Who Are the AI Champions?

# Who Are the AI Champions?

Intelligence needs to be metered, funded, built, distributed, and made cheap enough to deliver a Human Dividend. Who champions that?

### Who Are the AI Champions?

If you are in the habit of watching Andrew Keen and myself discuss this newsletter each week you will know that we have focused on trying to understand why the AI argument keeps arriving in negative form. The video is quite passionate and we often (well always) disagree so if you don’t watch, give it a try.

To be sure there are bad actors in AI. There is fraud. There is deception. There is spam, theft, impersonation, non-consensual imagery, fake books, fake sources, and bad conduct by platforms that should know better.

Those things do explain why there should be a concerned lobby, but, for me at least, not why concern should spill over into opposition to AI.

Illegal use of AI by bad actors should be dealt with like any other crime, directly, using law enforcement. Use copyright law. Use consumer protection. Use platform rules. Use disclosure where disclosure matters. And treat them as human crimes, not AI crimes.

I raise this because it perplexes me. Being against AI because some people misuse it makes little sense. We do not make electricity worse because criminals use power grids. We do not slow compilers because malware exists. We do not make browsers less capable because fraudsters build websites. The right answer to abuse is enforcement against abuse, not scarcity for everyone else.

The positive case AI for AI rarely leads the zeitgeist. The negative seems a lot louder than the positive. Where is the AI champion who can explain why the effort is worth the cost?

The news-led entry point this week sees Stripe stepping up to the role of champion. It positively flaunts its acquisition of Openrouter as evidence of enormous upside as AI rolls out. Not only its own upside but all of our upside.

In Alex Wilhelm’s reading of Stripe’s leaked OpenRouter memo, intelligence is “expensive, heterogeneous, and constantly changing.” That is why it has to be metered.

Metering intelligence sounds spooky. After all the word sums up the collective human evolution of thought and the application of thought. How can it be metered? Unless it can be owned?

The Stripe founder is really making a case for a fee to be charged for accessing intelligence. The fee has to pay for training, packaging, fine-tuning, hosting, serving. It is another way of saying that the companies investing in distributing intelligence need to get paid, and that customers need to know what they are buying, which model is doing the work, which task is worth doing, who pays, and when. So… not sinister. Just, if I spend capital to make your life better, I need to get paid. What is being metered is not “intelligence” but servers and software that are the means of accessing it, just like we pay a toll to drive on a toll road.

OpenAI is making the same point from the frontier-lab side. In the Core Memory interview, Sam Altman says the industry has not filled in enough of the “dot dot dot” between superintelligence and ordinary human life. People want prosperity, agency, meaningful work, and a future their children can live in. Greg Brockman says the models have shifted from being the product to being part of the product. The real work is the body around the brain: agents, skills, connectors, computer use, context, memory, and trusted personal systems that can act for people. These too have to be paid for.

If AI is a tool for lifting us up, then the thing to champion is its extension to more of us.

That is why the low meter matters. Azeem Azhar’s $6 agent is not a small anecdote. It is a sign of what happens when intelligence can be routed, bundled, escalated, and cost-managed. Cheap intelligence is not the enemy of revenue. It is how use explodes. Low cost is the primary driver of inclusion.

So, to champion a bit myself, metering is good. Data centers are good. Investment is good. Centralized frontier labs are good. Edge and local models are good. Product discipline is good. The culprit is price, it is bad and needs to get lower.

Those sentences are unfashionable, but they should be the center of the anti-AI argument. Not, we don’t want it but we want it for everybody. The transformational impact of AI on our lives demands that.

If intelligence is going to become a basic input into life and work, it cannot stay scarce, expensive, suspicious, or morally contaminated. It has to become cheaper, more available, more accountable, and more useful. That requires capital. It requires chips. It requires data centers. It requires power. It requires open models. It requires routing markets. It requires local inference. It requires companies that can capture, package, serve, meter, improve, and sell intelligence at scale. It even requires huga amounts of debt, borrowed by those building the capability.

And all of that also requires people willing to say so.

