Humans Create Intelligence

# Humans Create Intelligence

Subtitle: Now Everybody Can Access it (All of It) Source: https://www.thatwastheweek.com/p/humans-create-intelligence Published: 2026-08-07T22:02:01.779Z

## Humans Create Intelligence

Humans need AI. Why? Because it gives us access to intelligence and makes us better at our specific skills. Who wouldn’t want to be better?

Intelligence is the accumulated knowledge human beings have already created: language, science, medicine, law, engineering, art, software, companies, markets, and institutions. It is also not static. It grows every day through work, research, argument, invention, and use. Intelligence is not something AI invented. AI is simply a tool for making it available.

AI is a tool for distributing intelligence

AI is an asset because it captures that human-created knowledge, makes it usable, and distributes it back to people. It is not itself intelligent. It is humanity’s knowledge made more available to humanity. It is a tool, albeit a powerful one.

That is my starting point for understanding the developments I see all around me.

I assume we can agree that people deserve better access to intelligence. A student needs a tutor. A doctor needs a second pair of eyes. A founder needs a research department. A scientist needs help reading the literature. A creator needs tools that extend imagination into execution. A small company needs capabilities once reserved for large ones.

This weeks included pieces about AI agents - drug discovery, mathematical breakthroughs, self-improving systems, open models, data centers, power, memory, hyperscaler spending, Wall Street financing, and public resistance - all point to this human need.

Read together, they say one thing clearly: Companies are stretching themselves to build the AI infrastructure because the human need is real. Or to put it another way, the demand is real. Much as I may criticize details, I do want this innovation. And the resistance is a function of failed explanations. If people knew that the payoff is universal access to intelligence, and understood the consequences the protests would turn into support.

The argument for affordable and universal access to intelligence

To really change the world AI will need to be affordable and universal. Access is a key need. Human knowledge is universal in origin. AI should be universal in distributing intelligence. Access and cost are correlated of course.

Universal access has to be delivered globally to children everywhere from the moment they have questions. You can’t do that without scaling the infrastructure of AI. A tool used only by frontier labs, large companies, elite universities, or rich countries is not enough.

Access requires scale.

It requires chips, data centers, power, memory, networks, devices, interfaces, software, finance, and competition. Scale is not a vanity project. It is the condition of universality.

Because the human need is real, the physical buildout follows. Aggregated and distributed Intelligence needs chips, data centers, power, memory, networks, cloud contracts, land, water, finance, and local consent. The future of intelligence is an infrastructure story.

Scale is expensive but justified

This week Tom Tunguz’s “Spending Like a Hyperscaler” captures what that means. That is why the FT story on Google’s “$200bn Wall Street finance machine for Anthropic” does too. The New York Times piece on a “deluge” of AI computing power - ditto. The buildout is no longer just an idea. It is capital expenditure, private credit, energy capacity, optical networks, chips, leases, and long-term contracts.

The bill for distributed intelligence is huge. But the goal is even bigger. Every human gets access to our collective knowledge as the starting point for their life.

The goal is abundant intelligence. Faster science. Better medicine. More creation. More entrepreneurship. More agency in more hands. The infrastructure is justified.

But cost creates politics.

Infrastructure creates winners, losers, local burdens, financing risks, and institutional power. Communities ask who pays. Wall Street asks who owns the cash flows. Regulators ask who controls the system. The Futurism story about ordinary people being arrested while protesting data centers is part of the same story as the financing stories. Once the successful roll out of AI needs land, power, water, debt, and public permission, it becomes a political economy. Detractors try to get us to focus on the cost and the local consequences but not the results.

The infrastructure needed to distribute intelligence becomes politicized and friction is added to the attempts to uplift human capabilities. The blame for this lays at the feet of the industry leaders who have so far failed to explain the upside of distributed intelligence.

Nuance

There are many respectable arguments for slowing, testing, licensing, and controlling AI. “America’s Superintelligence Dilemma” makes the national-security argument. “Trump’s AI testing plan is limited and vague” shows the administrative argument. “Open-weight AI models are catching up to the frontier. The safety gap remains” gives the safety argument. None of these arguments should be dismissed. Real systems can cause real harm. But all are addressable if the goal is well understood.

Regulation can add to the friction, but also create a moat for the incumbents. Safety concerns can become a permission system that only the market leaders can succeed in passing. Compute can become a gate that only the richest can afford. Finance can become a point of control. If the same institutions that own the infrastructure also define the rules of access, then the public does not get universal intelligence. It gets managed intelligence. And quite likely metered intelligence at a cost higher than required for universality.

Humans Need AI

AI should distribute intelligence in order to expand human capability and human agency. It should be used by creators, researchers, companies, schools, doctors, founders, workers, and citizens. It should make more people capable, not fewer people powerful. It should be open enough for competition, broad enough for ordinary use, and cheap enough to matter outside the richest institutions.

That does not mean pretending AI can do everything. “AI agents can’t yet do open-ended AI research” is a useful warning. “Drug Discovery Has No Magic Wands” is another. “Knowing When to Stop” reminds us that loops, agents, and systems still need judgment. AI does not eliminate the need for human taste, responsibility, measurement, or institutional design.

But these limits are not an argument against use. They are an argument for better and more use.

What I believe

I believe humans need access to intelligence because human knowledge should not be trapped inside experts, institutions, or companies.

I believe distributed intelligence needs infrastructure because universal access cannot be delivered at boutique scale.

I believe the cost is justified because the goal is abundant intelligence in human hands solving more problems faster and impacting our lives.

I believe the politics matter because infrastructure creates power but it needs to be the politics of abundance not constraint.

I believe the answer is broad capability, competition, and shared upside, not a closed system of managed intelligence. Funding intelligence is the most intelligent thing we can do.

The question is not whether distributed intelligence will be built. It is being built.

The question is how fast and for how many at what cost.