Not literally. But the resemblance goes deeper than style. Both men run petrostates. Both have mistaken a temporary resource advantage for a permanent mandate. And both, I think, have overplayed their hand.

California isn’t a state in the normal sense. It’s a petrostate that exports software instead of oil. And like petrostates everywhere, its government has learned to extract wealth without performing, because the resource can’t leave.

Tech companies are geographically captive in a way other knowledge work isn’t. Network effects are brutal: the talent is here, the VCs are here, the acquirers are here. You can move your headquarters to Austin, but you’ll still need a Bay Area office. You can’t replicate an ecosystem with tax breaks.

When Chicago lost Citadel, it was a crisis. When companies threaten to leave New York, the governor negotiates. When California lost Oracle and Hewlett-Packard, it proposed wealth taxes and exit taxes. That’s not the behavior of a government competing for business. It’s the behavior of a government that knows the resource is captive.

The other petrostate prerequisite is no political competition. The House of Saud doesn’t hold elections. California technically does, but Republicans haven’t won statewide since 2006. The legislature is a Democratic supermajority. The real elections are primaries, where turnout is low and public employee unions write the checks. When you can’t lose power, you stop optimizing for voters and start optimizing for the interests that keep you in power.

To keep its citizens happy, the Saudis have a social contract. The royals extract, but citizens pay no income tax, gas costs 60 cents a gallon, and the government subsidizes housing, healthcare, and education.

California has no such bargain. Captive resource plus no political competition produces a specific pathology: enormous spending with terrible results. The state spends $26,000 per pupil on K-12 education, among the highest in the nation. It ranks 37th in outcomes. Only 35% of fourth graders are proficient in math.

The pattern repeats everywhere. Los Angeles spent $1.3 billion on homelessness in a single year. The homeless population went up. A highway patrol officer can retire at 50 with a $150,000 annual pension. The bullet train has consumed $15 billion without laying a single mile of track, its cost estimate ballooning to $128 billion. (For context, SpaceX has raised $12 billion across its entire 23-year history and rebuilt the American space program).

These aren’t failures. They’re only failures if you think the system is optimizing for residents. If it’s optimizing for wealth transfer to favored constituencies, retired workers, NGO executives, government contractors, the system is performing beautifully.

Every petrostate needs a legitimizing ideology. Saudi Arabia has religion: the monarchy derives authority from custodianship of the holy sites. California has progressivism.

You’re not being fleeced. You’re funding compassion.

The function is identical. Both make it socially ruinous to question the extraction. In Riyadh, you’re questioning God. In Sacramento, you’re questioning justice. Challenge the $128 billion bullet train and you’re against climate action. Ask why $1.3 billion didn’t reduce homelessness and you lack empathy. Wonder why $26,000 per pupil produces 37th-ranked schools and you must not care about children. The ideology doesn’t just justify the spending. It makes the spending impossible to audit.

The billionaires trickling to Miami aren’t fleeing taxes, exactly. They’re fleeing a government that treats them as a resource to mine, and wraps the pickaxe in virtue.

But petrostate models are fragile. They work only as long as the commodity price stays high.

Saudi Arabia needs $90 oil just to balance the budget. Oil hit $70 today. Their Q1 deficit hit $16 billion, more than half the forecast for the entire year, and the year’s barely started. Goldman Sachs projects the full-year number could reach $67 billion. NEOM, the $500 billion fantasy city? The Line has been scaled back from 170 kilometers to 2.4. The sovereign wealth fund suspended construction entirely. The Asian Winter Games they were supposed to host in 2029, indefinitely postponed. The dream is being quietly dismantled.

California’s commodity is capital gains. The state gets half its income tax from the top 1%, mostly stock sales. When tech booms and markets rise, the extraction model hums. But California has been running deficits even during the boom: $27 billion in 2023, $55 billion in 2024.

These are the good times and California is still underwater.

MBS is retreating. Scaling back NEOM, cutting spending, quietly accepting the math. He’s being humbled by the price of oil, and more importantly, he knows it. He’s always known where his power comes from.

Over the past year, $2 trillion has been erased from software stocks. When revenue slows, California doesn’t question the model. It squeezes harder. It thinks the money comes from good governance, not from harvesting network effects.

The well dries up eventually. It always does. The only question is whether you saw it coming. MBS did. Sacramento won’t.

On the night of February 25, 1634, a group of officers climbed the stairs of Eger Castle in western Bohemia. They were looking for their commander.

Albrecht von Wallenstein was the most powerful military entrepreneur in European history. As supreme general of the Holy Roman Emperor’s forces, he had built an army of over 100,000 men, financed largely from his own fortune. He controlled more territory than most kings. And for three years, he had been running a side game that made Qatar look like an amateur.

Wallenstein negotiated simultaneously with the Emperor, the Protestant princes, the Swedish crown, and the French. He took money and made promises to all of them. He played each faction’s desperation against the others, extracting land, titles, and wealth at every turn. His logic was simple: he was too important to lose. Every side needed his army. Every side needed his intelligence. No one could afford to move against the man who held the balance of power in the Thirty Years’ War.

He was wrong.

The Emperor decided that the most indispensable man in Europe was actually the most dangerous. Ferdinand II issued a secret order. That night in Eger, Wallenstein’s own officers found him in his chambers, unarmed and in his nightshirt. Captain Walter Devereux drove a pike through his chest.

Four hundred years later, Qatar was running the same play at a national scale.

They hosted Al Udeid Air Base, CENTCOM’s forward headquarters and home to over 10,000 American troops. They funneled hundreds of millions of dollars to Hamas and sheltered its political leadership in government compounds. They kept back-channels open to Tehran. They brokered negotiations between parties that wouldn’t speak to each other. They were the indispensable middleman: too connected, too useful, too central to hit.

Then every side hit them anyway. Iran fired missiles at Al Udeid during the Twelve-Day War. Israeli jets struck a residential compound in central Doha, killing six Hamas leaders Qatar had been financing and protecting. In January 2026, Qatar told Washington it would not allow its bases to be used for strikes against Iran. The hedge again. Stay in the middle. Don’t provoke.

Iran launched 66 missiles at Qatar on February 28th anyway. By March 2nd, Qatar was shooting down Iranian Su-24 fighter jets, and QatarEnergy, the world’s largest LNG producer, had halted all production. European gas prices surged 50%.

Struck by Israel for funding its enemy. Struck by Iran for hosting America’s base. The indispensable middleman discovered what Wallenstein discovered: when you play every side, you give every side a reason to hit you.

Why?

Not because Qatar was weak. Qatar is rich, armed, and strategically located. They got hit because they were illegible.

No one knew what Qatar actually was.

An American ally? They hosted CENTCOM. An Iranian partner? They kept the back-channels. A Hamas sponsor? They wrote the checks. Qatar was all of these things simultaneously, which meant that every committed player in the region looked at Qatar and saw a variable that needed to be resolved.

This is the pattern. It’s not really about Qatar. MBS made the same calculation: refuse the Abraham Accords, refuse to let Washington use Saudi territory, keep Iran at arm’s length, protect Vision 2030 at all costs. On March 2nd, Iranian drones hit Ras Tanura, Saudi Arabia’s largest oil refinery. Aramco shut down 550,000 barrels per day. The thing MBS was hedging to protect is now under direct threat precisely because he hedged. Iran looked at Saudi Arabia and saw the same thing the Emperor saw in Wallenstein: a player too entangled with everyone to be trusted by anyone.

Starmer did it too. He blocked the US from using Diego Garcia for Iran strikes. The stated reason was international law. The real reason was that Labour’s coalition couldn’t survive being seen supporting strikes on a Muslim country. It was a domestic hedge dressed up as legal principle. Trump said it was unlike anything that had “happened between our countries before” and withdrew support for the Chagos Islands deal. Then Iran hit RAF Akrotiri in Cyprus. Within 48 hours, Starmer reversed course. The UK ended up exactly where they would have been anyway, but with a damaged alliance and nothing to show for the hesitation.

Each one discovered that in a fracturing world, complexity isn’t protection. It’s a target. Committed players don’t tolerate ambiguity. They resolve it.

Iran hit every Gulf state last weekend except one: Oman.

Oman has mediated between the US and Iran for years. It hosted the nuclear talks. It keeps channels open to Tehran. On paper, it looks like another both-sides player. But Oman never played both sides. It picked one role: honest broker. And it never deviated. It didn’t host American military bases while courting Tehran. It didn’t fund armed groups while brokering ceasefires. It was transparent about what it was and what it wasn’t. Iran spared Oman not because Oman was neutral, but because Oman was legible. There was no variable to resolve.

The UAE tells the same story from the opposite direction. They signed the Abraham Accords in 2020. Saudi state media branded them an “Israeli Trojan horse.” Across the Arab world, they were called traitors. They held the line. Never recalled their ambassador from Israel. Never canceled flights. Never froze cooperation.

When 165 ballistic missiles and 541 drones came screaming across the Gulf last weekend, the UAE absorbed the blow, because picking a side had given them access to things hedging never could: Israeli-built Barak and SPYDER air defense systems, fully integrated intelligence sharing with American and Israeli services, years of joint exercises through CENTCOM. The Abraham Accords weren’t a photo op. They were a defense architecture.

Oman was spared because everyone could read it. The UAE leveraged US and Israeli defense partnerships to intercept 95% of incoming missiles. Qatar, Saudi Arabia, and the UK unfortunately paid (and will continue to pay) the price for strategic ambiguity.

The world that tolerated ambiguity is gone. From 1991 to roughly 2020, you could host an American base and a Hamas bureau in the same city. You could buy Chinese 5G equipment and American fighter jets. One power underwrote global security, and the cost of being illegible was low. The contradictions didn’t collapse because the unipolar order held them in suspension.

That order is over. The system is fracturing. And in a fracturing system, every uncommitted player becomes a variable that committed players need to resolve. Wallenstein learned this in 1634. Qatar, Saudi Arabia, and the UK are learning it now.

The UAE understood something crucial: commitment has a price. You pay it upfront, in political capital, in domestic backlash, in being called a traitor by your neighbors. But when the missiles come, you have allies, architecture, and something to stand on.

There is nothing more expensive than not picking a side.

Americans are furious about healthcare. Insurance company CEOs are getting gunned down in Manhattan. The government just ended the longest shutdown in American history fighting over subsidies.

There is a solution. It isn’t single-payer. It isn’t more subsidies. But it requires understanding why the system is broken in the first place.

We built two health insurance markets in America. Same insurance companies. Same hospitals. Same country. One works fine. The other requires $350 billion in annual life support.

160 million Americans get insurance through their employers. Their premiums are stable. Their coverage is decent.

24 million Americans buy insurance on the individual market. Their premiums are astronomical. Their coverage is terrible. Without government subsidies, it would collapse tomorrow.

Same insurers. Same doctors. Same drugs. The difference is who’s in the pool.

Why Pools Matter

Employer health insurance works because a company’s workforce looks roughly like America: young and old, healthy and sick, desk workers and warehouse staff. Insurers can price this mix. The math works.

The individual market has a different pool. It’s where you end up if you don’t have employer coverage: self-employed, between jobs, early retirees waiting for Medicare. This pool skews old and sick.

If you’re young and healthy and your employer offers insurance, you take it. The people left buying individual coverage are disproportionately the people who really need it.

The Death Spiral

Economists call this adverse selection. I call it a death spiral.

When your pool is mostly sick people, premiums are high. When premiums are high, healthy people opt out—they’d rather take the risk than pay $400/month for coverage they probably won’t use. When healthy people leave, the pool gets even sicker. Premiums rise again. More healthy people leave.

Repeat until collapse.

The Affordable Care Act bet that subsidies could break this cycle. Make coverage artificially affordable, and healthy people will join. Balance the pool. Stabilize premiums.

It didn’t work.

A healthy 30-year-old freelance designer in Austin looks at the exchanges today. Even with subsidies, she’s paying $300/month for a plan with a $6,000 deductible. That’s $3,600 per year plus $6,000 before the insurance actually kicks in. For someone who goes to the doctor once a year.

There’s no penalty for skipping coverage anymore. She does the math. She opts out.

Multiply her by millions of people, and you understand why 90% of individual market enrollees need subsidies to afford coverage. When nine out of ten customers need government assistance to buy your product, you don’t have a market. You have welfare wearing a market’s clothes.

Fix the Pool, Fix the Problem

You can’t solve pool problems with money. You can only solve them with different pools.

Imagine if tomorrow, every American bought insurance on the same market. Young tech workers in San Francisco. Retirees in Florida. Factory workers in Ohio. One giant pool that looks like America.

The death spiral would break. Adverse selection would disappear. The individual market would become as stable as employer insurance, because it would be employer insurance—just portable.

ICHRA

Individual Coverage Health Reimbursement Arrangements were created in 2019. Instead of your employer picking a health plan and offering it to everyone, they give you tax-free money to buy your own coverage on the individual market. Same tax treatment as traditional employer insurance. Same total cost. But you choose the plan, and it’s yours—it doesn’t disappear when you change jobs.

About a million people use ICHRAs today. Growing 52% annually among small businesses. Employers like it because it’s simpler. Employees like it because they have choice.

Now imagine making it mandatory.

Tomorrow, every employer stops offering health insurance directly. Instead, they contribute what they were already spending—around $8,500 for individuals, $30,000 for families—to their employees’ ICHRAs. Employees use that money to buy coverage on the individual market.

The ACA pool goes from 24 million to 184 million. Young, healthy workers flood in alongside older, sicker enrollees. Risk spreads across the entire working population. The pool transforms from “sick people who can’t get employer coverage” to “Americans.”

When your risk pool looks like America, you don’t need $350 billion in subsidies. You need maybe $50 billion for targeted assistance to people who genuinely can’t afford coverage—not 90% of enrollees.

Kill the Exchanges

This requires killing the ACA exchanges as they exist today.

Not reforming them. Not improving them. Ending the subsidies that keep a broken pool on life support and replacing them with something that actually works.

The exchanges were designed for a pool that doesn’t exist: healthy young people voluntarily buying coverage alongside sick older people. That pool never materialized because the incentives are wrong. No amount of subsidy fixes bad incentives.

Mandatory ICHRA doesn’t need subsidies to function. When the pool looks like America, premiums stabilize on their own. You only need targeted assistance for people below the poverty line—not 90% of enrollees.

The other thing that changes: plan availability.

Right now, insurers offer limited plans on the exchanges because the pool is toxic. Why design good products for a market full of expensive patients?

When 184 million Americans are buying on the individual market, insurers will compete for them the way they compete for employer contracts today. More plans. Better networks. Actual choice. The same insurers who offer great employer coverage will offer great individual coverage—because it’s the same pool.

The Leash

Your health insurance is a leash. It ties you to your job. If you want to start a company, go freelance, or take a year off, you have to figure out the insurance problem. For people with families or health conditions, this can be paralyzing.

Employer-sponsored insurance made sense in 1943, when it was a wartime workaround for wage controls. It doesn’t make sense now. Your health coverage shouldn’t depend on where you work any more than your car insurance depends on where you work.

Portable insurance means you can take risks. Start companies. Change careers. Move to cheaper cities. The insurance follows you, because it was always yours.

Permanent Insurance, Aligned Incentives

When your insurance is portable and permanent, the insurance company’s incentives change. Right now, they’re optimizing for the three years you’ll be on their plan before you switch jobs. Why invest in preventive care when some other insurer will reap the benefits?

If you’re insured by the same company for decades, suddenly they care about keeping you healthy at 35 so they don’t pay for your heart attack at 55. The economics flip from “minimize this quarter’s costs” to “minimize lifetime costs.” That’s when you start getting calls reminding you to schedule your colonoscopy.

The Choice

The American healthcare system isn’t broken because we spend too little. We spend more per capita than any country on Earth. It’s broken because we built two markets when we should have built one.

For fifteen years, we’ve tried to fix a poisoned pool by pouring money into it. $350 billion a year. It hasn’t worked. It was never going to work. You can’t fix adverse selection with subsidies. You can only fix it by changing who’s in the pool.

The left wants universal coverage. The right wants market-based solutions. This is both. Everyone gets insurance. Everyone buys it on a real market. The pool works because it includes everyone.

The fix is three steps:

End the ACA exchanges. End the subsidies. Mandate ICHRA.

That’s it. No new bureaucracies. No government takeover. Just one big pool that looks like America, where insurers compete for your business and your coverage follows you for life.

We’ve spent fifteen years and trillions of dollars covering up the failure of the ACA. It’s time to stop.

Trump is threatening to send the National Guard to Chicago. Marc Benioff asked for a deployment to San Francisco, then quickly backtracked.

Before we get to Trump and federalism, answer a simpler question: Are US cities getting more dangerous?

The official answer is clear:

• San Francisco Mayor Daniel Lurie: “Crime down nearly 30%, car break-ins at 22-year lows, homicides at 70-year lows.”

