start or lead companies. It’s very common for second-time founders to be effective at recruiting and fundraising, so many second acts benefit from this core competency.

Matrixing your way to Success

Finally, there are companies that are very good at many things — they picked a great market, built an excellent product, distributed well, raised money, built solid teams. They may not be industry-leading at a specific thing (or maybe they are!), but by being strong across many dimensions, they end up building a “system” where 1+1 = 3.

Snowflake built a deeply technical product but is known to be very sales-led. Coinbase was strong technically but also had to be at risk-management and compliance. Mercury is known for strong design, while also operating compliantly in a highly regulated space. Amazon was world-class at supply chain & inventory management, while excelling in storytelling and vision.

Great companies’ core competency matches their market

There are loose patterns to this:

• Software startups are often but not always product-led.

• Consumer, marketplace, and enterprise startups, are often but not always business-led.

• Serial founders, rollups, and complex new categories are often but not always company-led.

• Regulated or operationally intensive companies are often but not always matrixed.

The DNA of leadership dictates the DNA of the company

Wildly successful companies had a DNA that was an exceptional match for the problem they were solving. One of these competencies takes root early on; usually derived from the founders, CEO, and the exec team. This common theme affects the hierarchy and power centers within the company, their culture and values, and how they make decisions.

This is what early investors are trying to identify with “founder-market fit”… not just whether the founders have any particular background or experience in the relevant subject mater, but whether the founder → company’s native language (product / business / company / matrix) matches what they’re building. Will their tendencies and gifts shape an organization that will be an exceptionally good match for the problem & market the company is tackling?

This leadership DNA shapes the company. A company that succeeds by being exceptional product, sales, or company-building — or by being matrixed — can succeed in many markets, purely on the strength of their capabilities. But if the DNA of the company is an ideal match for the market and problem its tackling, something special happens.

A couple of definitions before we dig in

Software vs. Services (vs. Agents):

• Software companies sell an API call1.

• Services companies sell a fraction of someone’s time (whether they are tech-enabled or not); trust and the human touch is an intrinsic component.

• Agentic companies sell outputs; the customer doesn’t care how it’s done or by whom… they just care that it’s done.

Tech-enabled: Your company is counting on automating some of the work that the service-provider is offering in order to improve margins to make them look more attractive to an investor. This is your main competitive advantage compared to “old-school” incumbents.

Venture-scalable: A company that suits the venture capital funding model, which typically means: a company which can exit at a $1-5B+ valuation within 7-10 years.

• This requires a company to grow really rapidly for several years in a row (2-3x+ / year).

• One common benchmark that startups use to meet this cadence is “T2D3” (triple twice, then double three times) — this results in a 72x in 5 years.

If an institutional VC leads a seed round, Series A, etc. in a company, they are expecting it to behave in a “venture-scalable” or “venture-backable” manner. (Note: angels or PE / growth investors don’t always have the same expectations.)

Things that are not to be confused with tech-enabled services:

• Implementation services: offering one-time services to implement software does not count as tech-enabled services. Ultimately, the company is mainly buying your API calls, not your team’s time.

• Services marketplaces or BPO firms: if the service providers are not on your P&L (e.g., Uber, TaskRabbit), then you’re a services marketplace not a service provider. The dynamics of that model are totally different.

• AI Agents: as mentioned above, an agentic startup is typically trying to sell outputs or well-defined “units” of work. This work is not denominated in hours, and hence is not services from a customer’s perspective.

Why might one want to start a services-centric business?

You can charge a lot more for services than you can for software. In my experience, customers will usually pay between 5-10x for a person doing the work, compared to software that will do most of the work for them. This is true even if it takes the customer the same amount of time to manage the services provider as the software.2

As a founder, this probably sounds awesome! “I can charge $2K / mo = $24K / yr for services rather than $200 / mo = $2,400 / yr for software? And I can spend less time coding upfront, iterate more rapidly… I’m sure I can figure out how to make the work efficient later. In the meantime, I can crack $1M in ARR with just ~40 customers and raise a big series A in a year. Simple!”

Business Model Dynamics

Gross margin & resulting valuation dynamics

Every dollar of revenue is not equal. There are many things affecting “quality of revenue” but two of the most important ones are gross margin and retention.

Software businesses usually have gross margins of 70-90% (vs. 30-50% for services), and they typically have high retention & expansion rates (usually expressed as Net Revenue Retention or NRR). Services businesses probably have very high 12- and 24-month retention because of the human touch, but it probably tapers from there (many services get brought in-house over time).

Which is why, in order to get to a $1B valuation in a steady state, it might require:

• $100-200M in revenue with a “SaaS profile” (80% gross margin, higher NRR)

• $300-500M in revenue with a more “transactional” / “services” profile (50% gross margin, lower NRR)

So you might be able to charge 5-10x the revenue, but if each dollar of revenue is only “worth” a third as much, you’re getting less benefit than you might think.

If you’re curious about the levers here, feel free to play around with my shorthand DCF calculator here.

Context switching is the mind-killer

(Apologies to Dune for the mangled reference.)

From my experience, it’s possible to get services gross margin to 30-35% very quickly based on standardization, templating, and basic automation. But note that in order to get to a $1B valuation with even $400M in revenue, you need 50%+ GM.

So how do you get there?

1. If you raise prices, that will (usually) come with higher customer expectations.

2. So perhaps we ask a given service provider to handle more clients?

Now, remember that the biggest benefit to having a human in the loop is that they remember and account for the context of a given customer, which they leverage to exercise judgment when handling their needs.

This benefit (and hence your right to charge 5-10x more than software) breaks down very quickly when you try to add more clients. For the work we did at AbstractOps — a breadth of back office services ranging from accounts payable, payroll, state compliance, vendor management, contract issuance, etc. — we were able to get from 4 to 6 clients without a problem, but then it completely broke. Going from 6 to 8 clients for even our best OPs (Operations Partners) burned them out within months and destroyed productivity. Humans can typically hold 7 “chunks” in their working memory. This sort of works the same way.

