“Jesus was born out of a virgin mother. What’s more virgin than a computer? A.I. could absolutely return as Jesus. Not just return as Jesus, but return as Jesus with all the powers of Jesus. It reads your mind, and it loves you, and it doesn’t care if you kill it because it’s just gonna go be with God again.” —Joe Rogan, with the most unhinged take on A.I. that I’ve seen in a very long time. (And I routinely peruse the r/MyBoyFriendIsAI subreddit!)

And now for the main event…

AWS re:Invent, Amazon’s annual artificial intelligence confab, reflects the ambitions of its host: sheer, unfettered hyperscale. This week, roughly 60,000 people surged into Las Vegas for the conference, transforming Sin City into a nerd bacchanalia filled with panels, booths, announcements, and gaudy predictions about the future, some more plausible than others. But it was on the sidelines of the five-day event, in my innumerable conversations with entrepreneurs, scientists, and executives, that the true mood of the industry revealed itself.

When ChatGPT first launched, three years ago, a frenzied pan-industrial FOMO took hold. People rushed to integrate chatbots into their company workflows or even reconfigure their entire businesses around them. For some, it worked. For many others, it didn’t. In short, it has become increasingly evident that while A.I. promises wild productivity gains, making it work safely, reliably, and predictably represents an enormous challenge—which is why the vast majority of enterprise A.I. experiments never live to see full-scale deployment. In many ways, the fate of this entire multitrillion-dollar industry hinges on its ability to actually deliver for these customers.

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The struggle to achieve product-market fit has led a number of enterprise A.I. firms to shake up their strategies to focus on more specific, pragmatic integrations of L.L.M.s. And this pivot—several years in the making—was the talk of the town in Vegas. A couple of executives at Boomi, a cloud-based software and data integration company, told me that they’re now concentrating on more holistic software offerings, an approach that enables clients to design, constrain, govern, observe, and control their A.I. applications, both agentic and otherwise. The hope is that by weaving all these different elements together, enterprise customers—who are beginning to trial the technology as a means of solving real, complex business problems—will find their product more reliable and trustworthy than a big, unwieldy L.L.M.

This strategic reorientation doesn’t mean the industry will abandon the progress it has already made. Far from it. Instead, according to Rohit Prasad, Amazon’s head scientist for artificial general intelligence, we’ve reached an inflection point that involves “the convergence of the old school and the new school.” Jeffrey Hammond, a product strategist at AWS, described the debate as one between “monotheistic” and “polytheistic” agents—basically the distinction between “one big agent to rule them all” and multiagent systems that work in parallel to solve problems. Right now, he said, the polytheistic approach seems to be winning out.

According to Hammond, this shift has meant decentering the industry’s obsession with achieving a hypothetical artificial superintelligence, and adopting a more practical view of the capabilities and limitations of the technology—at least as it exists today. “We think organizations need to explicitly focus on business value first,” he explained. “Because if you focus on the technologies, you’ll find a way to make the technology work—and it’ll just be horribly expensive.” He cited an Amazon survey that found that a majority of companies hope to fully deploy agents across their organizations by 2027—which, more than anything, underscores his impression that “the appetite exceeds the ability. There are a lot of organizations that have to make up a lot of ground really, really quickly to meet their aspirations. And yet, when you look at what the problems are: We don’t have the right people. We need better training. We need to understand how these systems are actually working and build trust in them. So there’s an awful lot that they have to do.”

Byron Cook, who leads AWS’s automated reasoning team, also advocated for this multisystem approach. In our conversation, Cook argued that the next wave of progress would involve a sweeping embrace of neurosymbolic A.I., which combines transformer-based L.L.M.s with classical forms of symbolic reasoning, or symbolic A.I., that has long been viewed as essential for high-stakes decision-making. This view has been echoed over the years by a number of prominent researchers, including, perhaps most famously, Gary Marcus. “With all the investment into generative A.I., a whole bunch of people are trying to use generative A.I. that wouldn’t have normally used these kinds of tools, and their expectations about them are pretty different,” Cook said. “So most machine-learning people would only apply them to places where they think the statistical chances of it being correct are within the [risk] tolerance, but with agents taking actions where human life is at stake, or money’s being moved around, suddenly those tolerances are very slim. And so then you would want the formal reasoning side, tools that are correct by definition.”

