Before dawn on July 16, 1945, a flash lit the New Mexico desert.
The Trinity test was a success.
J. Robert Oppenheimer and the scientists at Los Alamos helped turn theory into the world’s first nuclear explosion.
But proving that something can be built begs another question: What happens once people start using it?
That’s the cloud hanging over the AI industry today.
Over the weekend, Anthropic CEO Dario Amodei called for a slower pace of frontier AI development. His argument is that capabilities are advancing far faster than the industry’s ability to manage their risks. Both Sam Altman and Elon Musk agreed with the need for additional safeguards.
It invites an uncomfortable comparison…
Is Amodei having his “Oppenheimer moment”? In other words, confronting the consequences of a technology he helped create, while he believes there is still time to influence its course?
But here’s the relevant question for you:
Would slowing the development of the most powerful AI models also slow the demand for the chips, electricity, and data centers that run them?
My view is that we need to examine those questions separately. The historical analogy can frame the debate. It cannot, by itself, tell us what happens to AI infrastructure earnings.
The Oppenheimer Comparison
Source: Envato
In June 1945, before the first atomic bomb test or the bombing of Hiroshima, Oppenheimer joined a panel of scientists that supported using the bomb against Japan. But the panel acknowledged that scientists disagreed. Knowing how to build the bomb, they wrote, did not make them uniquely qualified to decide how it should be used.
After the bombings, Oppenheimer and his colleagues warned that having the most advanced weapons would not necessarily keep America safe. In 1946, he helped develop a proposal to put atomic energy under international oversight.
That’s where the comparison with Amodei becomes useful: How much say should the people building a powerful technology have over its future? And who else deserves a seat at the table?
In We Must Pace the Frontier, Amodei calls for independent reviewers to work inside AI companies, for leading labs to coordinate their efforts, and for governments to get more involved. Under Anthropic’s proposal, outside reviewers would get a close look at the company’s work and could publish their findings, with limits to protect confidential information and security.
There is an important difference, though. Oppenheimer helped build a weapon through a government-run program. Amodei runs a private company and is asking for more outside oversight.
Calling that “handing off responsibility” assumes more than we know. Amodei is proposing changes to who can examine and influence AI development. That alone doesn’t tell us he is trying to escape responsibility for what his company builds.
The Scientists Speaking From Inside
Another parallel involves the people doing the work and their concerns about where it might lead.
In July 1945, Leo Szilard and fellow scientists petitioned President Harry Truman, arguing that the United States should not use atomic bombs before giving Japan clear surrender terms and a chance to accept them. They also warned that America’s decision would set an example for how other countries might use these weapons.
Last week, Anthropic researcher Jacob Coxon publicly announced his resignation and accused Anthropic and OpenAI of “gambling with our lives.” In an interview with WIRED, he described colleagues’ concerns about how quickly AI was advancing and the pressure to keep up with competitors.
In both cases, the warnings came from people who knew the work firsthand. That gives us a reason to listen. It doesn’t mean every danger they foresee will come to pass.
And just because Coxon’s departure and Amodei’s essay appeared close together doesn’t mean one caused the other.
Amodei had already discussed serious risks in his January essay, The Adolescence of Technology. His latest proposal builds on concerns he has raised publicly before.
The biggest difference between Oppenheimer and Amodei is what had already happened when each man spoke out.
Oppenheimer’s postwar push for oversight came after atomic bombs had devastated Hiroshima and Nagasaki. Amodei is calling for action to prevent future harm from increasingly powerful AI, while also responding to problems already reported.
The comparison helps us think about the responsibilities of people who build powerful technologies. It does not mean the consequences are the same.
Conscience and Commercial Interests
The Oppenheimer comparison also raises a question about how history will remember the people building AI.
When a technology leader publicly warns about the dangers of his own work, that warning becomes part of his legacy. Years from now, people can look back and say: He saw the risks and spoke up.
But we can’t know how much of that warning comes from personal concern, a desire to protect his reputation, or business strategy. We can only guess.
What we can examine is how his proposals might affect the industry.
One concern is that expensive safety reviews and complicated rules could help the biggest AI companies hold on to their lead. Those companies have the money and staff to meet new requirements. Smaller rivals may struggle to keep up.
The OECD identifies complicated regulations as a potential obstacle for new competitors. It also notes that safety and certification rules can determine which companies are allowed to serve certain markets.
But oversight can also help competition. Making AI systems easier to inspect and easier to use together could give customers more confidence and more choices.
The details will matter: Who has to follow the rules? How much will that cost? And will those rules make it easier or harder for new companies to compete?