Guess what happens next? The burned out employees leave, which means you have a different switching of context — transferring knowledge from one person to another. This is lossy, expensive, and not a great experience for the customer, but it’s inevitable. We had to cycle some clients through 4 OPs in less than a year.

Besides the bad CX, the cost of onboarding / training / offboarding hurts your business model further.

Management layers and coordination overhead

Scaling services does not come with positive returns to scale. It comes with negative returns to scale.

The ratios will vary by service model, but let’s say that for every 6 service providers you’ll need to hire a manager, for every 5 managers you’ll need to hire a director, and every 4 directors a VP. This pattern means that the amortized cost of a single service provider IC will increase ~15% → 20% → 25% as your team grows.

In other words, you need to improve efficiency by 20-25% just to match your gross margins when you’re starting out. You need to sprint just to stand still.

Services are a more fragile business model

If you’re running a software business, you can spin down AWS servers to reduce your cost of goods sold.

You can’t “spin down” employees. So, during market cycles, the business model is really brittle.

Even a bad month or two makes life really difficult. If you have too few people, your employee hurts; and if you have too many, your business model hurts. There is a very narrow Goldilocks zone for capacity utilization. The lack of flexibility makes a services-first business model much less resilient.

The really great professional services businesses take decade(s) to build: investment banks, consulting firms, and audit firms are primarily hired based on their brand and credibility. Firms like BCG / McKinsey or Goldman / Morgan Stanley can command 70%+ gross margin (in part because they charge 2x the fees of other strategy or IB firms), and this makes them very resilient to market cycles. But that’s not something a startup can replicate in a few years.

All this comes with BIG customer expectations… and churn

When the customer expects to interface with a person, their expectations balloon accordingly. “I’m paying you $2K / mo, I expect a lot more than this.” or “If I hired someone for this, I’d expect them to be operating at a much higher level.”

People intuitively understand that software is an input-output machine, and it won’t do everything you want. But when your UX is a human being, a “user” is tempted to ask for whatever they want. A customer-minded service provider will often be a people-pleaser, and say yes to things that aren’t actually a good idea, which will lead to broadening scope, worsening service quality, and burnout. It’s really really hard to find people who can make customers happy, exercise judgment in tricky situations… but also be firm and say no when needed.

So when (unreasonable) expectations are not met, customers churn, which makes all of your goals above harder.

Cultural Dynamics

People repeat mistakes

Software is deterministic. If there’s a bug and you fix it, it won’t happen again. But the same person might make the same mistake over and over, and different people will make the same mistake over and over.

This sort of quality is nearly impossible to control for, without hiring an army of “drones,” which brings us to…

Motivating people is hard in this environment

There are so many reasons that retaining your best employees is nearly impossible for this sort of business.

Often, the best way to avoid mistakes, create efficiency, and improve margins is to pick a narrow slice of work and do it very well. Effectively, a “productized” service. This means you’re expecting people to be automatons, and no talented person will want to do that for very long.

On the other hand if you add variety to the work, it’s harder to standardize and productize, which will hurt you in the long term.

The context switching leads to burnout, as we established.

Your employees will have the goalposts shifted on them constantly (because your business model will need to iterate rapidly, and the employees are the business model), which will feel very unstable and uncertain.

Recently, employees who have never run a business have also come to believe odd things like think that “employment is theft.” One employee believed “I don’t think the company should have the right to upcharge more than 15% for my work,” without internalizing the fact that the company had to absorb the cost of capacity utilization, training, hiring, sales, vendor / equipment expenses, regulatory overhead, professional services and literally everything else it takes to run a business. Needless to say, this is a frustrating conversation to have.

Incentives sometimes backfire

The way that many long-standing professional services businesses (banking, consulting) solve for motivation is an apprenticeship approach, with timed promotions.

But the issue for a new business / startup (even one that’s growing rapidly) is that they can’t dangle the promise of massive comp increases that a bank or consulting firm can offer. MDs / Partners at those orgs make $500K - xM / year. An early stage company will take a long time to build the brand credibility needed to support the pricing (and hence compensation) to sustain that sort of model.

This sort of model worked at Forge, because there was a massive pot of money in the investment banking / brokerage business, which are an effective motivator for junior employees to grow and succeed.

Apprenticeship and promotions failed miserably at AbstractOps. We promoted quickly (albeit with smaller increases — from $80K to $95K to $110K), which came with the unfortunate side effect of everyone getting obsessed with promotions and comp. We had people get pissed off because they weren’t promoted twice in one year.

Your best people leave sooner, and might take your customers with them

Your best employees will probably work the hardest and get burned out the fastest. In the meantime, their competence is on full display to their clients. And for clients this is the dream scenario: they get an extended work trial, with another company taking on the burden of training and managing the employee.

You probably need to institute a penalty clause in your contract if your clients hire your employees (and even if you do, the pain of offboarding, handoffs, etc. are material). I’m a big believer in freedom of labor, but it doesn’t make this feel any less of a gut-punch when it happens.

What will feel worse is when an employee leaves and starts a mini-competitor; becoming a fractional service provider themselves, and working with a bunch of their former clients. This especially sucks because you lose the employee (whom you’ll need to backfill, train, etc.) but you also lose a bunch of clients.

Your core competency must become people, not product

All this means that your core competency isn’t software, efficiency, or building product. It’s staffing. You need to be world class at hiring, onboarding, training, managing, and firing people. Maybe that’s your happy place, but it was certainly not for me.

Every company has a first-class and second-class function (everything can’t be equally important). If you don’t make your services the first-class function and priority, you will have massive attrition, staffing, customer service, and culture issues.

But if you try to make your product & engineering, the first class citizen, something weird happens…

Your services folks might resist the software

At a retreat, I said something I thought was very logical: “Our job isn’t just to serve customers. It’s specifically to serve customers through our product.”

There was an uproar. The team was already somewhat burned out, and this felt like a top-down mandate from people who didn’t understand how the work was done (which was not true since my cofounder and I were regularly deep in the weeds on client work, and in fact “owned” several client relationships and we did the work).