### The Champions

So who are the champions we can look to for the arguments?

There are really two groups here. Champions make the public case. Intelligent Architects make the case true.

The first group says we should build, fund, meter, distribute, and cheapen intelligence. The second group designs the loops, rails, identity systems, markets, security models, energy systems, and edge deployments that let intelligence work in the real world.

Sam Altman, Greg Brockman, and OpenAI: the product-and-access champions.

OpenAI’s enterprise revenue crossing consumer revenue, The Defender’s Window, and the Altman/Brockman Core Memory interview all point in the same direction. OpenAI is trying to move beyond models as magic tricks and toward intelligence as a dependable product surface for work and life. That means agent platforms, personal context, Codex for more than coders, security workflows, and the discipline to make compute a product input rather than a complaint. OpenAI is a champion here because it keeps insisting that access, deployment, product, safety, and abundance have to be solved together.

Jensen Huang, Nvidia: the infrastructure champion.

Nvidia’s AI moat is shifting from chips to capital shows Huang making the hard case for the buildout. The article says Nvidia announced a Wall Street pact to pursue $500 billion of GPU financing and agreed to support OpenAI’s Ohio data-center project with up to $105 billion. That can be read as circular demand. It can also be read as the obvious next step in a market where AI factories need long-term capital before their customers have long-term credit histories. Huang is a champion because he makes chips, power, finance, factories, and national capacity feel like productive abundance rather than a hidden plot.

Patrick and John Collison, Stripe: the metering champions.

Stripe Acquiring OpenRouter, Aggregating AI?, Flipping the Business Model, Stripe wants to meter intelligence, and Ramp Launches Router.com to Become the CFO for AI Agents belong at the center of this issue. Stripe already understands payments, usage billing, subscriptions, and developer distribution. OpenRouter gives it a way to route demand across models and providers. Ramp points at the other side of the same market: companies need agents to spend with budgets, approvals, wallets, and audit trails. The champion case is not that intelligence should be expensive. It is that metering makes intelligence legible, billable, governable, comparable, and eventually cheaper. You cannot make a market abundant if nobody can see, price, route, approve, or pay for the thing being consumed.

Tomasz Tunguz, Simon Willison, Qwen, and Hugging Face: the edge champions.

Decentralization is not a replacement for large centralized companies. It is a parallel development. Tomasz Tunguz’s Birds Don’t Fly Like Planes. Neither Does AI. argues that local models can match cloud-model quality on venture tasks while taking a different route to the answer. Simon Willison’s Qwen 3.8 27B is excellent shows capable local open-weight models running on consumer hardware. Hugging Face’s State of Open Models shows Qwen’s downstream footprint across more than 151,000 derivatives. This is not anti-frontier-lab ideology. It is how intelligence diffuses: frontier systems push capability forward; edge systems bring privacy, latency, resilience, cost control, and ownership closer to the user.

Ford’s engineers and Brian Solis: the human-expertise champions.

Ford Rehired the Experts AI Was Supposed to Replace, That’s the Story is useful because it refuses the lazy replacement story. Ford brought back experienced people to mentor younger staff, lead design reviews, find failure points, and improve the information used to train and guide AI systems. That is what useful AI adoption looks like inside institutions. It does not delete human expertise. It makes institutional memory more scalable.

Li Bo and Jinguyuan: the ordinary-adoption champions.

A dumpling shop becomes a poster child of AI adoption in China is one of the week’s best details because it makes pro-AI adoption small, local, and practical. Li Bo built an AI skill so personal agents can check the menu, make recommendations, and join the queue. That is not a lab pitch. It is a restaurant owner seeing that agents may become part of daily life and deciding to be ready. The pro-AI zeitgeist may be buried because usage like this is racing ahead of ideology. You don’t need a zeitgeist when the case is self-evident.

Matthew Yglesias, Nate Silver, and local consent: the legitimacy champions.