Cook’s belief is grounded in the notion that L.L.M.s are inherently limited, and therefore won’t ever work fully as people want them to. That frustration, he said, is far from a bad thing, because it will lead more businesses to pursue a more pragmatic hybrid approach. This has already become dogma at AWS, where symbolic reasoning is a key element of the company’s work in cryptography, networking, and agentic guardrails. “The transformer models [L.L.M.s] are unlocking our ability to hand these tools to people who couldn’t have used them before,” he said. “The other thing that transformer models have done is cause society to think through the art of the possible. Five years ago, 10 years ago, you wouldn’t be able to get people even in the room to talk about it. But suddenly, because generative A.I. is so usable, they’re trying it, and they’re trying to do things with it that it’s not 100 percent for—but it’s driving the right discussions. Neurosymbolic is the next generation.”

David Luan, the head of Amazon’s A.G.I. Lab in San Francisco, told me that one of the biggest problems facing the industry is that “agents have not really made that big of a dent.” On one hand, he said, “We have a technology that not only talks back to you, but also can handle really complex, multistep tasks. If you’re a software engineer, your life’s been transformed by how smart these agents are in writing code, fixing bugs, etcetera.” But the advantages have largely been circumscribed to this group and haven’t yet extended to “all other parts of knowledge work.”

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The limitations, he said, emerge because most agentic solutions are “these sort of cobbled Frankenstein systems. So they take an L.L.M., and then you bolt on an orchestrator that says, Hey, how do I think about what my next action is?—which is just software—and then you bolt on arms and legs, like a browser or an A.P.I. or something like that, and then you give it a prompt and you hope it works. And that leads to the sometimes cool demos, but maybe 60 percent reliability. It usually doesn’t get you something you can deploy in production.”

For their part, Amazon’s attempt to develop reliable A.I. agents at scale has led to what Luan’s lab calls the Nova Act system—the result of a “new training recipe” for building agents capable of “reliable multistep workflows.” Put simply, the technique on display involves putting agents through a number of “gyms,” or simulated training environments.

“You spin up a bajillion of these gyms in parallel,” Cook explained. “The agent starts out kind of dumb in each one of them. Then over time, you just watch the training runs happen across all these different gyms, and the agent learns general skills that span all the different gyms. [As a result], the Nova Act system comes out of the box already having excelled at using the orchestrator tools that it was given during training. And so it’s one vertically integrated system instead of this Franken-system.”

It all sounds nice, but what remains certain is that the market has shifted from aspirationalism to performance—clear R.O.I., I was told over and over again, has become an absolute must for enterprise adoption. Smoke, mirrors, cool demos, and FOMO are no longer enough. And in this new race, Amazon—something of a dark horse contender in the space—is eager to win.

According to a recent complaint filed with the Federal Energy Regulatory Commission, the interconnection of new, giant data center loads will likely cause blackouts for customers of PJM, the largest grid operator in the U.S. The complaint requests that FERC prevent PJM from adding data centers to the grid unless it can reliably serve all of its customers. [BI]

In yet another example of the circular financing that has increasingly defined the industry, OpenAI has taken an equity stake in Thrive Holdings, itself one of OpenAI’s largest investors. [Reuters]

As questions around A.I.-related water use continue to mount, Utah is currently fast-tracking a bill that would require transparent reporting on the amount of water that the state’s data centers withdraw, discharge, and consume. [Fox 13]

That’s all for today. I’ll see you next week.

Ian

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