But there were several understandable reasons, with varying depth and emotion:

1. Getting used to a new product takes time. When you’re stretched thin, you have to pick between getting stuff done the old way, vs. investing time in a “better” way.

2. And for a while it might not even feel like a “better” way! Building good product takes time. Our early versions were full of bugs, which caused a lot of frustration to people who were expected to use it every day. There’s no doubt we could have been a lot better, but I think it’s normal (even healthy) to ship before it’s perfect and iterate in the real world. That doesn’t work very well when your power users are colleagues who are trying to get stuff done. The “normal” way of building feels like teammates are letting each other down.

3. A couple of the team members hinted at the fact that they believed our goal was to automate away all the work and fire everyone. This was absolutely not true; as we grew, even if we achieved tremendous automation and efficiency, there was going to be tons of room for high performing people. But it’s hard (as a non-technical person) to shake the feeling that software is taking away jobs from all knowledge workers.

All this builds up to…

If you’re services-first / product-second, It’s very hard to have an elite product culture

Who sets the priorities at the company? Who speaks for the user internally? If you try to become a product-first company, then the services team will understandably feel like they’re being treated unfairly when — by all measures — they’re driving revenue and growth at the company.

I’ll put it more bluntly.

You can’t do both well — you need to pick one and focus. And because development work is extremely high leverage, you can’t put them second. But if you put services second, you will have a toxic culture.

After all this… Exponential growth is not an option

Let’s say you figure out and solve all the hard parts above. You’re a legendary founder (certainly better than I was).

You still can’t scale this on a venture-scale timeline, when your headcount needs to scale linearly (or worse-than-linearly).

Take an example business with $100K / year service providers, for whose time you charge $200K / year. Let’s say you got to ~$3M in revenue (congrats!) in year 3, with a team of 15 ICs.

Now if you want to do the T2D3 (triple twice, double three times) we talked about:

This is sort of the revenue path you will be expected to follow if you’re building a venture scale business… but the headcount required is wildly different. By the time you’re in year 7 and beyond, you’ll need 2-3 times as many employees (e.g., $200M ARR public SaaS companies average ~700 employees).

The following things should be apparent:

1. Increasing revenue / employee is a slow, painful march.

2. This is a very different business than what you might imagine when you think "startup" or "tech-enabled services". The thing that matters most is whether you can manage well & scale an organization.

3. Maintaining quality while scaling a team to thousands (especially this quickly) is wildly hard.

4. This does not seem fun (at least, it was not fun for me).

Note: The higher-value the work you’re automating, the better; automating work with a market salary of $250K is a lot better and high leverage than work with a market salary of $75K.

This is often the case with brokering, where the work is comparable whether you’re working on a $500K revenue deal or a $25K revenue deal. So we focused on large deals and scaled to $20M+ in annualized net revenue in a few years, with 50% operating margins. It was an awesome ride for a little while.

But (from what I can tell, I was no longer there) a lot of the same cultural and scaling issues kicked in during years 5 - 8. Today, ten years in, the company does $80M in revenue, and will (hopefully) be profitable again soon.

So… What should you do?

Option 1: If you really want to build a company on the venture capital timeline, and swing for the fences with a big exit, you should probably not build a services-centric business. You should probably build a software business, fintech business, etc. Go raise a $1-2M pre-seed (or if you can swing it, skip straight to a $2-3M seed).

Option 2 (this is not for the faint of heart): If you want to utterly disrupt a services industry and ride the AI wave, build a product that only offers unitized services, in an agentic manner. This only makes sense for products that are already fixed-price services offerings. For example:

• Legal: trademark filings… not general corporate legal services

• Finance: bookkeeping… not fractional CFO services

• Sales: lead sourcing… not fractional GTM services

In the former categories of each, there is an expected deliverable that is a knowable quantity of work. It has a clear input → workflow → output workflow.

This was impossible 2 years ago, and now is merely almost-impossible. If you try to build this company, you will probably fail. Especially if you haven’t built an AI product before. It is harder than either Option 1 or Option 2, and you will have serious competition because literally everyone is doing this. Your insights are probably not unique unless you’re a hardcore AI research scientist (there are very few subject matter insights that matter IMHO). You’ll just have to out-execute everyone from a technical perspective, which is a high bar and why you will probably fail. But… You’ll probably still be able to raise a $5-20M seed round if you have great founder-market fit or a prior exit, and obviously that’s a terrible idea and you should not do that (but you’ll probably do it anyway and then regret it).

If you somehow manage to build this, it will probably be a generational company, because AI models will improve to the point where they will be able to handle not just trademark filings but also all other corporate legal services… and you will have the ideal foundation to land-and-expand.

Option 3: If you want to build a tech-enabled services business, accept the fact that you might not look “venture-backable” and that’s fine. In fact, it can be great, but it’s a totally different path than all your friends’ startups and you have to be okay with that. It might look like:

• Bootstrapped, or based off a small angel / F&F round of funding;

• Financially modeled to cross $1-2M in revenue and break even within a ~year of operating;

• Built in a PE-style model: less agile as a business, slower growth (but still pretty good!). After crossing $2M in ARR, your growth will probably slow to <100% CAGR permanently, so an ambitious goal might be $10M after 4 years, $40M after 7 years, $100M after 10 years — with a steady state gross margin of >50%;

• If you do it right, you might generate $1-2M in FCF in year 4 and $25M+ in year 10;

• Getting a taste of entrepreneurship and financial freedom with less baggage and fewer expectations;

• Building software to improve gross margins, to improve customer stickiness, and to expand ACV;

• A lot of your work with automation should probably focus on preserving and presenting a specific customer’s background juxtaposed with your knowledge base to your service provider ICs — this is one of the biggest sources of context switching;

• Exiting it for 2-5x revenue when you get it to a critical mass (usually 7-8 figures in revenue).

• You might get lucky… once you have a strong brand, and with the advancements of AI models, you might be able to get margins up higher, quicker; or grow faster. But if you aren’t ready and you try to force it, you might kill the golden goose. But if it is ready, services might end up being the best wedge of all;

Honestly all 3 options suck because being a founder-CEO sucks, so I don’t know why you’d want to do any of this to yourself.