Data centers are good, but consent matters. The English town with 40 data centres, Why does everyone hate data centers?, and Giving the people what they want (not data centers) show the hard part. AI infrastructure lands in towns. It changes power demand, planning politics, noise, heat, tax revenue, and trust. A champion does not wave those concerns away. A champion argues that the buildout is necessary and then makes the community bargain real: better tax structures, credible water and power rules, visible local gains, and no secrecy that makes residents feel tricked.

Nathan Gardels and Helene Landemore: the public-feedback champions.

AI Needs Public Feedback at Scale Before It’s Too Late is not an anti-AI piece. It is a legitimacy piece. If AI companies have two-way contact with hundreds of millions or billions of users, they have the first global deliberative surface in history. The champion’s answer is not to let politics freeze the technology. It is to use the platforms of distribution to collect public feedback, surface values, and make the product of intelligence more legitimate as it scales.

Carta, Augment, and the private-market builders: the liquidity champions.

Carta’s tender-offer update and Augment’s stock-token analysis show that financial structure is becoming part of the AI-era economy. Companies are staying private longer. Employees and investors need liquidity. Tokenized shares can be real ownership only when issuer participation, transfer-agent alignment, legal records, permitted venues, and disclosures line up. This matters because the idea I call the ‘Human Dividend’ is also an ownership argument. Access, productivity, pension exposure, and tax receipts are not the same as giving people an asset claim on the surplus.

### The Intelligent Architects

Not everyone who matters will become a public champion. Some of the most important people will be architects.

Esther Dyson’s earlier “.agent” instinct is a good example. If agents act in public, transact, represent people, and make commitments, identity and accountability have to attach somewhere. That is not a slogan. It is architecture.

This week has the same pattern everywhere. Tunguz and Willison are not merely cheering local models; they are measuring when edge intelligence is good enough, fast enough, and private enough to use. Hugging Face is not merely publishing open-model optimism; it is showing which model families become reusable substrates for builders. Greg Brockman’s Defender’s Window is security architecture: give defenders agents, context, skills, vulnerability backlogs, code-review hooks, and bounded triage workflows before attackers do the same. Ford’s returned experts are operating architecture: institutional memory wired back into AI-guided product development. Jon Ma’s Artemis is investing architecture: public, private, and onchain data pulled into models that can form a thesis and route execution. Gabriel Vasquez and Angela Strange’s borderless founder is company-building architecture: diaspora, local knowledge, Silicon Valley capital, customers, and talent loops as one operating system. Martin Varsavsky’s Preserving AI When the Grid Goes Dark is resilience architecture: preserving model weights, documentation, power, and recovery capacity in case the infrastructure around intelligence fails. SemiAnalysis on PJM is grid-market architecture. Apollo Atomics and home batteries are energy architecture. Carta and Augment are liquidity architecture.

Champions win permission to build. Intelligent Architects make the buildout usable, accountable, and cheap enough to matter.

### The Human Dividend

The champions should not be asked to apologize for building. They should be asked to make the case for building in full and as fast as possible.

Companies should own, operate, meter, and profit from distributed intelligence. Governments should not run the models. Committees should not manage the loops. But if intelligence becomes a foundational input like water, electricity, language, or money, then broad access and broad participation in the surplus become economic questions, not sentimental ones.

I worked with AI on my book this week, reducing 80k words to about 60k. Here is the essay that inspired me to do it: Intelligence: Who Owns it?, AI And Its Enemies.

The book - The Human Dividend - identifies two dividends.

First, intelligence itself has to become free or cheap enough to be a normal human capability rather than a luxury product.

Second, the economic abundance created by intelligence has to produce broad ownership of the surplus, not only access, productivity, tax receipts, or pension exposure.

AI needs champions because the critics have a simple story - this is changing things in a way I don’t like.

The builders do not but they can. The champion story is just as simple:

I want my children to have access to human intelligence at the start of their life. I want the same for anybody leaarning new skills. Why penalise them with limiting access, or slowing its progress?

Build more intelligence. Meter it. Pay the builders. Build the data centers. Build the edge. Build the power. Build the markets. Make the price fall. Punish abuse directly. Share the upside broadly.

That is how AI stops being a thing done to people and becomes a dividend from the intelligence people created.