• Illinois Governor JB Pritzker: “Murders down 32%... shootings down 37%... robberies declined 34%.”

• California Governor Gavin Newsom: “Overall violent crime down 12.5%... a 34% decline in robberies.”

So why did your car get broken into last month? Why are your neighbors installing Ring cameras? Why does no one go downtown anymore?

If we’re at “22-year lows,” why doesn’t anyone feel safe leaving the office at night?

Someone is lying—or more precisely, someone has figured out how to make the numbers lie for them.

The Two Honest Crimes

Most crime statistics are useless.

Assault? Depends who reports it. Burglary? Only if victims bother filing. Shoplifting? Retailers stopped calling police entirely- why bother when prosecutors won’t file charges under $950?

Reporting rates collapsed. Definitions shifted. What counts as “crime” is increasingly a political choice.

But two crimes can’t be faked: homicide and auto theft.

Homicide is the proxy for social cohesion. Bodies demand autopsies. Medical examiners don’t answer to mayors. Families demand justice. You can’t hide a murder in a spreadsheet. When homicide rises, social fabric is tearing: neighbors turning on neighbors, disputes escalating to violence. When it falls, communities are stable. People have hope. Trust exists.

Auto theft is the proxy for institutional deterrence. Stolen cars trigger insurance claims that require police reports—paper trails politicians can’t erase. More importantly, auto theft isn’t about desperation. Nobody steals a car because they’re hungry. It’s pure calculation: (Probability of getting caught) × (Severity of punishment) = Risk.

When auto theft explodes, the criminal justice system has stopped functioning. High auto theft means criminals calculate the risk as zero. When consequences disappear for property crime, you’re advertising that the system won’t protect anything.

Together, these metrics tell you everything: Homicide reveals whether your neighbors might hurt you. Auto theft reveals whether anyone will bother stopping property crime.

What the Numbers Actually Say

Let’s start with what everyone agrees on: homicide is genuinely falling. The FBI reports murder down 14.9% in 2024. These aren’t statistical tricks: fewer people are killing each other. That’s real progress.

This is the number politicians want you to focus on. And they’re right to celebrate it—falling homicide means something fundamental is working. The social unrest of the pandemic is over. Communities aren’t tearing apart. Social bonds remain intact. People aren’t desperate enough to kill.

But here’s where the lying starts.

When politicians claim “crime is falling,” they’re obscuring the problem in two ways.

First, they compare to pandemic highs. Crime across the board spiked in 2020-2022, then fell back. But “down from a historic spike” isn’t the same as “safe.” Most cities remain well above 2019 levels; they just hope you forgot what normal used to feel like.

Second, they use falling homicide rates to hide rising property crime. When pressed on auto theft, they pivot: “But overall crime is down!” They’re betting you won’t ask which crimes are down and which are up.

Statistical sleight of hand.

Here’s what the data actually shows: Rank district attorneys by how progressive their platform is—the list matches per capita auto theft rates almost perfectly. Every city with auto theft rates above twice the national average has a DA who ran explicitly on decarceration.

The divergence proves the manipulation. If cities were genuinely getting safer, both numbers would move together. Social breakdown drives both murders and property crime. Social stability reduces both.

Instead we see: social stability (falling homicide) plus institutional collapse (exploding auto theft).

That combination isn’t random. It reveals something specific: these cities stopped enforcing property crime laws. This isn’t incompetence; it’s deliberate policy producing exactly the results progressive prosecutors promised: fewer people in jail, more property crime on the streets.

The New York Proof

New York City is the control group that destroys the excuses of progressive politicians.

Compare New York to Chicago. Similar size. Similar density. Similar diversity. Both progressive. Both expensive. Both Democratic strongholds for decades.

NYC’s auto theft rate: 175 per 100,000, below the national average of 250.

Chicago’s: 870 per 100,000, 350% above the national average.

Chicago residents are 3.1 times more likely to have their car stolen than New Yorkers.

That’s not a rounding error. That’s policy.

The difference isn’t policing budgets. It isn’t poverty or inequality. It’s consequences. New York maintained prosecution. District attorneys continued filing charges. Courts processed cases. When you steal a car in New York, you get arrested, prosecuted, and sentenced. The system still works.

And New York does this while remaining progressive on everything else. Sanctuary city policies. Substantial social services. Housing regulations. Criminal justice reform for violent offenders.

Turns out cities don’t need authoritarian rule to maintain order. They just need to enforce their laws. New York proves you can be progressive AND prosecute car thieves. The choice progressive governors pretend doesn’t exist, already exists in America’s largest city.

The Pritzker Problem

Governor Pritzker’s response to Trump reveals the core hypocrisy.

Trump’s threat to send federal troops violates federalism. Pritzker is right to oppose it.

But his outrage rings hollow: Pritzker controls the Illinois National Guard. Chicago’s murder rate is 500% above the national average. Auto theft is 350% above. He could deploy state forces tomorrow.

He won’t. Because that would require admitting there’s a problem. Pritzker won’t even acknowledge Chicago’s crime crisis, he just cites falling homicides while ignoring that both murders and auto theft remain well above 2019 levels. He hopes you don’t ask about your stolen car, or remember what safe used to feel like.

Ed Koch didn’t have this problem. In the 1970s, he ran for mayor calling New York “plagued by filth, crime, bankruptcy, and racial tensions.” He acknowledged the crisis, then fixed it. He reformed policing to work, not out of existence. Police presence increased. Criminals got prosecuted. Social services expanded. He didn’t pretend you had to choose.

Pritzker can’t claim New York’s approach is impossible, it exists. He can’t claim prosecution abandons progressive values, New York proves otherwise. He can’t claim density makes it unworkable, New York is denser.

So instead: constitutional theater. Passionate speeches about federalism while refusing to use his own Guard. The working-class nurse misses shifts because her car was stolen. Politicians debate federalism on TV. Workers lose their livelihoods over a lie.

By refusing to deploy state resources, Pritzker creates the vacuum Trump wants to fill. The choice isn’t Trump’s troops or nothing—it’s Trump’s troops or Pritzker’s Guard.

When you refuse to govern, you invite someone else to govern for you.

The Choice

People will tolerate inequality. Bureaucracy. Bad schools. High taxes.

They won’t tolerate chaos. They won’t forgive being lied to about it.

This is how republics die: state governments fail to govern, lie about the results, then cry tyranny when someone else steps in.

Progressive governors face a choice they pretend doesn’t exist: Make your cities safe, or watch people invite federal power permanently.

Bodies and stolen cars don’t lie. Politicians do.

When did America's most important research move from universities to corporations? And more interestingly—why didn't anyone notice?

Something strange happened over the last 30 years. MIT still exists. Stanford still has labs. The NIH budget keeps growing. But when you look at where breakthrough research actually happens—from AI to antibiotics, from quantum computing to cancer drugs—it's coming from companies, not universities.

We didn't plan this. Nobody voted for it. It just happened.

Pfizer runs more advanced biotech research than any university. Google solved protein folding. Microsoft is building AGI. Moderna created a COVID vaccine in 48 hours. Even Walmart runs more sophisticated logistics research than academic operations research departments.

The privatization of American research isn't coming—it's complete.

Most people see this as a problem to solve. They're having the wrong debate. They're arguing about whether corporations should do research. But that ship has sailed. The question now is: how do we point this massive private research apparatus at problems that don't have natural markets?

The answer is surprisingly simple. But to understand it, we need to first understand how we got here.

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The Great Migration

In 1970, if you were a brilliant physicist, you probably worked at a university or government lab. By 2024, you almost certainly work for a corporation. What happened?

The simple answer is money. But that's not the whole story. The real answer is that companies became better at research than universities. [1]

This sounds backwards. Universities are supposed to be about pure knowledge. Companies are supposed to be about profit. But somewhere along the way, those roles flipped.

Universities optimized for publication counts. Companies optimized for breakthroughs. Universities chased grant money. Companies chased solutions. Universities built bureaucracies. Companies built labs.

The migration was gradual, then sudden. First it was just the applied researchers. Then the theorists. Then even the pure mathematicians. Google now employs more top machine learning researchers than any university. Microsoft has more quantum physicists than most national labs.

But here's the interesting part: this might actually be good.

Companies have something universities lost: urgency. When Microsoft funds fusion research, they want fusion to work. When Pfizer develops a drug, they need it to actually cure people. The feedback loop is tighter. The results matter.

There's just one problem.

Markets Point in the Wrong Direction

Markets are optimization functions. They're incredibly good at finding solutions—but only to problems that make money.

This works great for smartphones and social networks. It works terribly for antibiotics and pandemic preparedness.

Consider antibiotics. By 2050, antibiotic resistance will kill 10 million people annually. We know how to develop new antibiotics. Pharma companies have the labs, the talent, the capability. They just don't have a reason.

The math is brutal. Developing a new antibiotic costs $2 billion. But good antibiotics are used sparingly to prevent resistance. Your blockbuster drug that saves humanity generates maybe $100 million in revenue.

Spend $2 billion to make $100 million? That's not a business model. That's charity.

So pharma companies work on what pays: chronic conditions, lifestyle drugs, expensive cancer treatments. Not because they're evil. Because they're rational. [2]

The same logic applies everywhere. Energy companies won't develop grid-scale storage that makes their existing assets worthless. Tech companies won't build AGI safety solutions that slow down AGI development. Nobody will build pandemic preparedness systems that sit idle between pandemics.

Markets fail when the benefit is public but the cost is private. And for civilization's biggest problems, the benefit is almost always public.

The $6.5 Million Miracle

In 2004, DARPA did something strange. Instead of funding research into autonomous vehicles, they offered $1 million to anyone who could build one.

The research community thought this was insane. Autonomous vehicles required breakthroughs in computer vision, sensor fusion, path planning, and machine learning. You can't just offer a prize and expect magic to happen.

106 teams disagreed.

What happened next was beautiful. Every team failed. Carnegie Mellon's heavily funded effort made it 7 miles before driving off a cliff. Caltech's entry caught fire. One motorcycle team's bike fell over at the starting line.

But here's what made it beautiful: every failure was public. No NDAs. No trade secrets. No patents. Just 106 different approaches, failing in 106 different ways, where everyone could see.

The learning was exponential. Teams could see exactly what didn't work and why. The computer vision team learned from the sensor fusion team's mistakes. The path planning team learned from the machine learning team's failures.

By 2005, five teams finished the course. By 2007, six teams were navigating urban traffic.

Then something predictable happened: the winners became founders. Sebastian Thrun, who led Stanford's winning team, started Google's self-driving project—now Waymo, worth >$50 billion. Chris Urmson, who won with Carnegie Mellon, later founded Aurora ($13 billion). Other Challenge veterans launched Cruise (acquired by GM for $1 billion), Argo AI (Ford/VW, $7.4 billion), and Nuro ($8.6 billion).

Venture Capitalists had watched the Urban Challenge from the audience. Within months, they were writing checks. The entire autonomous vehicle industry—every major player—traces back to those desert failures.

Total DARPA investment: $6.5 million. Value of self-driving car industry: $500+ billion. ROI: 76,923x.

But the real lesson wasn't about self-driving cars.

Buying Results, Not Research

DARPA discovered something profound: you get what you pay for. If you pay for research, you get research. If you pay for results, you get results.

This sounds obvious. But somehow we've built a $55 billion grant system that does the opposite. The NIH doesn't pay you to cure cancer. They pay you to study it. [3]

Think about the incentives. If you're a grant-funded researcher, success means getting more grants. The way to get more grants is to publish papers. The way to publish papers is to work on problems that are interesting but not too hard. Solving the problem completely would actually be bad—then you couldn't get grants to study it anymore.

This created a system that optimizes for the wrong thing. When Amgen tried to reproduce 53 "landmark" cancer studies, only 11% worked. Bayer found similar results—just 25% of published findings could be replicated. We're spending $55 billion annually on research that's 75% noise. [4]

Prizes flip this entirely. You don't get paid to try. You get paid to succeed. No publications, no citations, no committees. Just results.

This changes everything about who can participate. With grants, you need the right credentials, the right institution, the right connections. You need to know how to write a grant proposal—a skill completely unrelated to solving problems.

With prizes, none of that matters. A garage inventor can compete with MIT. A startup can beat a national lab. Pharma companies can dust off shelved research. Anyone with an idea can try.

The payment structure maintains rigor: 10% for proof of concept, 30% for demonstration, 60% for full deployment. You get funding to attempt the solution, but the big payout requires actual success. This filters out cranks while enabling serious attempts from unexpected places.

When Stanford's team entered the DARPA challenge, they weren't the best funded or the most credentialed. They won because they learned fastest. That's what prizes select for: learning speed, not pedigree.

The Two Problems Worth Solving

Once you understand that we've privatized research and that prizes can direct it, the question becomes: what should we buy?

There are two categories of problems worth creating markets for:

Type 1: Acceleration Prizes These are technologies that markets will eventually develop, but too slowly. Self-driving cars would have arrived by 2040. DARPA pulled them forward to 2025. That 15-year acceleration will save hundreds of thousands of lives.

Type 2: Existence Prizes These are breakthroughs markets will never develop because the economics are permanently broken. Antibiotics. Pandemic vaccines for unknown diseases. Carbon capture. The science is possible. The business model isn't.

The key insight is that you don't need to fund the research. You just need to create the market. Companies will handle the rest.

Why America Can Do This and China Can't

Prizes aren't just another funding mechanism. They're free markets for research.

Think about what we've been doing with grants: centralized allocation. Government bureaucrats decide who gets money, what approaches to try, which institutions deserve funding. It's Soviet planning with American characteristics.

China should be great at this. Central planning is their entire model. But they're losing to our accidental free market—where private companies compete to solve moonshot problems.

Prizes make this explicit. Instead of government picking winners, it just defines winning. Then it steps away and lets the market work.

This is our systemic advantage. Free markets are the greatest information processing system ever invented. Millions of independent actors, trying different approaches, failing in different ways, learning from each other. No central planner, however brilliant, can match this.

When you offer a prize, you're admitting profound ignorance: "We don't know how to solve this problem. We don't know who can solve it. We don't know what approach will work." You're crowdsourcing intelligence to the entire market.

Imagine Xi Jinping announcing: "We don't know how to build quantum internet, so here's $5 billion for whoever figures it out. A garage startup in Shenzhen might beat Huawei. We're fine with that."

It's systemically impossible. Their entire model is built on the state knowing best, picking winners, controlling outcomes. They can copy our technology. They can't copy our market structure.

When Stanford's scrappy team beat GM in the DARPA challenge, we celebrated. That's not a bug—it's the feature. The ability for nobody to beat somebody, for chaos to beat planning, for markets to beat ministers.

This is 21st century government done right: Define problems. Create incentives. Get out of the way. Let free markets do what they do best—solve problems through competition.

We've been trying to out-plan China's planners. That's fighting on their terms. Prizes let us fight on ours.

The Implementation Is Obvious

We're already spending $55 billion annually on research grants. Cancel all grants and convert them to prizes. Pay for cures, not conferences.

Start with the obvious ones:

• New antibiotic classes: $10 billion each

• Pandemic vaccine platform (any virus, 100 days): $20 billion

• Carbon capture at scale: $25 billion

Structure them intelligently. 10% for proof of concept, 30% for demonstration, 60% for deployment. Make them open to anyone—garage startup or Google.

But here's what's elegant: we don't need to figure out all the prizes. Once you establish the principle, problems will reveal themselves. Markets are good at that.

The Real Insight

Last week I wrote about how America's tech monopolies became our research labs. Readers panicked, thinking I was defending monopolies.

They missed the point. I wasn't defending anything. I was describing reality.

We've built the most powerful research engine in human history. It just happens to be distributed across corporate balance sheets instead of university endowments. Google has $170 billion in cash. Apple has $165 billion. Pfizer has $44 billion.

Fighting this is like fighting gravity. The smart move is to use it.

Prizes don't replace corporate R&D. They multiply it. When there's a $10 billion antibiotic prize, watch Pfizer dust off shelved research. When there's a $50 billion AGI safety prize, watch Google's deployment timeline suddenly include safety stops.

We keep trying to rebuild the 20th century research system. But we've already built something better. We just need to point it at the right problems.

The future belongs to whoever figures out how to unleash innovation, not control it. We've already done the hard part—building organizations that can tackle impossible problems.

Now we just need to make the impossible profitable.

[1] I'm using "better" in a specific sense here: more likely to produce usable breakthroughs. Universities are still better at producing papers.

[2] This is why the "ESG" movement mostly fails. You can't make companies act against their interests. You can only change what's in their interest.

[3] Yes, some grants have milestone payments. But the core metric is still publications, not solutions.

[4] This is also why prizes are more transparent than grants. When someone wins a prize, you can see exactly what they built. When someone gets a grant, you can only see what they promised to try.

Something unprecedented is happening in America's AI race with China. Unlike the Apollo program, which cost taxpayers $260 billion in today's dollars, our nation's AI efforts have been privatized.