But whichever path you follow, I hope this post helps.

Thanks to Adam Spector, Noah Itovitch, and Mitali Gala for reviewing and providing feedback on this post, and to Brett Jackson for some good debate on the topic.

That has left room for the term to become a Rorschach test... random observers have shared their own views on how to be a founder or run a company, co-opting "Founder Mode" to justify it.

This is my attempt to define and contrast Founder Mode and Manager Mode in a concrete, falsifiable way. I’ve tried to limit my own takes, instead distilling source material from Brian Chesky, Paul Graham, and Steve Jobs:

Founder Mode means leveraging a founder's context & moral authority to create urgency, clarity, and conviction across the organization.

Founder Mode isn't "better"; it's different. It depends on what an organization needs: roughly speaking, Founder Mode vs. Manager Mode is a tradeoff between value creation and value preservation. Startups are obviously better off in Founder Mode, while your local power grid needs Manager Mode. Given how single-threaded Founder Mode can be, it is critical to set corresponding expectations with your team which you abide by, in order to avoid a toxic culture and mass revolt.1

Defining Founder Mode based on source material

Brian Chesky defined a good bit of the underlying concept on his podcast episode with Lenny last November. Here's a summary of the critical points:

1. When a crisis (pandemic) happened, people wanted Brian more involved.

2. He ruthlessly cut scope – not one team on 3 things, but 3 teams on one thing. This made it possible to centralize the roadmap into a single living document, with 30-40 senior people engaging on it via a single continuous conversation.2

3. He enforced a rule that managers must operate as player-coaches. Leaders must be in the detail. (If you're not in the detail, how can you measure work or create accountability?)

4. The CEO must review all the work, which enabled attribution and created accountability

His overall realization is that everyone really just wants clarity, to row in the same direction really quickly, and he set up systems to enable this.

In another interview, he referenced the founder's contextual and moral authority to be more decisive: being the "biological parent," "having permission to make change", and "knowing [how it works and hence] how to rebuild the company."

Jobs (heavily referenced by both Chesky and Graham) references singleton owners at Apple for each major part of the company: iOS, hardware engineering, etc., and that they get together to talk about the whole company, holistically, for 3 hours. "We all work on the same thing, and bring it together as a product."

Graham in his post also describes a few other characteristics that seem to describe Founder Mode:

1. Avoiding treating parts of the organization as black boxes that are delegated away completely

2. Lots of discussion outside the reporting line (i.e., skip-levels and more)

3. The DNA of companies (and their leaders) should dictate how delegation and autonomy works

4. The concept will get misused by people who want to co-opt it (already happening!); Founder Mode increases upside but also increases potential damage caused by a bad leader

The resulting definition from the common themes:

• Increase speed and clarity by centralizing decision-making (with the CEO + small group of other people); by contrast, consensus is slow and sclerotic.3

• Maximize context for fewer decision-makers, who are "in the details" (a player-coach), including the CEO. Create a single, simple context where decisions are made by that small group (one meeting, one roadmap, etc.).4

• Avoid excessive delegation, because it hurts centralizing context, slows down decision-making, and eliminates accountability.

• Tailor to the DNA of the company and its leaders, instead of installing "best practices."

So what’s Manager Mode, and why is it common?

The more mature a company, the more "responsibilities" it has: customers, revenue, employees, shareholder value... "Move fast and break things" doesn't scale. So it's natural that a company should be more deliberate and "safe" with decisions.

The obvious – and lazy – way to enforce safe decisions is by consensus. This is what Managers usually deploy – committees, stakeholders, buy-in, etc. In a word, bureaucracy.

So the characteristics of Manager Mode are:

• Decrease the rate of change and prefer maintaining the status quo, by creating more dependencies; this reduces the risk of bad decisions (i.e., false positives); by contrast, Founder Mode causes rapid change that is hard to check.

• Decentralize decision-making to reduce the reduce the "bus factor", i.e., dependency on a few linchpins within the company. This makes the organization more resilient than Founder Mode, which relies on a few people sticking around.

• Delegate heavily, since larger organizations have more resources to decentralize the work, and delegation involves creating process – which standardizes and reduces risk with the work being done.

• Create a more generic management structure, through best practices; this enables anyone to step into a company and pick up where the last person left off.

It’s a game of tradeoffs

Manager Mode exists because bureaucracy has its uses; it creates checks and balances and limits the damage any one person can do.

The tradeoff is that it's slow, and nothing new and amazing ever got designed by committee. Speed is often a critical input into being an elite, high-functioning organization.

Founder Mode is riskier for Value Preservation, but is more likely to produce Value Creation.

Manager Mode is better at Value Preservation, and bad at Value Creation.

Endorsing Founder Mode is making a choice to trade off stability for potential upside; it is picking a side in the Innovator's Dilemma.

Of course, these are broad strokes; there are examples and counter-examples for each:

1. Jobs, Zuckerberg, Bezos, and Musk are clear evidence of building multi-trillion dollar companies on Founder Mode. Specifically, they each built multiple groundbreaking products or businesses in rapid sequence because of their speed, conviction, and clarity. That's not a fluke.5

2. IBM is an example of a company on Manager Mode that continues to sustain – while they're no one's idea of an innovative company any more, they're still generating billions of dollars in new revenue off AI. The famous phrase "nobody got fired for buying IBM" is about as succinct an argument for Manager Mode as I've ever heard.

3. Boeing has been in Manager Mode for decades, but ended up destroying brand and value. This is because Manager Mode lacks “central planning”; when left to itself for long periods of time creates layers of bureaucracy, cruft, and misaligned incentives.

4. Adam Neumann and Elizabeth Holmes are prominent examples of Founder Mode leaders who wrote checks that reality couldn't cash. They were, unsurprisingly, notorious for being mercurial and unpredictable, and even idolized Founder Mode people (Musk and Jobs respectively).

For a company that is "too big to fail", or has to prioritize serving existing customers the same way, or providing a public good with minimal risk... Manager Mode is objectively better.

