AI Spending Surge and Key Insights from Big Technology's AI Summit

June 26, 2026 - 20:04

Don't expect the artificial intelligence spending frenzy to slow down anytime soon. That was the clearest message to emerge from the Big Technology AI Summit, held last week in San Francisco.

Over the course of a few hours, the event packed in a month's worth of discussions. The consensus around spending, both on infrastructure and services, pointed only upward, even amid what some called the "tokenmaxxing reckoning."

Speakers included OpenAI President Greg Brockman, Anthropic Labs Lead Mike Krieger, Box CEO Aaron Levie, and Corridor Chief Product Officer Alex Stamos. Here are the most resonant takeaways from the day.

Don't Wait for Moore's Law

The traditional rule that computing costs decline over time appears broken with AI. Box CEO Levie explained that the technology's growing capabilities push companies to tackle harder, more expensive problems. "People get confused. They say, 'I thought AI was supposed to be getting cheaper,'" he said. "Actually, no, we're outrunning the efficiency improvements in our appetite for what these models can do."

Levie noted that Box's token consumption has risen exponentially. When the company launched its first AI use case, tasks used 5,000 to 20,000 tokens on average. Now, its latest agents might use 1 million to 5 million tokens per task.

Current AI Policy Fulfills the Pause Movement's Dream

Remember the six-month AI pause petition that went nowhere? Levie argued that current U.S. policy, where the White House reviews and approves each new model, effectively delivers what the signatories wanted. "If you had to establish a regulatory regime that said we are going to review models," Levie said, "you would kind of need something that shocks the system into that kind of regulatory framework."

Frontier AI Cybersecurity: Not Just a Fable Issue

The White House placed export controls on Anthropic's latest model, but Alex Stamos argued the real milestone passed quietly. The AI cyber breakthrough came last year with models like Opus 4 and GPT-5, which gave human engineers superpowers. "It's like all of a sudden you went to a high school track meet and all the kids are running Olympic times," Stamos said. He noted that Mythos is the best public model at finding bugs, but it is not unprecedented.

Fable Gets Things Done with Less Back and Forth

Mike Krieger, the Instagram co-founder and current Anthropic Labs lead, said Fable requires less hand-holding than earlier models. He could hand off larger, more open-ended tasks and step away, rather than babysitting the model through constant back-and-forth. "It would do the whole thing, and then kind of hang out for the next seven hours," Krieger said of one project.

Compute May Be the Core Differentiator

Greg Brockman made clear that OpenAI believes it will differentiate itself partly through the amount of compute it accumulates. "There just is not going to be enough compute in the world to satisfy all the demand," he said. "Right now, we're talking about compute constraints and the number of people using these agents is on the order of 10 million, 20 million maybe. We're not at planet scale."