The race for artificial general intelligence, potentially the most consequential technology since nuclear weapons, isn't being led by DARPA or government labs. Microsoft alone spent more on R&D last year ($29.51 billion) than the entire U.S. National Science Foundation ($9.806 billion). The most important technological race since the Cold War is being bankrolled not by Congress, but by Excel subscriptions and search ads.

This isn't a theoretical concern or future trend. Public research has collapsed. Universities have become paper mills. Government labs can't compete for talent. The transition happened gradually, but the end result is clear: private companies now drive America’s most important research.

This forces an uncomfortable truth: The same tech giants that Congress investigates for antitrust violations are the ones outspending our government on frontier research by orders of magnitude. Breaking up Google might get you better ad rates, but it would also destroy the research capacity that's advancing AI faster than any government lab.

In our technological competition with China, America's edge depends on recognizing this reality: Sometimes market dominance serves the public interest—not despite concentrated power, but because of it.

The Golden Age of Government Innovation: When America Dreamed Big

In 1962, President Kennedy stood at Rice University and declared that America would put a man on the moon "not because it is easy, but because it is hard." The audience didn't laugh. They didn't tweet skeptical memes. They believed him.

What followed was the greatest government funded technological achievement in human history. We didn't just reach the moon—we revolutionized human capability in the process.

From 1940 to 1970, government research spending grew nearly twentyfold. DARPA, NASA, and federally-funded university labs weren't just agencies—they were cathedrals of innovation, places where decade-long research horizons weren't just tolerated but expected.

The Space Race was the ultimate expression of government-driven innovation. When Sputnik beeped overhead, Americans understood the stakes viscerally. This wasn't about profits or patents—it was about survival. Congress opened the checkbook. Scientists got blank permits. A nation unified behind a single technological mission.

The technological spillovers were staggering: GPS navigation? Created to guide spacecraft. Solar panels? Developed to power satellites. Modern semiconductors? Miniaturized for rocket guidance systems. The early internet? Born from DARPA's need for resilient military communications.

Each breakthrough sparked cascade effects that transformed the economy. GPS enabled everything from precision agriculture to Uber. DARPA's network experiments evolved into the trillion-dollar internet economy. Government-funded semiconductor research at Stanford seeded Silicon Valley itself.

The golden age of government innovation didn't end because the model failed. It ended because we stopped believing in shared national purpose. Federal R&D spending peaked at 1.9% of GDP in 1964, falling to just 0.6% by 2019. As public funding retreated, something had to fill the void.

The Corporate Research Cycle: From Bell Labs to Google Brain

What's fascinating is that while government innovation was booming in the mid-20th century, another powerful innovation model was quietly developing in parallel – one that would eventually take center stage. This wasn't the startup ecosystem we celebrate today. It was something much more surprising: research funded by market-dominant corporations.

The story of America's innovation evolution isn't just about government retreat. It's about a torch being passed – first from government labs to corporate research giants, then from old-school monopolies to today's tech platforms. This cycle reveals an uncomfortable truth: breakthrough innovation has always depended on concentrated power and resources, whether wielded by nations or corporations.

Bell Labs: When Monopolies Invented the Future

The year was 1947, and Bell Labs had just invented the transistor, the foundation of all modern computing. The team celebrated by... returning to their other world-changing projects. They were also busy inventing cellular technology, solar cells, and information theory.

This wasn't a startup racing to an IPO. It wasn't a government moon-shot. It was AT&T's research arm, funded by telephone monopoly profits.

Bell Labs wasn't just a research center—it was a monument to what market dominance enables. AT&T's telephone monopoly generated such massive profits that they could employ 1,200 PhDs to explore whatever interested them. No quarterly earnings pressure. No venture capital timelines. Just pure research.

The results were staggering:

• The transistor (basis of all computing)

• Unix operating system

• C programming language

• Information theory

• Fiber optics

• Solar cells

• Cell phone technology

• Nine Nobel Prizes

Meanwhile, across the country, Xerox's PARC facility was performing similar magic. Protected by Xerox's dominant position in copying, PARC invented:

• The graphical user interface (GUI)

• The mouse

• Ethernet networking

• Object-oriented programming

• Laser printing

In 1956, regulators struck a historic bargain with AT&T: Keep your monopoly, but run Bell Labs for the public good. They understood something we've forgotten: Sometimes consumer benefit comes from innovation, not just lower prices.

The 1984 breakup of AT&T is usually celebrated as a win for competition. Phone calls got cheaper. Long-distance rates plummeted. But Bell Labs died. The new AT&T couldn't afford to fund basic research. The monopoly profits that powered innovation vanished, depriving humanity of unknown future advancements.

Fast Forward: When Tech Giants Became Nation-States

Now fast forward to 2024. We face another existential technology race, this time against China's AI capabilities. The stakes are just as high as the Space Race. The technological gap is just as daunting.

But here's the plot twist: This time, it's not NASA or DARPA leading the charge. It's OpenAI, Anthropic, xAI and Google DeepMind. The most important technological race since the Cold War is being bankrolled not by Congress, but by Microsoft, Amazon, Sequoia and Nvidia.

This isn't how it worked last time. Imagine if in 1962, Kennedy had announced: "We choose to go to the moon... and Boeing will handle everything." The nation would have been baffled.

Yet today, we take it for granted that private companies are driving humanity's next great leap. The government's role? Mostly writing stern letters about AI safety.

Let's talk about scale:

• Microsoft will spend more on AI research and infrastructure in 2024 than NASA's entire budget

• Google maintains quantum computing facilities that make government labs look obsolete

• Meta's AI research team is larger than most university computer science departments

• Apple spent more developing its own chips than the entire Apollo 11 mission cost (adjusted for inflation)

This isn't corporate R&D as we once understood it. This is nation-state scale investment by companies with GDP-sized revenues.

The Return of the Monopoly Innovation Machine

We're witnessing history repeat itself, but with a critical twist. Like Bell Labs before them, today's tech giants have created the conditions for breakthrough innovation through three key elements:

1. Enormous, sustained profits

• Google's search dominance funds DeepMind and quantum computing labs

• Microsoft's cloud and software revenue bankrolls OpenAI and massive AI infrastructure

• Apple's premium hardware margins enabled them to spend billions designing their own chips

2. Freedom from short-term market pressures

• These companies can pursue research agendas that span presidential administrations

• They can build infrastructure anticipating needs 5-10 years in the future

• They can withstand years of losses on moonshot projects that might never pay off

3. The ability to compete with nations

We're watching something unprecedented in human history: A handful of private companies are racing against an entire superpower to develop artificial general intelligence—potentially the last invention humanity ever needs to make.

China has bet the entire might of its authoritarian state on winning the AI race:

• Data from 1.4 billion citizens

• Mandatory corporate cooperation

• State-directed university research

• The full power of a techno-authoritarian regime

By every conventional measure of state power, China should be dominating this race. They're not. Not even close.

A handful of American companies—funded by cloud computing profits and advertising revenue—are out-innovating a superpower. This isn't just surprising. It breaks everything we thought we knew about technological innovation.

History tells a consistent story: breakthrough research is brutally hard to commercialize. Bell Labs invented the transistor but struggled to turn it into products. Xerox PARC created the graphical interface but couldn't bring it to market. Even Google, which invented the transformer architecture powering modern AI, watched OpenAI turn the technology into ChatGPT.

This "commercialization gap" isn't a bug—it's a feature. When Bell Labs struggled to commercialize the transistor, they created billions in surplus value for Intel and countless others. When Xerox fumbled the graphical interface, they gave birth to Apple and Microsoft. Google's transformer architecture spawned dozens of AI startups. Great research creates opportunities that extend far beyond its creators.

Yet there's a crucial difference between then and now: Bell Labs existed because regulators allowed AT&T to maintain its monopoly in exchange for public-interest research. Today's tech giants face aggressive antitrust scrutiny even as they fund America's most important innovation.

America's Innovation Vacuum: How Our Institutions Failed

With private companies now leading America's most crucial technological race, we have to ask: How did we get here? The answer isn't that tech giants seized power. It's that our traditional innovation institutions imploded, creating a vacuum that market-dominant companies were perfectly positioned to fill.

The Academic Meltdown: When Universities Chose Prestige Over Truth

In 2006, Duke University announced a medical breakthrough that made headlines worldwide: They had discovered how to match cancer patients to the most effective chemotherapy drugs using genetic testing. Prestigious journals published it. Clinical trials began. Thousands of cancer patients received hope.

There was just one problem: It was all fake.

Dr. Anil Potti had manipulated data, fabricated results, and lied about his credentials. But here's the truly horrifying part: Despite immediate red flags from other scientists, Duke let the clinical trials continue for four years. Journals kept publishing his papers. The NIH kept funding him. Cancer patients kept receiving treatments based on fraudulent research.

The system didn't just fail to catch fraud—it actively resisted discovering it.

This wasn't an isolated incident. When scientists at Amgen tried to reproduce 53 "landmark" cancer studies, only 11% could be replicated. Bayer reported similar numbers: just 25% of published academic findings were reproducible. We waste an estimated $28 billion annually on irreproducible preclinical research.

Academia has optimized for the wrong thing. Modern universities operate on a brutal equation: publish or perish. What advances careers isn't being right—it's being prolific. A scientist who publishes 30 flashy-but-wrong papers outperforms the one who publishes 10 rock-solid studies.

The result: modern academic science has become performance art. Papers written to be published, not to be true. Methods designed to sound rigorous, not be reproducible. Results framed for impact factors, not accuracy.

Even worse, pharmaceutical researchers have uncovered widespread "p-hacking" in academic studies—the practice of manipulating statistical analyses until insignificant results magically become significant. The issue is so pervasive that drug companies now employ specialized teams just to untangle the statistical manipulation in academic papers before deciding if a research direction is worth pursuing.

This isn't just a theoretical concern. In clinical trials investigating new treatments, a pattern emerges: industry-sponsored phase III trials show distinctly different statistical patterns than academic trials, suggesting systematic manipulation of results in academia. When billions of dollars and patients' lives are at stake, pharmaceutical companies can't afford the academic luxury of p-hacking.

Even Stanford's President Marc Tessier-Lavigne, the very top of the academic pyramid, resigned after investigations revealed data manipulation in his lab's papers. The rot goes all the way to the top.

Yet universities haven't lost any prestige. The public still views them as unimpeachable sources of knowledge while they've become paper mills optimized for publication metrics. They maintain their reputations while the reliability of their research collapses.

The implications are profound. As universities chase metrics over truth, the private sector has been forced to build its own research capacity. Companies can't rely on academic papers anymore—they need their own labs, their own experiments, their own verification processes. What started as a credibility crisis in academia has driven the largest privatization of research in history.

The Government Surrender: Abandoning America's Innovation Infrastructure

Our national labs, once the crown jewels of American science, are also running on fumes. Los Alamos uses computers generations behind those at Microsoft. Fermilab watches private companies build more advanced quantum systems. Lawrence Berkeley Lab hemorrhages talent to tech companies offering triple the salary.

America's best scientists are voting with their feet, and for good reason. Why would a brilliant physicist accept $150,000 at a national lab when Google offers $500,000 with cutting-edge equipment? Why commit to a government mission that might vanish after the next election?

During the Space Race, NASA had a clear purpose: beat the Soviets to the moon. DARPA had a mission: maintain America's technological military edge. Today? Government priorities reset with each administration. You can't achieve breakthroughs on a four-year election cycle.

America hasn't just cut research funding—it's abandoned the very idea of national purpose. As our public research infrastructure crumbles, we've created the perfect conditions for private companies to fill the void.

The Innovation Baton Pass: From Public to Private

With universities producing unreliable research and government labs losing their edge, a technological leadership vacuum formed at precisely the moment when America faced its greatest technological challenge since the Space Race.

Something had to fill this void. The future doesn't wait for broken institutions to fix themselves.

Enter the tech giants—flush with unprecedented profits, free from quarterly pressures, and able to think in decades rather than funding cycles. Where government retreated, they advanced. Where universities failed, they succeeded. Where national labs lost talent, they attracted it.

This wasn't a power grab. It was a response to institutional collapse. The market-dominant tech companies didn't steal America's innovation engine—they rebuilt it after we abandoned it.

The Regulation Paradox: America's Dangerous Gamble

Here's the dangerous paradox we now face: America's technological leadership depends on the very companies we're trying hardest to constrain.

Market dominance enables breakthrough innovation. But it doesn't guarantee it. Look at Epic Systems. They control 78% of U.S. patient records, a near-monopoly in healthcare data. Yet instead of transforming medicine, they've given us clunky interfaces and siloed systems. Market dominance provided the resources for innovation. Leadership chose stagnation.

This reveals a crucial truth: Market dominance is necessary but not sufficient. You need both the capacity and the courage to reinvest profits into ambitious research. What makes America's current situation unique is that our tech giants are doing exactly that—choosing moonshots over margins, decade-long bets over quarterly returns.

Meanwhile, China pursues technological dominance with decade-spanning research priorities that survive leadership changes, coordinated universities, and directed corporate resources. In theory, this centralized approach should give China an insurmountable advantage.

But something unexpected happened: America's tech giants built research capabilities that entire nation-states can't match. They attracted global talent that China can't access. They maintained research horizons that governments can't sustain.

Breaking What Works

If we apply traditional antitrust thinking to these companies, we might get lower ad rates or app store fees, but lose the innovation engine that's keeping America ahead.

Break up these tech giants, and you'll fragment the organizations that can think in decades rather than quarters. You'll dismantle America's last remaining bastions of breakthrough innovation.

As discussed in my Algorithm Wars post, this isn't an argument against all regulation. It's an argument for smarter regulation that distinguishes between:

• Natural network effects that enable innovation (Google Search gets better with more users)

• Abuse of distribution that stifles competition (Google using Search to push its flights product above competitors)

Let's look at the innovation landscape honestly:

• Government research has retreated to historic lows

• Universities have become unreliable paper mills

• National labs can't compete for talent

• Startups chase quick exits, not breakthrough research

• Venture capital wants returns in years, not decades

The only institutions consistently funding decade-long, civilization-scale research are the tech giants we're trying to break up.

The Stakes: More Than Just Market Share

We're living through a pivotal moment in human history. Artificial general intelligence, quantum computing, biotechnology are more than emerging technologies. They're the foundations of future civilization. The nation that leads these breakthroughs will likely shape the course of human progress for centuries.

In 1962, when Kennedy declared we'd go to the moon, America understood the stakes. We didn't break up NASA into competing agencies to lower the cost per rocket. We didn't worry that the Apollo program had too much market power in space exploration.

The current technological race with China is our generation's moonshot moment. But instead of rallying behind our innovation champions, we're debating which successful companies to dismantle first.

The uncomfortable truth is that in 2024, America's technological leadership depends on preserving these rare institutions capable of true breakthrough innovation. We didn't design this system. We didn't choose this path. But it's where we are.

The choice isn't between big tech and small tech. It's between American innovation and Chinese dominance. Between a future shaped by companies that can be regulated and one controlled by a regime that can't be.

We should regulate tech companies. But first, we need to understand what we're really regulating: not just corporate entities, but America's last remaining engines of breakthrough innovation.

America has a winning hand. Let’s not fold it.

Note: We typically talk about AI, but R&D is being privatized across other sectors too.

For example, in Pharma, the industry spent ~$83 billion on R&D, up from $5 billion in 1980 and $38 billion in 2000 (adjusted for inflation). Smaller biotech firms, often privately funded, also play a growing role, focusing on early-stage drug discovery.

Let's confront an uncomfortable truth: Blue states can't build.

A million people have fled California, yet housing prices keep climbing. Austin's rents just dropped 9% while San Francisco declares another housing emergency. And in an irony that would make Al Gore weep, Texas now builds more clean energy than California.

The standard narrative? Progressive politics kill development. But that's too simple.

Behind this crisis lies something deeper: a broken constitutional bargain and the forgotten power of political competition. It's not just about blue versus red—it's about who has to compete to survive.

Understanding this crisis begins with the Fifth Amendment—and how we broke it.

The Fifth Amendment's Forgotten Promise

"...nor shall private property be taken for public use, without just compensation."

These 12 words in the Fifth Amendment powered nearly two centuries of American growth. The bargain was simple: Government could restrict property rights for public benefit, but had to pay for what it took.

This elegant system built America:

• 41,000 miles of Interstate Highway in just 35 years

• 1.2 million homes built annually in the post-war boom

• Massive water projects that turned deserts into cities

• World-class universities while housing stayed affordable

How Courts Broke the Machine

Then in the 1970s, courts created a fatal loophole: a distinction between "physical" and "regulatory" takings.

The math became absurd:

• Take someone's entire property? Pay full price.

• Destroy 99% of its value through regulation? Pay nothing.

This judicial bug broke America's development machine. Why would any government buy land when they could regulate it for free? Why compromise when delay costs nothing?