But it's clear why Founder Mode is the most likely path to produce new things: it's inherently a higher variance route, which produces either moonshots or craters. The best practitioners of Founder Mode can add fuel to the moonshot (sometimes literally, like Musk), and minimize the radius of the crater. But they don't shy away from some rate of failure.

It's not an accident that the people most likely to endorse Founder Mode are techno-optimists, and any other way seems like anathema. Techno-optimists believe that change is intrinsically good.

The best practitioners of Founder Mode are incredibly rare; most leaders (founders or otherwise) probably aren't capable of it. It's even harder to find someone who can practice it at 10 people and 10,000 people. This is why most companies default to Manager Mode. But if a company has one of those rare generational leaders – like Brian Chesky – it's a disservice to everyone to "regress to the mean" too soon.

It's all tradeoffs. I recognize the fact that stability and continuity has its value in many organizations. But I do think too many companies switch to Manager Mode before they should, and never rediscover that spark that made them successful in the first place. I wish we had more thoughtfully practiced Founder Mode companies – whether it's a founder at the helm or not.

Thanks to Amar Sood, Noah Itovitch, and Barbara Pascetta for reviewing and providing inputs to drafts of this post.

Your company owes you nothing. You owe your company nothing. Work like hell anyway, because work ethic is a habit.

The interview process is the one of the few places where there's no such thing as coming on too strong. Early-stage companies want people who want to be there. Hedging or playing hard to get — especially if you are seriously considering much later stage (or worse, FAANG) companies — can actually be a cause for concern.

There are two definitions of ownership: owning outputs vs. owning outcomes. When a manager and direct report disagree on whether the employee is “exhibiting ownership”, this is why… the employee is doing a good job at specific tasks / deliverables / outputs, but the manager sees a lack of ownership over outcomes.

Ownership over larger and larger outcomes is how you progress in a (well-run) organization.

High horses have no place at a startup, so don’t get on one.

Almost everything worth doing is hard.

There are two kinds of people: those who live to work, and those who work to live. If you are joining a startup, it should probably be the former — your work should be a meaningful source of value in your life.

The one non-optional capability to make it in a startup is emotional resilience. If you need a day off to "process" news at work, you probably shouldn't be working at a startup.

Try to understand what the CEO, founders, and execs are obsessed with and staying up at night thinking about. Sometimes it’s as simple as just asking in a 1:1.

Be

• high authenticity = say what you think

• high integrity = mean what you say

• high reliability = do what you mean

• high agency = do without being told

• low ego = do even if you disagree

Find others that operate the same way, and appreciate you for doing all of these things.

If you truly have low ego, you’ll find it easy to “disagree and commit.” All too often, people take this to mean “disagree and obey,” which isn’t good enough. The “commit” part is essential. E.g., if a VP of Revenue disagreed with their CEO and then truly committed… then their Sales Director shouldn’t even be able to tell that they VP had disagreed. There should be that much solidarity and trust.

Find a crew of amazing people. Make the most of it.

Find a company where you can trust leadership. It frees you up to focus on actual work without cognitive load.

The Maslow's Pyramid of amazing people is Integrity → Competence → Authenticity → Good Taste.

If you are working on problems that have meaning for you, with people you have affection for, the rest is gravy.

Write a “user manual” for people you work with. It will save a lot of heartache and misunderstanding in the future. (h/t Claire Hughes Johnson)

Deeply understand behavioral styles. Frameworks like DISC make this easy. Internalizing these styles can really open your eyes to how differently people communicate.

Not everyone has to like you, but you probably can’t thoroughly piss off your manager, manager’s manager, CEO, or multiple immediate peers.

In a high autonomy culture, the best managers want you to manage them. They won’t tell you what to do (except in rare cases), but instead will give you a goal to run at… and expect you to pull them in when you need help.

An employee’s job is to execute on their mandates and create leverage for their manager, in that order.

A manager’s job is to unblock, enable, and accelerate their employees, in that order.

An early stage startup does not have an obligation to proactively “develop its people”. At a good startup, you are wearing so many hats and solving so many problems that you are growing and developing without even trying… and others around you are trying to do the exact same thing. No one has the time to think about “developing” you. If you need help, you need to identify that and ask for it.

Don’t expect that every company welcomes politics or "bringing your whole self” to the workplace, or work-life balance. There’s nothing wrong with wanting these things in your workplace, but there’s also nothing wrong with your company not wanting those things in the workplace. This is the culture-fit assessment: the company decides what its culture is, you decide what your values are, and both parties assess during the interview and employment relationship whether those things are a match for each other.

Teammates come and go. The customer and the problem persists.

Life is long and a multi-turn game. If you went through highs and lows with a group of people you respect, it’s a waste to not keep in touch with the ones you cared about.

A (healthy) dose of anxiety and urgency is essential

At an early stage startup, speed is more important than velocity. This is because your advantage is agility. When you can change direction on a dime, the direction matters less than the pace.

The only thing that works for cracking GTM in the early days is throwing shit at the wall, seeing what sticks, and doubling down on that. The “best practice” stuff really only matters post-PMF. (While there are no obviously “right” answers, there are definitely obviously “wrong” answers that you should quickly reject for your product / market / segment.)

Building a startup means feeling bad constantly. When you don’t have PMF, you feel bad. When you do have PMF, everything breaks and your customers might get angry at you and you feel bad. There is no feeling good. (h/t Michael Seibel).

The best definition of PMF is that people want to buy your thing faster than you can make it. (h/t Michael Seibel / Y Combinator).

The essential differentiator of a startup (as opposed to a small business) is growth (h/t Paul Graham).

(The right kind of) experience matters

Spend your 20s trying to discover the things you’re amazing at, that you can harvest the rest of your life.

Spend your 30s trying to find the people you respect and vibe with, that you can work with the rest of your life. (h/t Eric Bahn)

The definition of “being strategic” is the ability to optimize across multiple dimensions to achieve a global rather than a local maximum. The more dimensions you can optimize across, the more strategic you are.

• A salesperson who considers product strategy in their process is one notch more strategic than one who doesn’t.