A proposal can address a real safety concern and benefit the company promoting it. Both can be true.
For investors, the business effects deserve attention. Guessing what’s on a CEO’s conscience won’t tell us much about future earnings.
Slower Development Doesn’t Mean Demand Stops
Here’s where this debate becomes especially useful for investors in AI.
AI needs computing power for two main jobs.
Training is how developers build and improve a model. Inference is what happens when someone puts that model to work – asking a question, writing code, reviewing a document, or completing another task.
Finishing the training doesn’t end the need for computing power. Every time someone uses the model, computers have to do more work.
Deloitte’s 2026 outlook projected that running AI models would account for roughly two-thirds of AI computing, up from about half in 2025. That’s a forecast, but it shows how much demand could come from using the technology already built.
A company can put an existing AI model to work in more departments while the next version goes through safety testing. Developers can create new products using capabilities already available.
All of that still needs servers, memory chips, networking equipment, cooling, and electricity.
That’s the basis of my investment case for AI infrastructure: More people using today’s AI can keep demand growing, even if tomorrow’s AI takes longer to arrive.
But that doesn’t mean a slowdown would leave the industry untouched.
Amodei says the industry should consider limits on the computing power used to train models, the training process itself, and the use of AI to improve AI. His proposal goes beyond making companies wait longer to release a finished product.
Limits on training could affect equipment orders. Delayed releases could also hold back applications that need abilities today’s models don’t have.
Extra safety testing and monitoring would require some computing power, too. But we shouldn’t assume that work would make up for everything delayed or canceled.
The investment question is whether growing everyday use outweighs any slowdown in development.
What Would Change My View
I’m watching what businesses actually do: how much they plan to spend, whether they keep ordering equipment, how much of their computing capacity they use, and whether more customers are paying for AI.
If customers cut spending plans, that matters. If businesses slow their adoption of AI, that matters. If chip orders weaken, we need to understand why.
But if companies keep finding useful ways to put existing AI systems to work, demand for the equipment supporting those systems can hold up even as development slows.
That still doesn’t make every AI stock a good buy at any price.
A business can grow and its stock can fall. If investors paid a price that assumed much faster growth, even solid results can disappoint. Shares can also drop well before a slowdown shows up in reported sales.
I still see a strong long-term opportunity in AI infrastructure, with those conditions in mind. The case rests on more customers finding useful, valuable things to do with AI. It doesn’t depend on every lab releasing its next model as quickly as possible.
Perhaps this is Amodei’s Oppenheimer moment. History will judge that through his decisions and their consequences.
For investors today, the more immediate question is, are customers continuing to find valuable work for the machines already running?
There’s one more piece of this puzzle I haven’t touched on yet… and it comes from Elon Musk directly.
While the market debates whether AI demand can hold up, Musk has spent 20 years assembling the pieces of a very different bet – one that we believe converges on September 24. If he’s right, the fallout won’t stay contained to Tesla or SpaceX. It could ripple through the same infrastructure names we’re watching for AI demand signals, and open up an entirely new market that could dwarf today’s AI trade.
We laid out the full case – company names, tickers, and the four bottlenecks Elon still needs to solve – in our Vertical AI Event.
Take a look before it comes offline soon!
P.S. The last time Jensen Huang stood in front of a room full of shareholders and journalists, he used a word most executives never get to say and mean it: parabolic. Demand had gone parabolic. Compute capacity was converting straight into revenue and profit. And the numbers backed him up — $82 billion in a single quarter, up 85% from a year earlier. The fourteenth straight quarter of growth stacked on top of growth. But that’s not the number that stopped me. What stopped me was what Jensen called the “second layer.” A part of the AI economy he says is poorly understood. I agree with him.
Underneath the hyperscalers and the frontier labs sits a tier of hundreds (soon hundreds of thousands) of smaller companies building AI infrastructure for their own industries, their own countries, and their own factory floors. They are doing an enormous share of the actual heavy lifting in this buildout. And almost none of them are public yet. Gwynne Shotwell is living proof of what happens when that kind of buildout is allowed to run its course. Her company started in a warehouse. Today it puts more mass into orbit than every government space program on Earth combined.
I’m sitting down with both of them behind closed doors at the All-In Summit this week. You won’t see this on CNBC or Bloomberg, and all attendees are hand-picked. Which means what gets said in that room is nothing you’ll find in an SEC filing or a press release. What I come back with, I don’t know yet. But the last time I walked into a room like that one, it changed how I think about AI wealth entirely. Stay tuned for an update when I return Wednesday.
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