This bug didn't affect all states equally. The damage varied based on one crucial factor: political competition.

A Tale of Three States

California: The Collapse of Competition, The End of Building

California once epitomized America's building ambition. From 1967 to 2011, political competition thrived—Republicans held the governor's mansion for 31 of 44 years while Democrats controlled the legislature. This balance forced compromise and produced remarkable results: the California Water Project, world-class universities, and over 200,000 housing units annually.

Then competition collapsed. The 2003 recall of Gray Davis marked the last truly competitive statewide race. Demographic shifts and redistricting eliminated competitive districts. By 2012, Democrats secured legislative supermajorities, while Republicans became politically irrelevant.

Without electoral accountability, the consequences were swift and devastating:

• Housing permits plummeted below 100,000 annually despite population growth

• Renewable energy development lagged behind neighboring states

• After 15 years and $10B, the High-Speed Rail project has not finished environmental review

• The state nearly abandoned Diablo Canyon, risking 9% of its carbon-free power

This transformation from "build state" to "stalled state" wasn't driven by progressive values—it was driven by the absence of political competition. When politicians face no electoral threat, special interests dominate: public employee unions, environmental litigation groups, and coastal homeowners protecting property values.

The result? A constitutional crisis so severe that even losing 800,000 residents since 2020 hasn't lowered housing costs. As Governor Newsom acknowledged, the cost of living remains the "principal driver" of outmigration, with housing affordability being the state's "original sin."

Texas: When Competition Drives Results

While California demonstrates what happens when competition collapses, Texas reveals how political competition—even within a Republican-dominated state—drives building success.

Despite Republican control of statewide offices, Texas remains politically competitive. Trump won by just 5.6% in 2020. Major metros like Houston, Dallas, and Austin maintain genuine political balance. This competition translates directly to building outcomes:

Austin's housing response tells the story. When the pandemic housing crunch hit, Austin faced the same challenges as California cities. But unlike their West Coast counterparts, Austin responded with action—meaningful zoning reforms that actually increased housing supply. The results were textbook economics: Austin built 957 new apartment units per 100,000 residents between 2021 and 2023, far outpacing any other major metropolitan region. By late 2023, over 10,000 new apartments had entered the market.

The outcome? Austin's average rent has fallen by 9.3% over the past year—the largest decrease among the top 50 U.S. metro areas. As City Council member Chito Vela admitted, "We were working under the premise for a couple of decades here in Austin that if we did not allow new construction, that would help preserve neighborhoods and hold down costs. That has just been objectively shown to be false."

The same pattern emerges in energy. Texas has built more wind and solar capacity than any other state. In 2022 alone, Texas added 7.7 gigawatts of renewable energy—nearly double California's 4.2 gigawatts. Wind and solar now generate 31% of Texas electricity.

This isn't despite Texas politics—it's because of them. Republicans who oppose renewables risk losing climate-conscious suburban voters. Democrats who oppose development appear ineffective. When margins are tight, neither party can afford ideological purity.

Arizona: The Laboratory of Perfect Balance

If California represents one-party Democratic rule and Texas shows Republican governance with competitive pressure, Arizona offers something remarkable: almost perfect political equilibrium.

Arizona isn't just competitive—it's mathematically balanced:

• State legislature operates on razor-thin margins (17-13 Senate, 33-27 House)

• Biden won the state by just 0.3% in 2020

• Voter registration stands at near-perfect thirds: 35.9% Republican, 28.9% Democrat, 33.6% Independent

This balanced environment has made Arizona America's most effective building laboratory. Housing development in Phoenix has surged to 42,000 new units in 2023—twice as many per capita as similarly-sized San Diego. The state maintains stronger environmental protections than Texas while enforcing a 180-day maximum for permit reviews, preventing California-style endless delays.

Arizona's energy approach demonstrates how political balance drives pragmatic solutions. While California wavered on nuclear power, Arizona's Palo Verde Nuclear Generating Station—America's largest nuclear plant—continues powering 4 million homes with a 94% capacity factor. Meanwhile, the state has installed 5.2 gigawatts of solar capacity with another 2.8 gigawatts under construction.

The crown jewel of Arizona's balanced approach is TSMC's $40 billion semiconductor complex. Environmental reviews completed in six months. Bipartisan legislation funded $1.2 billion in infrastructure upgrades. Democrats secured labor protections while Republicans streamlined permitting. The result? America's most advanced chip manufacturing facility remains on schedule for 2025 production.

This success stems directly from political competition: Republicans can't ignore environmental concerns, Democrats can't strangle development with excessive regulation, and neither side can afford ideological purity.

The Competition Index: A New Way to Understand State Success

To test this hypothesis, I’ve created a simple Political Competition Index for all 50 states based on:

1. Average margin in statewide races (past 3 election cycles)

2. Legislative competitiveness (% of seats decided by <10% margin)

3. Primary importance (% of races effectively decided in primaries)

When we plot this index against housing permits per capita and renewable energy development, a clear pattern emerges: states with higher political competition consistently outperform one-party states—regardless of which party dominates.

The top performers in housing development? Texas, Utah, Arizona and Florida—politically diverse states with real competition. The worst? California, New York, and Illinois—all one-party systems with different ideologies but similar outcomes.

In renewable energy deployment (adjusted for resource availability), a similar pattern emerges. The fastest deployers include Texas, Arizona, Florida, and Georgia—states with varying political leadership but real electoral competition.

The Path Forward: Constitutional Rights Can't Wait

Political competition drives building success—the evidence is undeniable. States with real competition like Texas and Arizona still build because voters can punish paralysis. But constitutional rights shouldn't depend on local politics—they must be equally enforced everywhere.

While China builds entire cities for AI development, California can't approve basic housing. This paralysis in our most productive regions threatens America's global leadership. With California and New York representing 25% of American GDP, every state must be able to build for America to remain globally competitive.

The scale of what we need to build is staggering:

• 3.8 million new homes just to meet current demand

• Data centers for AI will require >800 GW of clean energy by 2030

• $3.8T of infrastructure investments to replace aging roads, bridges, and pipes

We can't afford to wait for political competition to return to one-party states. And we can't be globally competitive without thriving coastal economies.

The Founders understood that unchecked government power to seize or destroy private property was a form of tyranny. That's why they wrote the Fifth Amendment. But courts created an artificial distinction between "physical" and "regulatory" takings that broke this protection. Why would any government pay fair market value for land when they could regulate it into worthlessness for free? Today's regulatory state exploits this loophole, destroying property values without compensation.

The solution demands federal action:

• Restore Judicial Protection: End the artificial distinction between physical and regulatory takings. When regulation destroys property value, the Fifth demands compensation.

• Force Timely Decisions: Implement mandatory permitting deadlines with automatic approval. Environmental review should take months, not decades.

• Create Compensation Funds: Establish state-level funds to pay for regulatory takings. Public benefits deserve public funding.

• Mandate Regular Review: Every building regulation needs a sunset date. Force bureaucrats to justify why rules should continue.

No country has ever regulated its way to prosperity. We can protect our environment while building the housing, energy, and infrastructure we need—but only if we restore the Fifth Amendment.

This isn't just about housing or infrastructure or energy. It's about whether America will remain a nation that builds its future. The Constitution gives us the tools. Now we must use them.

Every economics textbook tells the same story: Our era is defined by the triumph of information technology and ideas over physical constraints. The shift from manufacturing to services represents inevitable progress, they say. America evolved beyond the industrial age.

But what if this entire narrative is backwards?

In 1971, something broke in America. Real wages peaked and never recovered. Manufacturing began its steady decline. Infrastructure investment collapsed. While many blame the abandonment of the gold standard, there was a far more consequential shift: America began rationing energy.

This wasn't evolution – it was adaptation to constraint. Restricted by power limits, we pivoted to innovate in bits rather than atoms. We rebranded our manufacturing exodus as "post-industrial progress" rather than what it really was: a strategic retreat from the physical world.

I call this "Power State Theory" – the idea that energy availability fundamentally shapes the paths of innovation. This theory explains both America's digital pivot and why our software revolution is now hitting its inherent limits.

To build a truly prosperous future, we must first confront an uncomfortable truth: America's digital revolution wasn't a triumph of progress, but a clever adaptation to artificial constraint. And now those constraints are catching up with us.

When America Entered Low Power Mode

In 1971, America hit pause. As oil prices spiked, we made a decision that would reshape the next fifty years: we chose to ration energy rather than expand production.

Most people have never heard of the Henry Adams curve. But this curve is as fundamental to understanding progress as Moore's Law is to computing. Adams observed something remarkable: humanity's power output doubled every decade from 1840 to 1900. Each exponential leap enabled new infrastructure, new industries, new possibilities.

Then in 1971, America fell off the curve.

For the first time in modern history, our energy consumption per capita flatlined. Instead of confronting this constraint, we found a clever escape hatch: we redirected America's innovative capacity toward digital technologies—where progress demanded fewer watts.

The divergence between our physical and digital worlds became so stark it's almost comical:

• In 1973, the Concorde flew passengers at Mach 2. Today, our commercial planes fly slower than they did 50 years ago

• In 1969, we put humans on the moon. Today, NASA struggles to repeat what it achieved with slide rules and punch cards

• In 1958, we built the 46,000-mile Interstate Highway System in just 35 years. Today, California can't complete a 119-mile high-speed rail line after 15 years of trying

Meanwhile, our digital technology advances at warp speed. We carry supercomputers in our pockets. AI writes essays and generates art. Computing power doubles every two years like clockwork.

This wasn't random. It was the predictable adaptation of a High Power State transitioning to Low Power Mode. To understand why, we need a new framework for thinking about energy and innovation.

Power State Theory: A New Economic Framework

The deeper I studied America's digital pivot, the more a fundamental pattern emerged. Nations exist in one of two states: High Power or Low Power. The transition between these states doesn't just affect energy consumption—it completely redirects how a society innovates.

I call this "Power State Theory." At its core, we can express a nation's Power State (PS) mathematically:

Where:

• E = Available energy per capita

• E_t = Threshold energy level needed for advanced industrial activity

When a nation has abundant, affordable energy (High Power State), it innovates everywhere—from massive infrastructure projects to microprocessors. But when energy becomes constrained (Low Power State), innovation gets redirected toward domains that can create value with minimal power input.

This redirection follows what I call the Innovation Allocation Rule: talent and capital flow to wherever they can create value within energy constraints. Like water finding the path of least resistance, a nation's innovative capacity gets channeled into increasingly narrow domains as its Power State declines.

This simple rule explains both America's spectacular digital success and our simultaneous physical stagnation.

The European Energy Experiment: A Tale of Two Paths

We don't have to speculate about Power State Theory. Europe ran the experiment for us.

While America retreated from nuclear power after the 1970s energy crisis, two industrial powerhouses—France and Germany—made radically different energy choices. Their diverging outcomes tell us everything we need to know about the relationship between energy and innovation.

France's Nuclear Commitment When the oil crisis hit, France didn't flinch. They launched the ambitious "Messmer Plan" in 1974, building 56 nuclear reactors that now provide over 70% of their electricity. The results were precisely what Power State Theory would predict:

• France maintained a higher manufacturing percentage of GDP through the 1980s and 1990s

• They built an extensive high-speed rail network while America's passenger rail stagnated

• They preserved strong positions in energy-intensive industries like chemicals, metals, and glass manufacturing

France's Power State Index remained above the critical 1.0 threshold long after America dipped into "low power mode."

But when France eventually slowed their nuclear expansion and began prematurely retiring plants, their manufacturing base began experiencing the same decline as the United States—perfectly aligned with their transition back toward a Low Power State.

Germany's Four-Act Energy Drama Across the Rhine, Germany has unwittingly staged the perfect demonstration of Power State Theory—a drama in four acts that shows exactly what happens when a nation's Power State rises and falls.

Act 1: Nuclear Expansion (1975-2000) Germany builds 19 nuclear plants, watches its Power State Index climb, and cements its position as Europe's industrial powerhouse.

Act 2: The Golden Years (2000-2011) With abundant energy flowing, Germany maintains its manufacturing dominance. Their Power State Index stays high, and their factories keep humming.

Act 3: The Energiewende Gamble (2011-2022) After Fukushima, Germany makes a fateful choice: phase out nuclear while betting big on Russian gas and renewables. Despite pouring billions into wind and solar, industrial electricity prices soar. The results match Power State Theory perfectly: energy-intensive industries start to crack while precision manufacturing holds steady.

Act 4: The Energy Curtain Falls (2022-2023) Russia's invasion of Ukraine delivers the ultimate stress test. Germany's energy strategy collapses overnight, and their Power State Index plummets. The industrial carnage that follows is both devastating and revelatory:

• BASF, Germany's 158-year-old chemical giant, considers shuttering its flagship site

• Aluminum smelters go dark

• Steel mills fall silent

• Paper mills close their doors

But here's the smoking gun: the sequence of industrial decline matches energy intensity rankings with almost mathematical precision. The most energy-hungry sectors fall first, while less power-intensive manufacturers like BMW weather the storm. It's Power State Theory playing out in real-time—a cautionary tale for any nation contemplating its energy future.

The verdict is clear: Manufacturing decline isn't destiny—it's what happens when a society can no longer power its ambitions. France proved high-wage economies can maintain industrial might with abundant energy. Germany showed how quickly that might crumbles when the power runs low.

How Software Broke Economics—And Hit the Energy Ceiling

While Europe ran its experiment in energy policy, America was pioneering something unprecedented: an economy that could create value without power.

Economics has a blind spot. From Marx to Solow to Ricardo, every major economic theory treats energy as just another input—like labor or capital. But energy isn't just an input. It's the foundation that determines what's economically possible.

Then software changed everything.

When programmers write code, they perform a kind of economic alchemy: transforming pure thought into billion-dollar assets. No factories. No raw materials. Just keystrokes and caffeine. This digital escape hatch let us temporarily bypass physical constraints:

• Anyone with a laptop could access the means of production

• Distribution costs vanished

• Scale became virtually unlimited

• Value creation detached from physical reality

As Marc Andreessen famously declared, "Software is eating the world." But he missed something crucial: Software was also devouring economic theory—and masking our underlying energy constraints. Our celebrated "service economy" isn't the pinnacle of progress. It's the adaptation of a society that can no longer afford to make things.

The numbers tell the story of our adaptation:

• Banking and finance exploded from 3% of GDP in 1950 to over 8% today

• Management consulting became a $250+ billion industry

• Legal services spawned mega-firms with thousands of attorneys

• Healthcare administration grew faster than healthcare itself

This massive expansion of services created an illusion of progress. We convinced ourselves that moving digits around was an "advanced" economy. That optimizing systems was better than building them. That financial engineering could replace actual engineering.

But now this brilliant adaptation is hitting a wall. We've exhausted all the low-power economic paths. Every sector that could grow without significant energy inputs has already scaled. The service economy's growth was impressive but temporary—a one-time harvesting of efficiency gains that can't be repeated.

Last week, NVIDIA's CEO Jensen Huang dropped a bombshell: we're in a "power limited industry." This wasn't just corporate speak—it was the most powerful tech executive admitting what Power State Theory predicted: our digital escape hatch is closing.

The math is brutally simple. The next wave of computing requires exponentially more power:

• Training a single large language model uses more electricity than some small towns

• Modern semiconductor fabs need more power than entire cities

• Cloud computing already devours 1% of global electricity, doubling every 4-5 years

This isn't just about data centers. Every frontier technology—autonomous vehicles, medical research, quantum computing—slams into the same wall: energy. The digital and physical worlds are converging again, with power as the ultimate bottleneck.

The Consume Less Fallacy: Why Nuclear is Our Only Path Forward

As our digital escape hatch slams shut, America faces a stark choice: nuclear power or national decline.

Nuclear is the only energy source that's continuous, clean, and scalable. We don't need fusion breakthroughs or technological miracles. The fission technology we perfected in the 1970s could power a new American century. Germany's failed experiment proves that natural gas and solar can't sustain an industrial economy. And spare me the safety concerns—modern nuclear plants are safer than every other energy source combined.

But here's what fascinates me: watch what happens when you suggest America should use more energy—even clean energy. People physically recoil. For fifty years, we've been conditioned to see consumption as destruction, to treat restraint as virtue, to believe that doing more with less is progress.

This mindset isn't just wrong—it's mathematically incompatible with human advancement.

Nobody preaches "use less internet" or "process less data." We don't celebrate "computation efficiency" by doing less cancer research. Yet with energy—the foundation of all human capability—we worship at the altar of less.

Every leap forward in human history has been powered by an energy revolution:

• Coal ignited the first industrial revolution

• Oil built the American century

• Nuclear must drive what comes next

Here's the brutal truth: stagnation is humanity's default state. Progress isn't gravity—it's rocket fuel. And it needs power.

The people opposing energy abundance are—knowingly or unknowingly—choosing decline. They've accepted the premise that humanity must consume less, do less, become less. They've wrapped this surrender in the language of virtue.