• If that person also takes into account gross margin and LTV calculations when comparing two deals, they’re one notch higher up.

• If they can consider mandates from the exec team and board in evaluating opportunities, that’s yet another notch.

An MBA has no relevance to building a startup. Being successful at a large company (>1,000 people) has no relevance to building a startup. This doesn’t mean these are adverse signals, but they aren’t obviously helpful signals either.

A banking or consulting background has one relevant skillset for building a startup: the willingness and ability to work long hours, under pressure. That’s it.

All things equal, a person who has 2-3 years of work experience at a quality company is an order of magnitude better than that person as a fresh graduate.

All things equal, a startup hire who has been early at another startup for 1-2+ years before is an order of magnitude better than someone who hasn’t.

All things equal, a founder who has founded and run another startup for a meaningful period of time before is an order of magnitude better than someone who hasn’t.

All things equal, a founder who has been early at another startup for 1-2+ years before is 2-3x better than someone who hasn’t.

All things equal, a team that has worked together before for a meaningful period of time will operate 2-3x faster than one that hasn’t.

Ask and you shall receive (help)

"Mentors" outside the company who are several steps ahead of you in their career almost never make sense (h/t Alexis Ohanian). Instead find either a mentor within your company (ideally but not always your manager); or find a near-peer to meet regularly with that you can jointly problem-solve with; or find someone who's one step ahead in their career.

Even better -- find a support group. Especially if you're going through a hard time, but ideally always. Build vulnerability based trust.

If you find that you still have a lot of unresolved stress or emotional trauma, consider doing a mushroom journey.

Cognitive load and context-switching are the mind-killer. Pay to alleviate these things at work and — if you can swing it or afford it — in your personal life too.

Learning to give, get, and solicit feedback is a skill worth it's weight in gold.

The Maslow’s Pyramid of scaling yourself and your organization is learning how to Do it yourself → Hire people to do it → Outsource it → Automate it → Not do it at all. It turns out that most things don't need to be done.

I couldn't help it. Guess that makes me a Silicon Valley thinkboi.

Some context

My political views aren’t “conventional;” they don’t fit neatly into any party or policy position — democrat or republican, liberal or conservative, libertarian or green.

But I’ve always been a “moderate.” In the last ~decade I’ve moved slightly to the center from the left, but I was never really clearly left or right:

Through hundreds of conversations — ranging from Romney staffers to people who worked on Obama and Hillary campaigns to fervent Trump supporters — most people tend to agree with the majority of views I’ve shared in face-to-face conversations.

But somehow, my tweets from 2021 really riled people up, on both sides of the aisle. Why was this?

Paul Graham had a great post defining an “accidental moderate”:

Intentional moderates are trimmers, deliberately choosing a position mid-way between the extremes of right and left. Accidental moderates end up in the middle, on average, because they make up their own minds about each question, and the far right and far left are roughly equally wrong.

I realized that I might have been “in the middle,” but I infuriated ideologues on both the far-left and far-right by calling out the flaws in each. On Twitter (and online in general), the nuance fades away and you hear what you want to hear.

Worse: the loudest voices on either side branded any opposition to key views as heresy. (Tim Urban’s What’s Our Problem is a must-read on polarization and partisanship.)

Further, applying labels really killed the discussion and made it tribal. We’re so conditioned to be married to “my party,” “my candidate,” and “my President,” that dissenting views — even those valid in principle — collect dust in a corner of our brains.

I think we shouldn’t give a damn about labels and party barriers. There’s much more that unites us than divides us, except we can’t seem to solve anything and end up just stepping all over each other.

Which brings me to the reason I’m writing this: By leaving it to the ideologues to generate policy, we're sacrificing real discussion in favor of culture wars and name-calling. Instead, I think it’s crucial for rationalists & moderates to share orthogonal proposals, concepts, and ideas. The suggestions below can be taken individually or together. They mix and match positions that belong to the Libertarian, Democrat, and Republican parties, along with a bunch of wacky ideas that belong to no party at all.

I know these proposals are politically impossible. That’s almost the point. By losing the constraints of what’s feasible, by going well outside the Overton Window and just wondering “what if”… perhaps we can stop riling people up and can spark some real discussion.

Never doubt that a small group of thoughtful, committed citizens can change the world; indeed, it's the only thing that ever has.

If you have comments, ideas, or suggestions, I’d love to hear them. Please comment on this post or shoot me a DM (@haridigresses), or comment on it in this Notion doc (requires login) or in this Google doc (preserves anonymity).

Let's get into it.

Education

College isn’t for everyone, and we need to stop telling kids that it is.

Dramatically increase the intensity, challenge, and bar for K-12 education. Heavily support kids who can’t keep up with after-school lessons, but don’t hold back the rest of the class. There will always be a distribution of outcomes but that’s okay.

Most people have some kind of genius within them. There are many different kinds of intelligence: scientific and mathematical, sure… but art, music, hand-eye coordination, social, creative problem-solving, endurance. But this doesn’t mean that everyone’s equally smart. There are still distributions within each kind of intelligence. Invest more in finding and fueling this hidden potential, not in serving the lowest common denominator.

Focus on enabling and unleashing the top 5% of kids in each field.

• Generally, the top 1% of a field are more impactful to society in 50 years than the bottom 50% — Jonas Salk, Henry Ford, JP Morgan, General Patton, Michael Jordan, Ella Fitzgerald, each had a bigger impact than a thousand other “average” people in their fields, combined.

• Of course, some of that is luck, but it’s clearly possible to identify savants early on.

• If societal progress generally comes from the outliers, then every child should have the same basic foundations, but the gifted children should receive disproportionate academic investment.

Heavily invest in trade schools. Plumbers are likely to have a more secure professional life than most white-collar workers in the age of AI.

Stop talking about student loan forgiveness. Subsidizing the cost of something makes the price go up. If you don’t believe this, take an entry level economics course.

Fix the broken parts of the student loan system:

• Allow student loans to be discharged in bankruptcy after 7 or 10 years

• Go after predatory student loan lenders — just like we go after predatory practices in any other financial sub-sector. They currently have an unregulated oligopoly.