France showed us the path forward fifty years ago. Our digital revolution, for all its wonders, was Plan B—what happens when you can no longer afford Plan A. But here's the good news: this constraint is a choice. We can choose differently today. The future isn't about doing less with less. It's about doing more with more—cleanly and sustainably.

Power up or fade away. The choice is ours.

What do you think of Power State Theory? Want to see more of the math? Respond and let me know

Every device in your home has a firewall. Your laptop, your phone, even your smart thermostat - all protected against digital invasion.

But your mind? Wide open, uploading everything it sees.

Our brains evolved for a world where information was scarce but reliable. Each signal mattered. Each story was worth processing.

That world is gone. We're drowning in low-quality content, engineered for attention rather than truth. And we're still uploading all of it.

The real danger isn't "fake news", which can easily be flagged. It's the endless stream of selective truth - stories built from real facts but missing crucial context. Not lies, just strategic silence.

And now AI is about to make it worse, lowering the barrier to creation and flooding our minds with hyper-persuasive content.

We need protection. We need a firewall for our minds.

How We Lost Truth

For most of human history, your mind was protected by expensive distribution. Moving information meant paying to move physical things – books, newspapers, people. Your brain was designed for this world – where information was scarce because spreading it was costly.

Universities and news organizations emerged as the gatekeepers of knowledge. The New York Times competed on trust, not viral headlines. Universities held exclusive domain over learning – you had to pay them to access knowledge. When reaching people was expensive, quality was the only way to win.

Then, the internet made distribution free. Anyone could publish, anyone could share. Even MIT put their courses online for free. Knowledge was finally available to anyone with an internet connection.

But something unexpected happened.

With no monopoly to protect them, institutions faced a brutal choice: chase engagement or die. The Times and Fox News didn't become partisan entertainment by choice – they did it to differentiate in a crowded market. This created a devastating feedback loop:

• Institutions chase engagement to survive

• Engagement rewards emotional resonance over accuracy

• Audiences fragment into reality bubbles

• Each bubble develops its own truth

• Institutions double down on serving their bubble

The damage isn't theoretical. Just two decades ago, America stood united in the face of terrorism. Now, in the very same city that endured 9/11, college students march down Fifth Avenue waving terrorist flags while their institutions debate whether terrorism is just another viewpoint. We've lost more than just trusted news sources – we've lost our ability to maintain even the most basic shared truths.

Without a protected monopoly, truth doesn't sell.

The Great Multiplication

While the internet destroyed the cost of distribution, one economic barrier survived: creation still required human effort.

Writing articles, producing videos, crafting arguments – it all took time and talent. Even the most engagement-driven newsrooms had natural limits. You can only create so many versions of reality with human hands.

That barrier is about to break.

AI is about to turn every creator into a factory. A journalist who once wrote one article per week becomes an army:

• Hundreds of story versions, each emphasizing different truths

• Thousands of narratives tested and optimized

• Every piece learning from engagement

• Each variant evolving to become more persuasive

We already can't handle today's flood of selective truth. Already, institutions shape our reality by choosing which facts to show us. What happens when every Fox News writer can spawn hundreds of versions of each story? When every New York Times journalist can test thousands of narrative variations? When each piece learns from your responses, optimizing to reshape your reality?

Your brain, already drowning in today's information, isn't ready for what happens when content becomes infinite.

Enter the firewall.

Your Brain's Last Defense Is Breaking

Some will say we just need to "think critically" about this flood of AI-generated content. That we need to keep our minds open, not build walls.

Here's the uncomfortable truth: your mind is already walled off.

It's called confirmation bias, and it's how your brain copes with information overload:

• You automatically reject what challenges your beliefs

• You seek out content that confirms your views

• You trust sources that match your identity

• You dismiss evidence that threatens your worldview

This isn't a choice. It's how your brain works. You're not choosing to filter – you're filtering by default.

The internet didn't create these biases. It just gave them rocket fuel. With access to infinite information, humans should have become more open-minded. Instead, we just got better at finding tribes that confirm what we already believe.

We traded physical villages for digital ones. And now AI is about to make those villages infinite.

The Evolution of Mental Protection

Every era builds new defenses against information overload:

Generation 1: Natural Filters (All of Human History)

Your brain's built-in defenses: tribalism, skepticism, confirmation bias. Crude but effective when information moved slowly.

Generation 2: Content Moderation (2010-2020)

Social media's clumsy attempt at digital filtering. Twitter's echo chambers, Facebook's algorithms, Google's personalized results. Not innovation – just confirmation bias at scale.

Generation 3: Community Notes (2023-2024)

Our first breakthrough in fighting selective truth. Not through fact-checkers or algorithms, but through collective intelligence. When institutions try to shape reality through careful omissions, thousands of readers can now fill in what's missing. For the first time, we're exposing the gaps between competing truths.

Generation 4: The Brain's Firewall (2025+)

We wouldn't let an unknown program write directly to our computer's memory. Yet every day, we let any content write directly to our brain – no firewall, no protection, no filter.

While Community Notes relies on humans to spot omissions, an AI firewall can instantly reconstruct what's missing from thousands of competing narratives. Imagine a system that shows you what every piece of content is hiding:

• Critical facts omitted

• Historical context stripped

• Emotional triggers used

• Manipulation patterns deployed

Think of it like nutrition labels for your mind. Before consuming information, you see: "This housing crisis article:

• Strips key context about zoning laws

• Uses language patterns that typically shift readers against development

• Mirrors tactics that changed your views before

• Contradicts local housing data"

This isn't just another filter – it's an upgrade to human consciousness. Instead of drowning in selective truth, you finally see how information shapes you.

The technology exists. Language models can already map narratives, spot triggers, and find missing context. We can already predict how content shifts beliefs and reveals manipulation patterns.

We don't need more content moderation or crowdsourced fact-checking. We need tools that can process the flood of information faster than human minds and show us exactly what we're missing.

The Future of Truth

There's a generational company waiting to be built: an AI-powered defense system for the human mind. Not just another content moderation tool – a system that helps us see what's being hidden from our reality. The business opportunity is massive, but the stakes are even bigger.

Maybe this is why we can't find advanced civilizations in space: they all drowned in information. As societies advance, information flow accelerates until it breaks minds. Until rational thought drowns in the flood.

We're already seeing the cracks:

• Our most trusted institutions optimize for engagement over truth

• Universities can't distinguish between terrorism and activism

• AI is about to multiply all of this by 100x

Democracy requires shared reality. When we can't agree on basic facts, when we can't see how we're being persuaded – democracy dies.

The flood is coming. We have two choices:

Let our minds drown.

Or build the firewall.

TikTok isn't a social media company.

It's the most sophisticated psychological influence operation ever created. And it's working better than anyone imagined.

While we worry about data privacy, China has figured out something far more valuable: how to reprogram the American mind 20 seconds at a time.

If we don't act now, we'll become something unprecedented in history: a digital colony.

The World's Most Powerful Weapon Isn't Nuclear

It's algorithmic.

Think about it: what's more powerful than controlling what 150 million Americans think about every day? What they care about? What they believe?

The most effective propaganda isn't the kind you resist. It's the kind you scroll through voluntarily, 20 seconds at a time.

The Global Subconscious

Every generation has a shared reality - a collective set of assumptions about what's normal, what's possible, what's true.

Think of it as humanity's global subconscious.

Throughout history, this shared reality was shaped by:

• Religion (for thousands of years)

• Mass media (for hundreds of years)

• Social media (for the past decade)

But these were blunt instruments. They could shape what people thought about, but not how they thought.

TikTok changed the game. For the first time in history, a single algorithm can shape not just what billions of people think about, but how their minds process information itself.

This isn't hyperbole. When 40% of Gen Z turns to TikTok before Google, we're not just seeing a shift in search habits. We're watching the rewiring of humanity's cognitive infrastructure.

The Architecture of Empire

Every empire in history has had three key elements:

1. Control of information flow

2. Cultural narrative dominance

3. Invisible compliance systems

The British Empire had its newspapers, its English language mandate, and its civil service system. The Roman Empire had its roads, its shared Latin culture, and its local governance structure.

What's changed isn't the playbook - it's the technology. And TikTok has perfected all three elements in ways previous empires could only dream of.

1. Information Flow: The 20-Second Revolution

Most platforms optimize for engagement. TikTok optimizes for rewiring the global subconscious.

Think about how ideas spread historically:

• Books required hours of commitment

• TV shows needed 30-minute blocks

• Facebook posts demand at least minutes

TikTok broke this pattern. At 20 seconds, you'll watch anything. Even ideas you disagree with. Even perspectives you'd normally reject.

This isn't just faster content consumption - it's a fundamental rewiring of how humans process new ideas. When you control the speed of information flow, you control the depth of resistance to new thoughts.

2. Cultural Reprogramming at Scale

When you control the algorithm, you control more than content - you control the global subconscious itself.

Each major platform shapes this shared reality differently:

• Google reinforces existing beliefs (the echo chamber)

• Facebook amplifies emotional extremes (the outrage machine)

• YouTube creates rabbit holes (the specialization trap)

• TikTok shapes what you'll believe next (the consciousness sculptor)

Example: Search "American history" on TikTok vs. Google.

• Google shows you what your subconscious expects

• TikTok shows you what they want your subconscious to expect next

This isn't random. ByteDance has built the world's most sophisticated system for cultural narrative control. They're not just serving content - they're shaping consciousness at a scale never before possible.

We've already seen this power in action. During the Hong Kong protests, TikTok's feed was filled with cute pets and dance videos. Meanwhile, protesters were fighting in the streets. This wasn't an accident - it was algorithmic anesthesia.

The same platform that can make a dance trend go viral chose to make millions of young people forget that democracy was dying next door.

That's the real power of controlling the global subconscious. It's not just about what people see - it's about what they don't see. What they forget to care about. What fades from collective memory.

3. Voluntary Compliance

The genius of TikTok's approach? We think we're choosing.

Unlike Facebook or Google, which feel like information utilities, TikTok feels like entertainment. We don't resist it. We don't question it. We scroll.

This is why traditional media regulations miss the point. You can't regulate voluntary behavior. The most effective empire is one where the subjects don't know they're subjects.

The Hollywood Warning

We've already seen how this control of the global subconscious plays out in Hollywood.

Remember when movies were America's secret weapon? When Rocky helped win the Cold War and blue jeans brought down the Berlin Wall?

Now:

• Tom Cruise's Maverick jacket mysteriously lost its Taiwan patch

• Marvel can't have Chinese villains

• John Cena apologizes in Mandarin for calling Taiwan a country

This isn't censorship. It's worse.

It's self-censorship.

Studios aren't waiting for Chinese regulators to demand changes. They're preemptively sanitizing their own content. In true Bradbury fashion, we're burning our own books.

What Real Power Looks Like

Everyone thinks America's power comes from our military or our economy. But our real power has always come from two things:

1. The dollar as global reserve currency

2. Control of the global subconscious

That second one? We're giving it away for free.

When kids in Indian villages watch Mr. Beast, that's American power. When the Kardashians influence fashion in Asia, that's cultural dominance. When protesters worldwide demand their "Miranda rights" (even in countries where they don't exist), that's America shaping the global subconscious.

Now imagine that power in reverse.

The World's First Digital Colony

We're watching the birth of something new: digital colonization.

Traditional colonization claimed land and resources. Digital colonization claims something far more valuable: the global subconscious itself.

Why fight a war when you can just... change how people think?

Some numbers to consider:

• TikTok users spend 95 minutes per day in algorithmic reprogramming

• 40% of Gen Z trusts TikTok to shape their reality

• ByteDance (TikTok's parent) is worth more than Meta

This isn't just another social media platform. It's the most sophisticated consciousness control operation ever created.

Subscribe now

What Happens Next?

Banning TikTok isn't enough. We need:

1. Cultural export subsidies (yes, even for influencers)

   1. ex: subsidize movies that don't compromise for Chinese distribution

2. Ban foreign controlled algorithms

3. New frameworks for protecting the global subconscious

But first, we need to wake up.

The battle isn't about data privacy or screen time. It's about who controls humanity's shared reality. And right now, we're one congressional vote away from becoming history's first digital colony.

The choice is ours. But we're running out of time.

Share

The US government just launched its biggest tech antitrust case in decades. Their target? Google's Chrome browser. Meanwhile, China's TikTok has quietly become the West's dominant search engine for an entire generation.

The timing is darkly ironic. While American regulators obsess over browser market share, Beijing is winning the algorithm wars - building the most sophisticated attention prediction and influence system ever created.

We're fighting yesterday's battle with outdated weapons. Big Tech needs regulation - but not like this. We need a new framework for the age of digital utilities.

The Paradox of Free Products and Market Power

Consider Google Search. Despite commanding over 90% market share, it remains free to users. This creates a paradox for traditional antitrust theory: how can a product simultaneously dominate its market while extracting zero direct consumer surplus?

The answer lies in the nature of attention markets. Google monetizes through advertising, creating a three-sided market between users, advertisers, and content creators. Each additional user makes the platform more valuable for advertisers while simultaneously improving search result quality through enhanced training data.

Chrome's dominance follows a similar pattern. While critics point to its default search settings as anticompetitive, users face zero switching costs to alternative browsers or search engines. The persistence of Chrome's market position stems not from coercion but from the compounding benefits of vertical integration.

In fact not so long ago, Microsoft was under antitrust scrutiny for anticompetitive distribution of Internet Explorer. Chrome, a web browser not made by Apple or Microsoft, is one of the last independent browsers. The next closest competitor, Firefox, is almost exclusively funded by Google as well.

Abuse of Distribution, Not Network Effects

Traditional antitrust frameworks struggle with network effects because they confuse natural platform dynamics with anti-competitive behavior. Google Search gets better with more users. That's not monopolistic - it's how digital platforms work.

The real issue isn't network effects - it's how companies abuse their scale and distribution. When Google uses Search dominance to push its own products above competitors, that's anti-competitive. When Apple forces developers to use their payment system and charges them above market processing fees, that's abuse of market power.

Breaking up these platforms doesn't address the actual problems. You don't solve abuse of distribution by fragmenting networks. You solve it with clear rules about how dominant platforms can behave.

The Problem with Late Regulation

Regulation typically comes too late in an innovation cycle. In the early 2000s, Microsoft was fighting antitrust battles over Windows and Internet Explorer. This distraction caused them to miss building a dominant mobile platform.

We're seeing the same pattern today. For 20 years, Google's advantage was simple: more traffic = better training data = better results. This flywheel was nearly impossible to break and they created a phenomenal business. 

But technology has shifted. AI algorithms can now generate high-quality results with minimal data. Companies like Perplexity and You.com are proving you don't need Google's scale to build competitive search; these new models need a fraction of the training data to be accurate.

Meanwhile, Google is losing in the fastest growing category of search: video. 40% of GenZ prefers to use TikTok as their search engine. Not just for entertainment - for restaurants, products, and general information.

The government is forcing Google to fight over Chrome's market share while the fundamental nature of search is changing. We're regulating the wrong problem at the wrong time.

The Regulatory Path Forward

Rather than pursuing breakups, we should treat dominant tech platforms as digital utilities:

• Mandate equal access and non-discrimination

• Regulate rent extraction (e.g., App Store fees)

• Ensure data portability

• Maintain privacy and security standards

Let's take Google Flights as an example. Google showing its flight results above Expedia? That's using market dominance in search to squeeze competition in travel. A utility framework would require Google to rank Expedia fairly against its own products. Same rules should apply across Maps, Shopping, and other vertical searches.

The Algorithm Wars: the Digital Cold War

The real battle isn't about browser market share – it's about who controls the algorithms that increasingly shape global consciousness and commerce. While Western regulators fixate on Chrome's distribution, China has quietly built the world's most sophisticated attention prediction and influence system through TikTok.

This isn't just about social media dominance. TikTok's recommendation engine represents a fundamental leap forward in algorithmic capability. Its ability to predict and shape user behavior extends far beyond entertainment, becoming the default interface through which Gen Z discovers everything from products to politics. Each scroll, each pause, each interaction feeds into an increasingly precise model of human attention and behavior.

The stark contrast in governance models is telling:

Western platforms operate under intense scrutiny – their algorithms dissected by researchers, their CEOs regularly summoned before Congress, their data practices constrained by privacy laws. Meta's every algorithmic tweak faces public criticism; Google's ranking changes trigger congressional inquiries.

Meanwhile, ByteDance operates as a black box. Its algorithm – potentially the most powerful tool for mass influence ever created – answers to Beijing, not democratic oversight. When TikTok executives testify before Congress, they offer carefully scripted evasions about data access and content moderation. The real decisions happen behind closed doors, guided by priorities that align with China's strategic interests.

This asymmetry creates a dangerous dynamic: While American tech giants are hammered into regulatory submission, their Chinese counterparts operate with relative impunity, expanding their reach and refining their capabilities. We're effectively handicapping ourselves in the most important technological race of our time.