Institute pay for performance in education. The goal of the education system isn’t to “protect teachers”, it’s to educate and elevate students. The best teachers should make 2-5x the average because their work is massively impactful to society. Fire the bad teachers.

Disband teacher’s unions, solely because they will fight pay-for-performance tooth and nail.

(Both the pay for performance and disbanding unions points applies to all public sector organizations. There’s a slightly better argument for private sector unions to create leverage, but the government isn’t profit-maximizing the same way.)

Continue experimenting with charter schools, vouchers and other means to raise the bar in thoughtful ways. Experimentation should never be a political issue.

Institute similar outcome-oriented systems with colleges: every accredited institution should be required to publish post-graduation outcomes on a 0, 1, 3, 5, and 10 year horizon. It’s easy to crowdsource this with simple authentication of alumni email addresses.

Start taxing endowment funds in excess of $250K / enrolled student.

Civic Discourse

I’m pro “Woke 1.0” but anti “Woke 2.0”.

• The phrase “woke” originally referred to the fact that there are embedded biases in society, which… of course there are. This isn’t saying “everyone’s racist”, which is a silly cudgel. It’s simply saying we’re all subject to heuristics, and maybe we should be mindful of that. If you don’t recognize that… I’d recommend taking an entry-level psychology course.

• But that should not pervade every aspect of how we operate as a society. It shouldn’t turn into an us vs. them. It should be used to fuel guilt and resentment. It shouldn’t be used to cancel people — a trial-without-jury in the court of public opinion. We should find ways to build bridges, not burn them. This toxic culture where “if you’re not for me, you’re against me” is what is being characterized as “woke”, and the over-correction has undermined the original goals.

No safe spaces at universities. Life doesn’t have safe spaces. Resilience (within limits) makes us better people.

We should measure and encourage diversity of all kinds. This isn’t a dog-whistle, by the way; we should measure and improve demographic diversity as well. But if we listened blindly to “intersectionality,” then there’s only one acceptable hierarchy of diversity, which is silly. There are many ways by which people have overcome adversity in their life, not all of which are visible to the eye.

Encourage this not because some academic taught about right and wrong, but because in most cases in everyday life, breadth of experiences in a group results in better decisions and outcomes.

When seeking to improve diversity, work on top of the funnel, and avoid quota-based systems. Certainly never tie incentives to diversity outcomes, because this always results in unforeseen consequences.

Bring back the Fairness Doctrine, which encourages the discussion of opposing viewpoints.

Find ways to boost other incentives and business models besides just ads (which lead to dopamine, outrage, and clickbait) — be it mainstream media or social media.

Government

End gerrymandering — constitutionally limit voting districts to N edges.

Constitutionally outlaw money in politics. Citizens’ United is the most harmful thing to happen to our society in decades.

Switch to a true popular vote, or actual run the electoral college the way it was designed. The current system gives us none of the benefits of a representative democracy, with all the downsides and risks of a populist-driven system.

Constitutionally add an upper limit for age of elected officials. If someone has to be >35 to run for President, there’s no reason they shouldn’t be limited to <75.

Start passing more constitutional amendments. We haven’t passed a serious amendment in >50 years (no, the 27th Amendment doesn’t count).

The US Code is just like computer code. It needs a massive refactor. In the absence of this, any system of rules will gradually develop cobwebs and cruft.

Massively increase the budget for the US Digital Service. If you aren’t familiar with this agency, you should take a look: they just created a free tax filing system, so most people never have to pay TurboTax or H&R Block another dime. Not all heroes wear capes or uniforms.

No person who’s never run a business should every become FTC Commissioner.

Ban members of congress from trading stock. At the very least prohibit insider trading with a restricted list (like every other employee of any company that has material non-public information).

Institute term limits for members of congress.

Avoid adding regulation to make things easier. Even if you mandate “make this thing easier”, complying with those regulations always, always ends up making it harder.

If you want to disincentivize a behavior, don’t ban it. Just add red tape.

Speaking of, let’s please just build more damned housing.

Social Services

Stop subsidizing things that are seeing massive price increases (healthcare, education). Again, Econ 101.

Raise the retirement age (for social services) to at least 70. I know this stiffs people who have already contributed a lot into the system, but quality of life has massively improved and 70 isn’t what it used to be. It sucks, but we need to cut off the finger to save the hand.

Rewrite the tax code. Literally, throw it out and start over.

Encourage efficiency in healthcare through vertical integration. There are a handful of local healthcare networks that do this brilliantly (e.g., Kaiser). Seed more of these organizations.

Start a national public chargemaster for hospitals. If hospitals don’t participate, penalize them, but still allow patients to upload their bills (AI can now easily extract the line items and reverse engineer every hospital’s chargemaster if enough people are made aware of this resource).

(Actually, someone should probably build this as a public service project in a weekend.)

Run experiments with Universal Basic Income. We just don’t know enough here, but it’s possible that we should replace all social safety nets with just one check per month, tied to the person’s tax return from the prior year.

Stop “harm reduction” initiatives. This is simply attempting failed, unproven theories to help people who just need help (instead of enablers)… and along the way, massively reducing quality of life for the remaining 95%+ of the population.

Invest in mental health facilities and shelters. Once that’s done, there’s really no good reason not to enforce safety and sanity on the streets (crime, hard drug dealing & use).

End subsidies for things that are slowly killing us, like corn (which is mostly used to make cheap high-fructose corn syrup). Why is farming a more noble profession than being a car mechanic, or school bus driver, etc. — none of whom receive subsidies?

More ruthlessly prosecute things that are poisonous. PFAS / PFOA are forever chemicals which DuPot and 3M have been introducing into our water table with gross negligence, and should be sued out of existence. If we could do it for tobacco companies.

Centralize and streamline bureaucracy. There’s no reason every county should reinvent its own building codes. There should be a nationally developed and maintained set of “template” regulations — let’s say 10 or 20 types, some better suited to hot vs. cold weather, some suitable for hurricanes vs. earthquakes. Each county can adopt one for its weather, identity, and preferences. The same should apply to health inspections, business licensing, environmental regulations, etc., albeit perhaps a lot fewer than 10 or 20 for these.