The stakes extend far beyond market competition. These algorithms increasingly determine what information billions of people see, what they believe, and how they behave. If we allow this power to concentrate in the hands of opaque, unaccountable entities aligned with authoritarian interests, we risk ceding control of the digital infrastructure that will define the next century.

Breaking up Google won't solve this problem – it will accelerate our decline. Instead of fragmenting our technological capabilities, we need a coherent strategy that preserves innovation while ensuring algorithmic systems serve democratic values. This requires sophisticated regulatory frameworks that address real harms while maintaining our competitive edge in the global algorithm wars.

Looking Forward

As a technologist, I want more competition in Search. Google results feel stale, and innovation has stalled. The teams at Perplexity, You, and others are making exciting progress in next-gen search.

But as an American, it is treasonous to think Government overreach would suffocate one of our greatest strategic assets. The next decade determines whether the future of technology serves democratic or authoritarian ends. Breaking up American tech companies would be an own-goal of historic proportions.

The path forward isn't dismantling these platforms but thoughtful regulation that preserves innovation while ensuring they serve the public interest. We need to move fast - while we debate Chrome's market share, China is winning the algorithm wars.

for students🙋

Open Water Accelerator is now accepting applications for our Venture Scout Program! Venture Scouts help find new start-ups and beta test products that we are considering for investment. Unlike other scout programs, if we invest in a company you find we cut you a check (no complicated carry structures).

Join the Open Water Scout program and network with 100 of the most talented students across the United States.

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building Levels with Josh Clemente 🔨

This week we sat down with Josh Clemente, founder of Levels. Levels is a device that tracks your blood glucose in real time so you can maximize your diet and exercise. Prior to founding Levels, Clemente was the Lead Life Support Systems Engineer at SpaceX.

What's your ultimate vision with Levels?

The long term vision for the company is to make a meaningful difference in metabolic outcomes in the world. We intend to do that by having the most robust and actionable model of metabolic health possible.

We're going to provide an elegant, obvious interface with information that defines metabolic health as close to the point of decision making as possible. This will make it easy for people to know how to make a better choice and to see the results of their choices stacking up. It will truly be the metabolic health coach in your pocket.

What do you know now that you wish you knew when you started your company? 

The biggest lesson for me has been to embrace transparency. If simply knowing about your idea is enough for someone to replicate it, then it’s not a very good idea in my opinion. There should be significant execution risk. There should be a challenge that has to be overcome.

In the past, my approach has been, “I need to keep this thing hidden. I need to be wary of sharing information about this so no one else goes out and does it.” It was really when I partnered with my cofounder, Sam, that I started to understand the flaws of that mindset. He framed it best for me, saying that at this company we share information promiscuously because it's the best way for potential advocates to see inside your organization, see inside your mind, and understand how you think about and tackle problems. I really wish I had known that and embraced that earlier in the life cycle.

I found it very interesting that you weren’t just marketing towards current and aspiring professional athletes, but instead are modeling the lifestyle brand for regular people to help them make better decisions.

The question that I frame is: you're sitting down for lunch, what are you going to eat and why? And that's a very simple question, but most people feel ill equipped to answer that with any quantitative output. They answer, “Well, I'm going to eat something that tastes good,” or, “I think I’ll eat something that I've heard is healthy,” but they don't have any concrete objective data that's driving that choice.

The people that do know are the ones that have PhDs in physiology or metabolism, and that needs to change. We need to take the disconnect of research versus action and bring these two things together and mesh them into a framework for making better choices. It has to be based on the individual. It can't be based on population sets or general advice.

When your personal body tells you something, you listen, and that's the light bulb concept behind what we're doing. After using Levels, you will never look at food the same way again, and this is a good thing. You start to feel confident in the choices you're making and your choices are supported by data.

What's the biggest risk you've taken? 

The biggest risk for me was leaving my leadership role at SpaceX. I was working on the life support systems team that just flew Bob and Doug to the international space station. It was bittersweet to leave.

I moved cross country to start an entrepreneurial adventure that was in a totally different field, totally different industry. And honestly, the industry did not exist. It was thinking, “Oh, I'll start a new one,” which was kind of insane.

A year and a half later when I had zero traction, it felt like a devastatingly bad choice. I was getting nowhere on my own and the insecurity was kind of creeping in, “This is crazy. What did you do? Why'd you do this? SpaceX wouldn't take you back if you tried now.” I think everyone deals with this type of insecurity when they jump off a ledge, so to speak. I certainly felt like I was in freefall for some time.

Ultimately things turned around by just continuing to have confidence in the underlying concept and its potential. I had already changed my own life as a result of using continuous glucose monitoring information; that was the thread of objective knowledge that there is something here that kept me pushing ahead. Having that confidence, having that bit of personal skin in the game, helped me to pull through that crazy decision to leave a leadership role at a company that is doing unparalleled things.

How do you get unstuck in that situation where you're a year and a half in and you don't have traction? What's going through your head? How do you keep going and get to where you are now?

It was the realization that I had allowed myself to forget the value of an exceptional team. Going back to SpaceX, the most groundbreaking thing about it is the quality of the people and the action oriented culture that the team operates under. Elon Musk is a great guy and I'll get to him in a minute, but he is not the reason SpaceX is successful.

It doesn't matter how good the quality of your ideas are. It's whether or not you have the team that can execute. I allowed myself to forget that. I tried to do this venture solo and tried to make significant progress on my own. The inflection point was when I realized that I needed help, I needed support. I needed a framework that was built with multiple perspectives.

I had known my cofounder Sam, who's a good friend of mine, for several years and he and I stay in touch about our projects. One of us reached out and we had a deep conversation about what I was working on and the light bulb went off in his mind too. At that moment, there was a 48 hour period basically where we went from conversation to partnership and what I had worked on for a year and a half with very little inertia became a real thing overnight. It was just the force multiplier of having two minds on it.

Eventually as the team came together, it's gone in an unbelievable amount of time from an idea to a reality. I think the lesson is to just realize your own limitations. It's always better to have support, to find people who have similar values and work together. 

Who did you look up to while growing up and who inspires you today? 

I've always looked up to my dad and then my grandfather. These two guys have shaped my worldview in ways that are just kind of hard to fully describe. My understanding of independence and equality, the benefits of challenging the status quo and thinking outside the box, hard work, and close relationships are all things that I learned from them specifically.

My mom is also another hero in my life. She patiently taught me from kindergarten through 12th grade. I was homeschooled. Having those tight relationships with my parents and my grandfather, I am cognizant of those lessons learned and how impactful role models can be.

Today, I'm inspired by Elon Musk. This is a guy who completely ignores the objective impossibility of his ideas, and he just works to shape the future in his mental image. He's just says, “This is how it should be. I'm gonna make it happen.” Many say that what he is doing is “crazy” or “impossible” but he does it anyway.

It is challenging to push past the limitations other people put on you and I continue to be blown away with Musk’s work. I've worked with him, everyone's got their faults, but this guy is someone I think is utterly inspirational for me and for many generations to come. 

How did homeschooling shape you?

The biggest thing is that it taught me to teach myself. When I got to college, I went to class maybe 40% of the time. I would take the textbook, sit down, and teach myself through reverse engineering problems. I take the answer key in the problem and figure out how the concept works. That was kind of how I learned. I learned by myself basically because I'm from a large family and my mom did a really good job showing us how to string together those concepts. That's what mattered, not so much reading and trying to memorize the words in the book.

My parents were both nonconventional in many ways. We were allowed to set our own schedule and I spent a lot of time outside in the woods. I spent a lot of time in the garage building crazy machines that were made out of chainsaws and bicycles. That homeschooling environment of being able to really think independently and freely ended up being what I think got me my job at SpaceX, because I knew how to build stuff. I knew how to solve problems that I had not seen before because I needed to teach myself. I think you can get a lot of this through various means of education.

I certainly feel that I was lucky to have had that opportunity. And, in retrospect, even though I didn't like it while I was going through it, I think it was critical to where I am today. 

What do you think the future of software is? 

I'm going to caveat this by saying I'm not an expert. My personal career has been in hardware, but I do have some feelings about this. I think, in general, the future of software is that there will be more of it and it will be more actionable and insightful rather than just this utilitarian toolset. When I hear the word software, I think about Microsoft Excel. I think the software of the future is the integrated external second brain. Obviously we hear that term a lot, but something that is a layer in our lives that helps us make better choices every day.

Of course, I have a personal reason for believing that. This is what Levels is doing. Look at the future of health software as an example. It will be predictive and much more like financial software than our current medical records, which are totally useless; no one has ever consulted their personal medical record to make a decision in their lives. Today, I can pull out my phone and I can pull out my mobile banking apps and see my retirement projections, 15 to 30 years in the future. I have an opportunity to share that information with an expert, to get their opinions. I can see projections, best and worst case outcomes.

Yet the question I would ask myself is, “How do I know I'm going to be there to enjoy that retirement?” I think the future of software will be our health information being available in the moment via software in the same way that we use our financial information now. It will help guide you in real time so you understand the “deposits and transactions” you're making daily and how the interest on those “assets and liabilities” are going to set you up for good or bad outcomes. I see the future of software as being embedded, useful, and actionable.

future of drug delivery with Shak Lakhani 💊

Shak Lakhani is the CEO and Co-Founder of Avro Life Science (YC W18). He is building a patch that will deliver generic drugs and small molecules, with a particular focus on therapeutics for CNS disorders, cardiovascular health, and consumer health.

Photograph of Avro Patch

What have your last six months looked like and what do your next six months look like for growing this company?  

We’re spending a lot of time selecting our internal assets: the drugs of highest interest that we want to take to the clinic ourselves. When it comes to bringing in different drugs to market, we can go one of two different ways:

1. License in compounds from leading pharmaceutical companies and go to market ourselves. However, this can be pretty costly at an early stage company.

2. Explore generic drugs. Generic drugs have smaller margins, but if put in the right dosage form for us to patch could still do really well.

At the moment we're opting for the latter, looking at generic drugs where there aren't any restrictions to working with them from an IP/market landscape point of view. It's just a matter of if we have the technical chops to make it happen.

One of the fundamental steps that we're making is to attempt to get larger molecules through the skin. We’re trying to stretch the boundaries of what we believe to be possible. We’re spending our time talking to dozens and dozens of clinicians and specialists to figure out the science of it.

So, those are my main initiatives. Figure out what our internal products will be that we’ll spend three to five years taking to market ourselves and finding out if we're able to get larger and larger molecules through the skin.

Our next six months are going to be more of the same, but hopefully at a faster pace as COVID eases up where we are in Waterloo and we move into a new lab space.

The tricky part about biology or science in many cases is it's not necessarily additive; sometimes it is synergistic. What this means is that if we perform a series of steps in a certain order, it'll have one outcome, but if we perform them in a different order, it'll have a drastically different outcome. Sometimes even though a step may lead to a better result in one manifestation, if another variable has been tweaked ahead of time in another manifestation, it might actually take you backwards. The process is about trying to understand which of our parameters are universally helpful or which of our parameters can go in both directions. We will also be exploring this in the next few months.

What do you get about this space that your competitors don't get? Why were you able to start this company versus some large pharmaceutical companies? 

I think it's probably a mix of naiveté, enabling technology, and a belief in the ability to realize value in something where traditionally value isn't described. Pharma companies and the pharma ecosystem as a whole are heavily weighted towards new drugs and new therapeutics. That makes sense because that's how the industry has been driven for the last hundred plus years; you look for a new drug, try to bring it to market, and hopefully make billions off of it.

The way I see it is that the rate of drug discoveries is declining and patients still need better solutions and cures. There is value in improving the way drugs are delivered to patients and in some cases a new delivery method might be as valuable as a new drug.

I don't know if this is necessarily something that pharmaceutical companies haven't realized, I'm sure that they have in some manifestations, but it's about making something that seems scientifically ridiculous enough to not be worth pursuing possible. That's the core thing that we're going after here.

A lot of that is based on other branches of my scientific philosophy that I think make sense. We look at the types of technologies that will manifest themselves in other industries, but still work on the same fundamental principles that can be safely ported into our tech. That helps us take a look at how we can use nature and information that already exists to push the boundaries of what we think is possible.

What do you know now that you wish you knew when you started your company? 

If you think that your vision is already big enough, nine out of ten times, you're wrong. I think that we started Avro Life Sciences with too narrow a scope.

As a young founder, you can have a lack of perspective. You're working on something that seems like it’s really tough and can be really impactful, but you don’t realize your scope of the possibilities is already constrained to start with. One of the things I wish I knew was how much more we could potentially do if we opened our minds.

The other thing I wish I’d known is how many things go wrong along the way. I think it's easy when you're a precocious young founder to say, “Yes, I'm smart and I will figure all these problems out that other people have not been able to solve.” It's a common mistake to underestimate the efforts of people in the past and then find yourself in a situation where you’re in over your head.

What's the biggest risk you’ve ever taken?

This does feel like a bit of a cop out answer, but I think the biggest risk I’ve taken is forming a company in this space. All the optics and all the odds are stacked against us. Why are two college drop outs from Waterloo gonna figure something out that big pharma companies haven’t? Just logically speaking, it doesn't carry through at all. That's a pretty gnarly risk in itself.

I think the other risk is tackling a fairly large problem. It would be a lot easier and a lot more feasible to bring a single drug to market. I don't think that's impactful enough in most cases. It’s important to strike a good balance between how interesting or how impactful something is and how realistic it is.

Have you had any big failures so far? If so, how do you recover from setbacks as a team?

We had one major failure. One of the formulations we were working with, that we thought might be the one that took us to market, had major stability issues and didn't have strong results in our first set of trials.

It was half a year or a year of research down the drain. We figured out how to take the useful parts of the process that we had learned from that experience and adapt them to our next iterations.

For us, we accept that untraditional work is tough and we expect to fail sometimes. It's just a matter of figuring out why something has gone wrong and how we can take that information and make it useful to us in another way so that things go right the next time. 

Do you think you'll go back to college once you follow the course of this company?

Once I see it go through (hopefully it ends up doing quite well), I would be interested in going back to school to pursue a PhD or explore a field that would be relevant to my next company. I'm probably too set in a startup mindset now or too addicted to the kind of stuff that I do to enjoy a corporate role.

What do you think the future of the entire drug industry is going to look like 20 years down the road? 

I think there's a scene in Family Guys that depicts it kind of well: Quagmire walks out of a building and then gets an STD shot that automatically comes out of his phone. I don't think it will go quite that far, but I think that it is possible to have a society where we have, at an early age, a set of vaccinations or injections that will selectively activate or fight off infections that you encounter along your lifespan. There will be smart drugs as opposed to drugs that you need to take.

I think that there will also be a number of conditions that will hopefully be reversed or cured through gene therapies and cell therapies. That'll hopefully allow for the elimination of certain conditions or better treatment, whether that's for cardiac health or for Alzheimer's and mental health, or whether that's for the more specific musculoskeletal or retinal diseases that we see today.

I think that there's hope for gene therapies that act as permanent cures as opposed to bandaid solutions, and for appropriate or engineered drugs/bacteria combinations that create a stimuli responsive system in your body that shuts down problems before they occur. 

our take on the future of health tech 📣

Currently health care is reactive. Doctors detect an issue and then they prescribe a treatment to remedy the issue. The future of health tech will be in proactive care. Doctors will have tools to identify health issues before they emerge.

The health devices of the near future will detect critical health issues shortly before they happen. An example of this would be an Apple Watch calling an ambulance because they detected a series of irregular heart beats that indicate the user may have a heart attack shortly.

The next generation of connected devices will also use big data to help users live an healthier lifestyle and prevent long term health issues. Levels is an example of a company working to build a healthier future for its customers. Levels provides a dashboard that can produce a number for how healthy a user is eating and regulating their blood sugar.

Another early mover in the health care field is Whoop. The Whoop Fitness Band measures sleep, fatigue, and strain in athletes. With this data, the band can also predict when a user will catch a cold due to fatigue. As Whoop continues to refine their algorithm, I would not be surprised if they could start predicting a user’s lifespan.

At Open Water we are looking for the next generation of health tech that will keep its. users healthy and detect problems before they emerge. If you are working on a platform similar to this, we would love to hear about it.

leaving shore⛵: Savvy is raising an angel round

Savvy is a cash flow based underwriting platform for housing, insurance, and lending. They leverage their position in the market and data advantage to distribute financial super efficiently to consumers and businesses.

They are based in Brooklyn and backed by Antler VC, Switch Ventures, New Lab Ventures, and The MBA Fund.

Savvy is currently raising a small angel round to scale our engineering team to meet the overwhelming customer demand. If you would like to learn more, reply to this email and I’ll make the introduction.

internships 🖥️

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next week…

Next week’s letter will feature an interview with Hunter Walk, Partner at Homebrew, and some more remote job opportunities.

building Lambda School with Austin Allred 🔨

This week we sat down with Austen Allred, co-founder of Lambda School, to get his thoughts on the future of education. Lambda School is a remote coding bootcamp that lasts between 9 and 18 months. Unlike a traditional university, students only pay after they have been hired. Lambda School graduates have gone on to be hired by Fortune 100 Companies and the world’s fastest growing startups.

This interview was edited for clarity.

What did you learn from starting your first company?

From starting my first company, I learned how important product market fit is. It's 10 times easier to adjust the product to make it exactly what people want than it is to try to sell something that people halfway want.

That was my big takeaway from startup one to startup two: make sure that you have exactly what people want. The next important aspect that I think new founders tend to neglect is the importance of distribution. The hardest battle you'll fight is actually not product or hiring. It's getting a large enough base of people who care enough about what you're doing to pay attention.

While starting your first company, you lived out of your Honda Civic in San Fransisco. How did you gain the courage to start your second company after that experience? What made you want to quit your job and build again?

After the first company, I was pretty burned out. I just wanted stability. We never really had any major revenue at that company. There was the seed of some interesting stuff and we raised money, about half a million dollars, but we were never really at escape velocity. I wanted to know what it felt like to work at what I considered to be a real company. I went to Silicon Valley and I worked with a company that was well-funded and backed by YC. They were off to the races, and I learned a lot there.

Frankly, my biggest lesson was that it was a hundred times easier to sell something people want than it was to try to sell a subpar company. The Paul Graham-ism of “make something people want” is completely true.

How did you get your first hundred students at Lambda School?

For the first hundred students we taught free classes. That was another lesson I learned from a company that I went to. I actually recommend that if you can, you should work at a company before you start one.

We've found it's really easy to give things away. Anytime I'm looking for a user acquisition strategy, my first thought usually is: what can you give away? What can you give value for? Then you just have to let the word spread that you're giving value for nothing. We started out by teaching free classes and posting to Facebook groups and Reddit. We got thousands of people signed up. In the classes we tried to convert participants into full time students and charged each student $10,000. A few people joined and that was enough to get us off the ground.

From the employer side, it started out by equipping students to be great. Students brought in the first employers and when they would get hired, we would then reach out to those employers and try to deepen those relationships. That's part of the strategy to this day. Just let the network grow.

What's the biggest risk you've taken so far building these two companies?

I think the biggest risk with Lambda School was the zero money down, no tuition upfront model. No one had ever really done that before. There are a variety of reasons that's risky. Will students actually repay? Will that bring in students who are dedicated enough? The filter in the past has just been someone who writes a check, so we had to design a new method of admissions and create a new payment infrastructure. We felt the need to try the no upfront tuition model, and it turns out that that was a pretty good bet.

How do you fund the tuition of Lambda School students?

When we started out, it was all funded by Lambda School. As we got bigger, it didn't make sense to fund every student's tuition with equity dollars from our investors. Currently we use a model where we fund half the tuition and an investor funds the other half. The investor has a preferred return so they get their money back first.

This model has worked out well. We still put in our own equity dollars, so we have skin in the game, and the investors know they’ll get their money back first.

Considering the likelihood of this fall semester being held remotely, what do you think the future of education is?

I think we’ll see education being unbundled. In other words, you used to enroll in a university and it would do everything. Now, I think you're going to have many smaller, separate experiences. This could take many different forms. For example, it could be something like a master class to learn from celebrities, or Lambda School to get a job. I also think there may be a new model for liberal arts as well.

The educational experience is going to get pulled apart. Instead of one college education experience, you're going to have a dozen different things. I'm biased here, but that's my assumption.

What is the biggest mistake you made growing Lambda School?

Our biggest mistake was trying to do too much too fast. We expanded into Africa and Europe and we had all sorts of curriculums, UX and iOS and Android. It didn't allow us to make the product really great because we were focused on too many things at once. After a while, we had to slow down and take it one step at a time. I wish, in retrospect, that we had just focused from the beginning on making Lambda School absolutely incredible as opposed to trying to do more.

What do you think the future of software is?

I think what we'll see software becoming more and more accessible over time, in terms of people who are able to build it and things we are able to do with it. One of the things that I really hope comes true is that we learn how to do more with atoms as opposed to just bytes. I hope that software leaves the world of what we think of right now is software and enters more of the so-called physical world. But we'll see.

future of education with Allyson Dias, Program Director of the Thiel Fellowship

Group Photo of 2013 Thiel Fellows

Higher education is not a good fit for everyone. Allred is looking to build the vocational school of the future. Allyson Dias, Program Director of the Thiel Fellowship, is helping passionate students drop out of college to pursue their ideas. This week we interviewed Dias and got her thoughts on the future of education.

What is the Thiel Fellowship?

The Thiel Foundation, started in 2011, is Peter Thiel’s philanthropic vehicle and his answer to the notion that there might be an education bubble. Student debt is in the trillions, yet many young people think that higher education is the only path to a high-paying job. We at the fellowship give grants of $100,000 to students 22 and younger to drop out of school and work on a company or project.

What do you look for in Thiel Fellows?

We're looking for ambitious, self-motivated, self-starting young people. The candidates that we choose see a problem in the world and want to create the solution to that problem; they’re working towards building the future.  

Fellows can look very different. We have some fellows that have started nonprofits. We have crypto fellows like Vitalik Buterin, who started Ethereum. We have a lot of computer science and software fellows, as well as hardware fellows.  Additionally, we have science fellows like Laura Deming, who is another well-known fellow that has been working on longevity since she was a young girl.

What attracted you to the Fellowship's mission?

In 2012, I read an article in The New Yorker called “No Death, No Taxes” that profiled Peter Thiel. At the end of the article, he describes starting the fellowship to pay college students $100,000 to drop out of school and work on what they cared about most. I remember thinking that this was the best idea.

I saw a lot of flaws in my own education. I went to university in the US for one year and was very dissatisfied. It didn't feel like I was learning or getting closer to figuring out what I wanted to do in the world.

So, I dropped out and I re-enrolled at a university in Europe. It was really wonderful to travel, but I saw a lot of the same flaws in my education. Everyone was still on the same hamster wheel. The focus was still on rote learning and memorization, and I wasn’t any closer to figuring out what I wanted to do. 

All of the graduates were being funneled into investment banking or consulting. I didn't know what I wanted to do, but I knew that I didn't want to do either of those two. Ultimately, I dropped out of a master's program because I didn’t want to go down that path. 

When I heard about the fellowship, I thought it was such a great idea. Of course you should be paying great young people to be working on what they're working on! I think this kind of a program could be even applicable across different industries. I imagine the model itself of this $100,000 grant would do really well for artists, musicians and the places where a lot of creativity comes from: the youth.

Another thing I love about it is that young people have been somewhat disenfranchised in our society, and to give them this opportunity to create and to build is so significant. It’s amazing to tell young people that you believe in them instead of saying, “oh, you're too young. You don't get it. You won't understand.” To me, being open to creativity coming from any source is very exciting.

What do you think the future of higher education is?

I think right now is a pivotal moment with COVID-19 and universities deciding whether or not to hold in person classes. We're seeing students questioning if it's worth paying $50,000 a year in tuition for online classes taught via zoom. I think that the current situation is accelerating the decline of the university. I'm not sure what the future of higher education will look like but I imagine that the tipping point will involve student debt.

One of the best things about the university is the access to resources.  Students get the knowledge capital of professors and other students. The university’s resources undeniably add value, but we’ve been seeing tuition rates skyrocket over the last 30 years, outpacing inflation. This access will still be desirable to people, but I don’t think it will necessarily continue in its traditional format. 

I'm curious what direction this could take. For example, maybe there'll be different models of higher education that are two years or one year, and the program ends up being more project based or culminates in some sort of contribution that's directly relevant. This is something that you don't really see in degree programs right now. Instead, you see this kind of fake structure with tests and a thesis and a GPA that isn’t directly relevant to applying your knowledge in the real world.

I also wonder if there'll be some sort of education component brought in within private companies. It might be worth it for bigger companies, like Google or Facebook or even Netflix, to have their own version of an education model. If they could teach or train for three to six months and start building out their talent pipelines, that could reduce costs for their hiring. I think this is a possibility as well. 

Overall I think part of the problem with universities is that we've siloed this age range of 18 to 22 year olds away from the rest of society. In the future I’d like to see more of an integration with society, the way that it used to be in the past with practices like apprenticeships. 

This could take the form of vocational school. Society needs electricians and plumbers, and people with those skillsets make good money without needing a college degree. This could also work for Computer Science. Even with computer science, engineers learn a lot from working at a startup or a company. Oftentimes, they learn more from work experience than they do from their degree.

For our readers that are currently unhappy in college and thinking of leaving, do you have any advice?

I think it's important for young people to think about what's right for them, what they care most about and how they’d like to see the world. You don't have to be a tech founder to do that as well.

One of the things that I think is wonderful about universities is this access to so many topics and subjects. When I talk to people that are in school and not happy, one of the first things I recommend is to go take a class that you've always wanted to take. Go take a pottery class, go take a music class, just try something new. Because the barrier to entry is so low in university, exposure to many different things is so accessible and I think that's part of the learning and part of the experience.

our take on future of education 📣

In the future, AI will dominate almost everything; the one thing that it will not dominate is self expression. Over the next twenty years, we predict the value of a liberal arts education will increase while the value of STEM degrees decrease.

Allred is building a vocational school for highly skilled workers. Lambda school graduates learn current technology (unlike Universities who are still teaching Java) and have a career center that advocates for their students and helps them get hired. With Lambda School, the incentives are nicely aligned between student and school.

For many students, Lambda School is a better fit than an Undergraduate CS degree. Lambda School students graduate have a similar (if not better) understanding of CS, spend fewer years getting a degree, and have significantly less debt.

As Allred’s team launches more specialized degrees, an undergraduate degree in CS will began to look ridiculous. In. theory, even if a student desires to pursue a masters or PhD in Computer Science, they could enter a masters program after attending Lambda School.

We predict the value of a liberal arts degree will rise over the next twenty years. A liberal arts degree focuses on the clear expression of ideas. Unlike a CS education which can be condensed into an online bootcamp, the value of the liberal arts education is seen in small in-person seminars where students and professors exchange ideas. We would like to see universities invest in their English, Philosophy, and other liberal arts programs. Unlike STEM programs, the liberal arts experience can not easily be replicated outside of the university setting and the soft skills cultivated will not be made obsolete by AI.

next week…

Next week’s issue will feature an interview with Shak Lakhani (co-founder of Avro Life Science), Josh Clemente (founder of Levels), some more remote jobs and a pre-seed deal.

As always, we’ve included some weekly tech news and job opportunities in tech and finance. 

viral fundraising: Danielle Baskin 🦄

Danielle Baskin is the founder of DialUp, a new audio social network. Her past projects include VC Trading Cards and Branded Fruit for conferences. Most recently, she was interviewed on the Today Show and Colbert’s Late Show for selling face masks printed with memes.

Baskin took a unique approach when raising her most recent round for DialUp. Instead of asking for warm introductions to investors, she created a chain email where participants had to forward the email to 5 investors to get a meeting. The email chain went viral.

You launched your current round of fundraising with a viral chain email. What was the motivation behind the stunt and how did it go viral?

While raising our most recent round at DialUp, I was getting intros to investors from friends. I found this kind of boring and didn't want to keep asking for introductions. Of course friends are happy to do it, but no one likes asking for help and asking for an intro. It's such a burden. I also didn't want investors to meet with me out of obligation.

I wanted to spam all potential investors, tell them what I was working on, and gauge interest. That’s when I thought of the chain letter.

I tried to replicate that style of old chain mails from around 1999, even down to the multiple fonts and gifs. I actually looked at a lot of websites that had archives of chain letters and tried to imitate the language with phrases like, “it might sound ridiculous, but it really works,” and other things like that.

I thought the email served the purpose of reaching the investors, but I didn't know what would actually happen when I sent it. I thought, well, if this goes nowhere, at least I'm entertaining the friends I sent it to and they can see my “I'm a joke” fundraising strategy that I'm done with intros, let's just do chain letters. However, people actually forwarded it.

I sent it to five or six friends and founders. I didn't directly send it to any investors. It kind of just spread from there. When you have one note and then you send it to five people, and at least one of those people sends it to five others, it branches out pretty quickly. I think it reached over 500 shares within the first two weeks when we were doing analytics on it. Many investors reached out without even seeing the deck.

There actually was no deck. It was just about creating a sense of intrigue, which I guess says a lot about how investors are motivated.

Before an investor takes a meeting, they want to know that that person is validated in some way, that they know someone who knows someone. Because investors were receiving this list of familiar names on the list of forwards, they said, “Oh, this must be legitimate.”

I think our conversion rate was 12%. In order to get the followup, you had to have followed the instructions and forwarded it five times. I didn’t send a followup email to just anyone who received the chain letter. You had to earn it.

If you did, you got a link to a mysterious video telling you to book a meeting on Calendly. We got over a dozen meetings and are still in conversation with some of them, which is interesting because we never directly reached out to any of these firms.

What is your secret for going viral?

I do weird stuff all the time; it’s just in my personality. I've been doing this since I was a teenager. I try to make content that entertains me or my friends and it often gets introduced to a lot of people on the internet.

I feel like this is just a trend with things that I create, but I've done a bunch of different stunts. My goal is to create funny content. An example of this is my project from a few years ago, VC Trading Cards, which was featured in TechCrunch.

For every investor that I included, I put their number of exits, number of investments, a list of notable companies, their hometown, when they were born, and a fun fact. There’s a set of five random cards in each pack, but the most difficult part is that they all have stats. It was actually a lot of work to make. I won't reveal how many there are, because each pack is different, but there's a lot of cards.

Finding facts could be difficult. I would usually start by reading bios on Crunchbase or looking at Twitter. I thought it was interesting to think of investors as baseball players, sort of like you’re cheering for them.

I don't think there is a fantasy sports culture around investors. If there is, I'm not aware of it. But there definitely is a culture of following VCs and getting points for their thoughts on Twitter.

Tell me about the future of DialUp and your take on audio social networks.

I'm definitely more interested in intimate conversations that let people have a platform if they don't already have one. I don't really want to cater towards people who already have a following on YouTube or Twitch or Twitter, so that they can have yet another following on a different platform. I’d love for DialUp to be a place where someone can have this type of nourishing conversation without being a celebrity. So that's how we're different from other social networks. Of course, our product is still very young. I think that there's experiments to be had with both listening and with larger group calls, but a core focus of our app is not FOMO. In that way, it’s not the same as typical social networks. We have different values than Clubhouse, which will shape our product decisions.

What is the biggest risk you have ever taken?

I think it's kind of interesting that people think that starting a company is a risk. You can set up an LLC in a day and it doesn’t cost much; it’s totally possible to start an alternative career without quitting your job.

I think something that I do that is risky is that throughout different periods of my life, I've never had a full time job. I've never had any financial stability besides relying on my own projects.

I've had moments in my life where I've had $0 in my bank account and I'm still trying to sell whatever product I'm selling. I’ve declined paid opportunities because I thought my company would work.

Of course that's a total risk. I think that your level of risk is dependent on your personal situation. For me, it didn’t feel like that serious of a risk because I don't have a family and I don't have people reliant on me. I'm very lucky that I just have to focus on myself. I think that risks come when you're financially responsible for other people or employees.

The riskiest thing that I've done is putting myself in danger of physical harm trying to explore an abandoned island, but that's a long story and has nothing to do with entrepreneurship.

rapid fire with Sahil Lavingia🔥

This week we sat down with Sahil Lavingia, founder of Gumroad, second employee at Pinterest, and angel investor in Lambda School, Figma, Clubhouse, and others.

How did you get your first hundred customers?

Cold emails. I just cold emailed every single one of them.

What is the biggest risk that you have ever taken?

I don't know if I've taken any big risks. To be honest, I don't think I'm a risk taker in the sense that I don't do anything that's dangerous. All the risks I take are just time risks; it's just opportunity cost.

I haven't lost any real money. I've done some angel investing but again, never investing more than I could afford to lose. So it doesn't feel like much of a risk.

What do you think the future of software is?

Honestly, I can't tell you, because I'm trying to invest in it and I don't want anyone to know.

Read more about Lavingia here.

our take on audio social networks 📣

The past few weeks have been dominated by news of Clubhouse, an invite-only audio-based social network. Lavingia is an early investor in Clubhouse and Baskin’s startup DialUp is one of Clubhouse’s many competitors.

Clubhouse’s value add is that it distributes unfiltered conversation and provides spontaneous discussion. Figures from Marc Andreessen to Oprah contribute to Clubhouse discussions.

A few weeks ago, we reported on a leaked Clubhouse conversation. This incident is a sign of issues to come with scaling the platform. Clubhouse’s value is privacy. If privacy is infringed upon, users will be afraid to create content on the app. Controversial and difficult topics cannot be explored if users are afraid of being misquoted.