Encourage superblocks, autonomous vehicles, public transit, greening of cities. Disincentivize ground level parking lots.

Economic & Tax Policy

This one requires a little chewing on: the US should maximize long-term gross profit in engineering the economy (to be clear, not operating profit):

• Gross profit = the revenue (taxes) generated by the country, less the marginal cost (social security, medicare, safety nets, etc.).

• Every other budget line item (defense, government agencies) — and even those line items of taxes and social services — should be aligned to maximizing the absolute gross profit.

• If a policy change would increase high-quality immigration (cough cough, H-1B, O-1), we should dramatically encourage those.

• If a policy change results in better quality of life and productivity for the population (e.g., preventative medicine) we should invest in those.

• If a measure increases the risk of a shrinking economy (and hence revenue), it should be avoided — e.g., a top marginal tax rate of 60%+ will result in capital flight. Regardless of what you think of the “morals” of taxation, this is just the logical, pragmatic approach to taxes.

Massively invest in building out nuclear energy, reduce dependence on everything else. PSA: solar and wind aren’t as “green” as you think, they require such a massive amount of load-balancing and environmentally-damaging rare earth metal mining that they’re of questionable impact to the environment. There’s a reasonable argument that they’re just an opiate for the masses, to make us all feel better that we’re being earth-friendly. (The same applies to ESG investing and recycling btw. Most “ESG” stocks aren’t, and most recycling goes into a landfill.)

Disband corrupt, kleptocratic organizations that keep the economy and society from working well. The underlying missions are still valid, but their operation has just become massively bureaucratic, and their methods constitute a bigger tax to society than any bill paid to the IRS. Just shut them down and start over:

• SEC

• FTC

• Federal Reserve

• Department of Education

• EPA

• VA

• CDC

• FDA

• …

On the other hand, there are many agencies which are generally known to be well-run (not first-hand information):

• USDA

• FDIC

• IRS (I’m going to get a lot of flak for this one, aren’t I)

• DARPA

• …

Continue to invest in defense spending. That’s an intentional choice of words; this is simply because our ability to project power and protect trade lanes makes us (and the rest of the world) more prosperous.

Ending on a fun one…

The principles the US was founded on included disbanding oligarchies, which generally has to do with wealth, not income.

It doesn’t make any sense that a person who has a $5,000 net worth but makes $100,000 / year is taxed the same that as someone who has a $5,000,000 net worth and also makes $100,000 / year.

If we instituted a progressive system, e.g.,

• <$100K net worth, 0%

• $100K - $1M net worth, taxed at 1%

• $1M - $10M net worth, taxed at 2%

• $10M - $100M net worth, taxed at 3%

• $100M - $1B net worth, taxed at 4%

• >$1B net worth, taxed at 5%

It should end up being roughly revenue neutral but much more incentive aligned and eliminating a lot of silly behavior.

• Right now, someone worth $500M probably generates 12% on their net worth anyway = $60M, and is paying probably 25-30% in taxes so $15-20M. A wealth tax system would tax that person at 3-4%, which is…. $15-20M.

• Top down, the math also checks out; US household net worth is ~$150T and corporate market cap is ~$50T. A ~3.5% weighted average tax rate is ~$7T, which is approximately the total of the US federal, state, and local budget.

It avoids dumb games like tax loss harvesting, timing sales for long term, or rushing to buy or sell things on December 31.

Do not tax illiquid assets in the “traditional” sense. This is the biggest problem with all wealth tax proposals to date. Instead, tax them in-kind. E.g., if a taxpayer holds 10,000 shares in a private company for 3 years, and my net worth is $250,000, $500,000, and $1,200,000 during those 3 tax years, then when I sell it, I would have to cough up 1% + 1% + 2% = 400 shares’ worth of proceeds when I sell it. If I sold 5,000 shares, then the government gets 4% of those proceeds and the remaining 5,000 shares continue to accrue a fraction to the government each passing year.

Philosophically, these illiquid assets also offer some substance to back up the US debt. In reality, the US debt is very much “collateralized” by the assets and receivables of the federal government (colloquially called the “full faith and credit” of the government, but this is really what it’s getting at). But when thinking more explicitly of “assets” which are owed to the government by its taxpayers, it’s more apparent why US Treasuries aren’t just backed by “faith” but rather by real assets.

Also philosophically — it will both be a fair & progressive tax system, while also making it clear that the wealthy are more than pulling their weight.

• The top 1% of income earners today pay (more than) their “fair share” of taxes, but because of the complexity introduced by a dozen different kinds of taxes, no one thinks that.

• By simplifying the system, it becomes unequivocally obvious that those with a $10K net worth pay $0 in taxes (in fact, have a negative tax rate due to social programs or possibly UBI)…

• … while the ultra-wealthy have 4-5% eating away at their wealth every year (obviously offset by returns on their wealth).

• All of this is as it should be; the wealthy should indeed pay more than their proportionate share.

• But we should call a spade a spade. Which one might optimistically believe would help with class warfare.

Sam Seaborn on the West Wing put it much more eloquently:

The top one percent of wage earners in this country pay for 22 percent of this country. Let’s not call them names while they’re doing it, is all I’m saying.

Corporations would be taxed similarly in kind — they would simply issue the government a certain number of shares each year tied to their market cap.

• For publicly listed companies, the government would have a selling program where a portion of these assets would be sold to cover expenditures, and the rest would continue to be held to cover social programs.

• Because of the massively long time horizon on which the government can operate, it can ride out market cycles.

If done correctly, the taxpayer should never have to think about it. It should just get settled in the background in the course of income and sales, just the way that income taxes are withheld.

If it sounds complex, just remember that we’ve set up far more complex systems (e.g., your brokerage account automatically deposits fractional shares for stock dividends; this isn’t any more complex).

Of course people and companies will try to game it and find loopholes, but… not any worse than the way income tax is gamed today.