As a result, the thoughts and discussion of industry leaders will retreat into private invite-only rooms in the months to come. More public conversations will be held in listen-only rooms, turning Clubhouse into a live podcasting platform.

We predict that Clubhouse will become a community discovery platform. Clubhouse will curate invite-only rooms and create tools that help users find likeminded people, similar to LunchClub’s matching algorithm.

Clubhouse’s main competitors are neither Twitter nor Instagram; they are actually Discord and Lunch Club. Discord, and Slack currently host many online communities. However, these communities are difficult to locate and moderate. If Clubhouse can perfect community discovery and matching, it will become the home of discussion for internet communities.

things we love💜

📱App - Plan is part calendar, part project-manager. This app automates work life so you and your team can work less and do more.

📰Newsletter - 🦄 Unicorner is the premiere newsletter for learning about hot startups in dozens of different industries. Subscribe here to receive 3 startup rundowns each week.

🎤Event: InternCon 2020, the largest conference For and By interns. Come hear from 25+ speakers from top companies and venture funds for a day of learning, networking and socializing. Get your ticket at internconference.com

tech and finance internships

Exclusive opportunities curated for Open Water Weekly Subscribers. Subscribe today to receive founder interviews and job opportunities delivered directly to your inbox.

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next week…

Next week’s letter will feature an interview with Austin Alfred, co-founder of Lambda School, and some more remote job opportunities.

As always, we included some weekly tech news and job opportunities in tech and finance.

founder interview: building Interviewed 🔨

This week we sat down with Interviewed.com (acquired by Indeed in 2017) co-founder Chris Bakke. Previously Bakke was COO of 42Floors (acquired by Knotel), managed business development for Zillow (NAS $ZG), RentJuice (acquired by Zillow).

This interview has been edited for length and clarity.

What do you know now that you wish you knew when you started your company?

I think the biggest thing is to play long term games with long term people. This is a frequently quoted idea in VC and startups today, but I don't think it was as obvious to me when I first started out 10 years ago. 

I think that this can take a lot of different forms. Typically, the two places where it's going to matter the most will be the people in your early team (co-founder and first 20 people) and your investors. 

Many times, I've been in scenarios where people’s skills seem like a fit but they don’t quite click with my team. It’s tempting to only consider their skills and think that you can make the personalities click or the culture work. 

However, this is ultimately a mistake. If you run a company well and it becomes big, it can go for over 10 years. If that person is a bad actor, you have to deal with them for the lifecycle of the company. I think the same idea applies with venture capital. Investors and angels are more than just money; they’re also people that you have to work with.

That's probably the big lesson as I head into my eleventh year in startups. Before going into business with someone, it’s important to assess not only their skill sets but also how well they get along with you and your team.

The question that I always ask myself is whether this person is someone that I want my name, my team’s names’, and my company’s name to be associated with for the next decade plus, because that's the reality of startups.

How did you get your first 100 customers? 

It was a grind. For context Interviewed was selling assessments and recruiting solutions to enterprises (large insurance companies, tech companies, outsourcing companies, etc), some mid market and a handful of SMB customers. 

I got the first $2 million in revenue myself before we ever hired a salesperson, and that was critical. I think that as the founder of a company, getting your first hundred customers is one of the most important things you can do. I don't know many-if any- successful people that outsource this piece. It's kind of like building a team. You want to be super hands on with hiring, customer development, and finding product market fit. 

We had a broad approach at Interviewed, which I think is extremely important. Most founders pigeonhole their idea way too early. This can take a lot of different forms; you can pigeonhole yourself on price, geography, industry, and target customer. 

One question to ask for a B2B product is who will be purchasing it. In our space (recruiting), we ask: is a CFO, a hiring manager, or a head of recruiting more likely to buy the product? My thesis was that we don’t have the answer, so we're just going to try to appeal to as broad of an audience as possible.

For the first two years of our company, every business day I would pick one to five companies to cold email. I would scrape emails from LinkedIn and email 20 to 30 people within each company. My goal was always 20 cold emails a day and in a lot of cases it was more like 80 cold emails per day.

If you work at a large company, you need some signals. If somebody asks, “Hey, have you guys heard about Open Water? Have you heard about this guy David?” The last thing you want is for everybody in the company to say no. So a lot of times what would happen is, I would pick one company, I would send a minimum of 20, probably a maximum of 80 emails, all semi or fully customized, and I would send them to almost everybody in the C suite (CEO, COO, CFO, or chief HR officer). I’d also email any VP that I thought was relevant and as many recruiters and HR people as I could find.

 And what you would say is something like this:

Hi X,

My name is Chris and I'm the founder of Interviewed. We have a super exciting way for companies to assess the skills of people who are applying to their jobs. Can I show you a demo? 

In many cases, especially for our first hundred customers, the response would be, “I don't know, I've never heard of you. But it's weird. My VP forwarded me the same email.” And so you hit these companies from every possible angle and people forward the emails and say, “Have you heard of these guys? Maybe we should talk to them.”

Our conversion funnel from those cold emails was to then get people on a demo. In general, we would do as many demos as it took. Our first 10 customers paid us between $300 and $500 per month. As we closed in on 100 customers, we had customers paying us $30,000 for the same product for the same platform- it was just a much bigger use case. 

When we looked back across our first hundred customers, we found something crazy. When we looked across our first hundred contracts, or first hundred customers, we found that we sold to recruiters, VPs and C Suite executives. If we had pigeonholed too early and said, “we're a recruiting tool, and we're only going to target recruiters,” we would have missed out on a ton of potential buyers and eliminated the opportunities we had with CEOs, CFOs and VPs of engineering and sales. 

So our early process was just taking it one day at a time. We’d get contact info from LinkedIn and cold email a ton of people in the company. Then we’d move on to the next company the next day, and while we were doing that just try to get really good at demoing. That was how we got our first hundred customers and grew to ~$2M in ARR.

What is the biggest risk you have ever taken?

My biggest risk will be starting my next company. I'm in an executive role at a publicly traded company (Indeed). I'm giving up a lot of compensation and other benefits to go make zero dollars again. 

Before I started Interviewed, I was the COO at a well funded Series B company. It was a big leap of faith to go off and become a founder, and I’m glad that I did it. I’m very excited to be doing it again.

What has been your biggest mistake?

My biggest mistake has been hiring executives too early. This speaks to off loading responsibilities. Some people are really good at this. They naturally think about how to build a machine and are very hands off from day one. 

If you’re building the machine and you are the machine, then you're the one sending all the cold emails, doing all the recruiting, raising all the capital, writing all the code, whatever it is. This can be very hard. I don't actually think it matters whether you're a perfectionist or you're super hands off. If you're running that machine, it's very hard to take some of the things that you're doing and hand them off to people.

This mistake is very expensive. In a seed stage company, if you find an awesome VP (engineering, sales, etc), you're going to have to give them a significant piece of the company (anywhere from 0.5%-5%) in addition to paying them a crazy salary to convince them to leave their previous job. 

In a lot of cases, I brought on senior and expensive people to do stuff that I was tired of doing. It wasn't because I was ready to hand it off or because I should have handed it off. It was just because of feeling, for example, that I would love to have a VP of Sales sending cold emails for me so that I wouldn’t have to. 

And it goes back to my earlier point: startups are a grind. You're gonna have to do a bunch of stuff that kind of sucks for a very long time. But it's way better if you do it yourself for as long as you can. 

At Interviewed, our founding team wrote down a principle: Don't hire until we're bleeding. 

This principle extends beyond startups. I still use it to manage my team at Indeed, a company which is doing billions in revenue each year. I think it's a principle that works at a two person startup and at a 12,000 person company like Indeed. Adding more people early on, especially more senior people, does not always fix all of your problems.

So that's been a mistake I’ve made, where I’ve brought on senior people too early and expected that they'd know how to scale. There's not a lot of information out there about how to do these handoffs or when to bring people on, and it varies from company to company. But I think that that's been something that I've certainly learned the hard way many times.

What do you think the future of software is?

Although there are outliers, in our entire lifetime software has been built for buyers and not for users. If you look at the offerings that Oracle, IBM, and some of these large software companies provide, there's something for everyone. The reason that there's something for everyone is that they're trying to pigeonhole one product and make it as vague and as wide spreading as possible. 

Oracle’s only hurdle is to sell software that somebody will buy. They don't actually care about the user, because they are locked into a 15 year contract. Their goal is to sell to every company, but the software’s not specifically adapted to the needs of each company.

I think that designing and selling software to individual users, and then having a bottom-up consolidated approach is very compelling. It has been done very well in the last five or 10 years by companies like Slack, where you and I can just go buy an individual slack license. We don't need permission, we can use it for free, and then eventually if things go well, we can consolidate up. Services like Dropbox and Google Docs are like this too. There's a bunch of these companies, but there's still way more companies that are selling to buyers versus users. I think in the future we'll see a giant shift in this approach. 

The other thing is that I think there will be a general lack of innovation over the next 10 or 20 years, but I think that that's actually okay. I think what people are starting to realize, especially bootstrapped founders and founders that take small amounts of capital, is that you can take a piece of software or an offering, just tweak a couple things (price, distribution model, a feature, or even language), and sell that product. 

I think when we look back in 20 years, we'll think, holy cow, the world is wildly different than it was. But in contrast to these groundbreaking releases, it's possible to make a ton of money and create a ton of value by taking things that already exist and tweaking them to make them 10% better. This creates far more value than just a 10% improvement. So I think that the two biggest themes in software over the next decade or two will be bottom up adoption and small, iterative tweaks.

news 📣

OpenAI’s GPT-3 model was released to group of beta users. GPT-3 is the most advance natural language processing model ever created. One use case of GPT-3 is to generate code from natural language.

Our Take

We expect GPT-3 enabled startups to eclipse no-code tools like Bubble. No-code tools (Wix, Bubble, etc) allow makers to create websites without knowing how to code. These tools let makers iterate quickly but are limited in terms of functionality.

GPT-3 opens up a new category of “low code” tools. A future low code tool could take a wireframe design and a written description of an app’s functionality and then generate the web app’s source code.

There will be always be a place for no-code, but we think no-code is over utilized. We believe that many no-code users would be better served by an AI enabled low code platform and a few hours of work from a freelance developer.

things we love💜

📱App - Glimpse (YC W20): We use Glimpse to power networking events at Open Water. Glimpse is an app for 2 minute, 1-on-1, video chats. You get matched back-to-back with the people in your room, recreating the feeling of being together with a group.

📰Newsletter - The Hustle: The Hustle is a daily newsletter that breaks down the most important trends in the industry in an easy to digest email. They pair their industry knowledge with a sense of humor that makes their newsletter always fun to read

🎤Community Request: Open Water is thinking of building a community for students interested in taking a gap year. If this sounds interesting, please respond and let us know what you would like from a remote gap year community.

tech and finance internships 📈

Exclusive opportunities curated for Open Water Weekly Subscribers. Subscribe today to receive founder interviews and job opportunities delivered directly to your inbox.

Subscribe now

next week…

Next week’s letter will feature an interview with Danielle Baskin, co-founder of Dial-up, and some more remote job opportunities.

As always, we included some weekly tech news and job opportunities in tech and finance.

founder interview: building Thinknum🔨

This week we sat down with Thinknum Co-Founder Justin Zhen. This interview has been edited for length and clarity.

What is Thinknum?

Thinknum has been around for six years now. Our product has been live for four years. We organize and retrieve public data from the web. We track data points such as store locations, product pricing, or job listings. We then sell this data to over 270 corporations who want to understand the market better or invest more intelligently. We've raised $12.6 million in total, and we currently have a team of 33 people.

What do you know now that you wish you knew when you started your company?

I mean I didn't know anything when I started the company. (Laughs) I think the main lesson indirectly is you need to spend money to make money. I personally come from a really humble background. We didn't eat out at a restaurant for over 10 years, just because you know, we couldn't afford it.

So when starting a company, when you're figuring out how to acquire customers anything that costs more than say a few hundred dollars, to me personally, it just seemed like a lot of money. And at first, I was very hesitant to experiment with user acquisition tactics.

It took me a while to learn. We're trying to sell a product to a very high-end audience such as investment firms and directors of large companies. You need to meet these people in person, sponsor conferences, and hire people to do content. So you really have to spend money to make money. And after I internally accepted that, we really started taking off from there.

How did you get your first 100 customers? 

When we were trying to get our first 100 customers, we didn't have much money. What we did have was a product that was pretty unique. We were fortunate to have gone through a school where a lot of the alumni happened to work in our target market.

Our target market was professional investors like hedge funds, private equity funds, and traditionally Princeton University, that's where we went, they sent half the graduating class to that sector.

We did a lot of cold outreach. We said, “We're a couple of guys from Princeton, we built this product, which we think is unique and can benefit your process. Can we please take five minutes and show it to you?”

And so they took it. When we were showing them the product our first couple calls in, something went wrong. So we would do a call and then we would fix the bugs overnight. Then the next day, we would hop on the phone and do it all over again.

What is the biggest risk you have ever taken?

This is a little more fun and one of the more popular stories about us. So about four years ago when we first started the company, we were working out of a WeWork. Our team collected data on the public web that contained information on when customers joined WeWork and when they left. It was a pretty rich data set.

So we looked at the data and saw what we could learn. We found that the churn rate had been increasing over the past few months. We also found that they built this social network that almost none of their users used. They were raising capital with a technology company valuation and the social network was the pitch. So we blogged about it and then Reuters wrote about our blog post.

The next day Adam Neumann, who was the CEO at the time, personally evicted us and gave us 30 minutes to leave our office.

Obviously, WeWork kept raising more money at higher valuations over the next couple of years until recently, when our blog post was proven right. Our data found the flaws in their business model.

Now we're getting asked to speak in documentaries and movies about WeWork and we are glad to be vindicated. But it is still the biggest risk we ever took because we had to physically leave our office to do so.

What has been your biggest mistake?

I definitely made a lot of mistakes. In general, I think one mistake that we made is not letting go of someone fast enough. I think when you're a founder and you spend so much time with your team, you start to see each other as friends.

If someone is not working out or is not the right fit, in your heart you know that that it is not a fit, it could still be difficult to actually pull the trigger. In general, the biggest mistake I think would be letting someone who you know is not the right fit stick around for too long. And honestly, it's not good for them either.

Who did you look up to while growing up and who inspires you today?

This is easy. While growing up, there was Michael Bloomberg. I grew up in New York, and he's the most successful entrepreneur out of New York. He also happened to do it in in our industry, selling data to investment firms. Today, obviously, I still look up to Michael Bloomberg.

I also look up to a lot of serial entrepreneurs. Building one successful company is extremely hard, building multiple is even harder. The press loves Elon Musk and gives him credit for all the incredible things he does, but another founder that sometimes slides under the radar a little bit is Jack Dorsey. He built two $10 billion companies and he did it very quickly. Obviously, building a company is extremely hard. Doing it twice? That's who I really look up to.

What do you think the future of software is?

I'm going to comment on what I know best, which is the future of data. I think there is going to be a lot of consolidation in the big data analytic space. There's a lot of tools out there and every tool does something well. Ultimately, I think the end users will want to see all of this data and analytics in fewer platforms. I’ve gotten this feedback from other funds we work with.

I also think there is going to be a lot more data. Internet of Things will finally start to move over the next few years. We have been talking about this for along time now but I think it will finally happen.

Finally, and this is very specific to us, there's more companies that are going online (pharmaceuticals, trucking, etc). When that happens it provides more data to the index and gives us better indicators for those sectors of the economy.

news 📣: twitter fight edition 🥩

The Players:

• Dr. Balaji S. Srinivasan: Former CTO of Coinbase and former General Partner at Andreessen Horowitz

• Taylor Lorenz: New York Times Style Reporter

Background:

Last week the New York Times was writing a story on psychiatrist and blogger Scott Alexander and refused to conceal his identity. Revealing Scott’s full name would interfere with his treatment of mentally ill patients. In response, Scott deleted his blog and Silicon Valley called for a boycott of the NYT.

The Fight:

Earlier this week Taylor Lorenz, a NYT style reporter, tweeted this.

Dr. Srinivasan felt that the tweet targeted female founders so he spoke out. A Twitter War ensued with participation from many influential VCs.

The war culminated with Srinivasan calling Taylor racist, a meme bounty for memes against Lorenz, and a potentially illegal leak of a Clubhouse conversation where members expressed their displeasure with the press.

Lorenz’s Side of the Story (Twitter Thread) and Srinivasan's Side of the Story (Twitter Thread)

tech and finance opportunities

Exclusive opportunities curated for Open Water Weekly Subscribers. Subscribe today to receive founder interviews and job opportunities delivered directly to your inbox.

Subscribe now

next week…

Next week’s letter will feature an interview with Chris Bakke, founder of Interviewed (acquired by Indeed) and some more remote job opportunities.