AI at CES is Not Just Cheesy Gadgets Anymore

**Nathaniel Whittemore** (0:00)
Today on the AI Daily Brief, YCES is telling an extremely different story about AI this year than it has in the past. Before that, in the headlines, what investors and analysts think are the most important AI risks in the short-term, which frankly might surprise you. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
Welcome back to the AI Daily Brief Headlines edition, all the daily AI news you need in around five minutes. We are in the first full week of business being back in session for 2026, and as such, everyone is kind of trying to get a vibe for what this year is going to be like. Now, of course, we're not going to learn all that much in the first days of the year, but they do kind of set a tone. And this year certainly has not been without drama. We have a whole new situation in terms of the geopolitics of North and South America, all sorts of interesting questions around politics this year, which is, of course, an election year in the US., and some big market questions around AI, which has been the most important theme in markets for the past several years, all contributing to what is a really interesting environment. Now, in that context, I've recently seen a couple of different assessments around what some of the big risks in general, but also with AI are. And they're not necessarily the things that you normally think of when you think of AI risk. According to Wall Street analysts, for example, one of the most overlooked risks for this coming year is AI-driven inflation. Morgan Stanley strategist Andrew Sheets wrote, The costs are going up, not down, in our forecast, because there's inflation in chip costs and inflation in power costs. Sheets and Morgan Stanley are forecasting that inflation will remain about the Federal Reserve's 2% target until the end of next year, in part due to heavy capex spending on AI infrastructure. Now, one of the economic forces we saw in the latter part of last year was a lack of cost sensitivity when it came to datacenter construction. Input costs for these facilities are concentrated around the price of chips, making premiums on labor and electricity, frankly, kind of inconsequential. This sent construction worker wages spiraling higher, with some now commanding $200,000 a year to work on datacenter projects. Some analysts then are wondering if that sort of datacenter spending could flow through to generalized inflation through both elevated wages as well as price and sensitive energy consumption. Wrote Karmanak Portfolio Manager Kevin Thozet, Inflation risk remains very underappreciated. Inflation is what could start to scare investors and cause markets to show more cracks. George Chan, a consultant at Asia Group, believes we'll see price pressure on chips start to curtail the AI buildout. He wrote, Memory chip cost inflation will push up prices for AI groups, lower investors' returns, and then the flow of money into this sector will reduce. Now, I personally tend to think that the risk around things like price and sensitive electricity consumption is more about its flow through to politics than inflation, but it is interesting that this is part of a conversation that's happening on Wall Street even if it's not, I believe, the mainstream consensus. A second interesting risk discussion came from the Eurasia Group. Each year, their Eurasia Group publishes their list of the top 10 global risks, and they call 2026 a tipping point year. Interestingly, while they do point out that it's a time of, as they put it, great geopolitical uncertainty, for them, the biggest risks are not the standard ones you hear, such as rising conflict between the United States and China, but mostly about how the US decides to reposition its role in a new global order. Interestingly, however, risk number eight, they called AI eats its own users. It's short enough at three paragraphs that I think I'll just read it in whole. They write, Under pressure to generate revenue and unconstrained by guardrails, a number of leading AI companies will adopt business models in 2026 that threaten social and political stability following social media's destructive playbook only faster and at greater scale. We remain bullish on AI's revolutionary potential. Today's frontier models reason through complex problems, show their work, and are embedded in coding, research, and knowledge workflows. The hyperscalers are offloading large chunks of software development to AI, accelerating their own R&D cycles. In biotech and material science, AI is opening new research pathways, though commercial breakthroughs remain mostly ahead of us. Hundreds of millions of people now use chatbots daily for everything from drafting emails to debugging code and learning new skills. This is real and it's just beginning. But AI can't live up to investors' expectations in the short term. Even after hundreds of billions of dollars of investment, the most advanced models still hallucinate. Their capabilities are jagged, dazzling at some tasks unreliable at others, and often unpredictably so. That inconsistency makes them hard to deploy in high-stakes applications where errors are costly. Business adoption has been uneven, with only about 10% of US firms using AI to produce goods and services, according to the Census Bureau. Many companies report significant productivity gains, but surveys suggest most have yet to see meaningful bottom line impact. Real productivity increases will arrive through wide diffusion of the technology across the economy, but that takes time. Yet markets have priced in revolution, not evolution. Now, frankly, this is kind of a mess of a prediction. It starts talking about one thing and then talks about totally different things. Now, I know they're not just being cynical and skeptical for the sake of it, and as they say, they remain bullish on its potential, blah, blah, blah, blah, blah. But given that this is a group that people listen to, I think it's worth being a little bit more specific. And frankly, I'm much, much more interested in where the assessment starts, than where it ends up. When it comes to this whole third paragraph about AI not being able to live up to investors' expectations in the short run, I just think that this is a fundamental misunderstanding of where AI is right now. A common misunderstanding, but a misunderstanding nonetheless. This idea that the capabilities are too jagged for AI to be valuable in a workplace context right now is simply not true anymore. Anyone who is slowing down their deployment of AI because of some generic concern about hallucination is not paying attention. Does that mean that there aren't specific use cases where hallucination creates too high a cost barrier for that to be a viable solution in the short term? No, that is a thing. But the idea that hallucination is creating some major headwind slowing down adoption overall I think is just incorrect. Secondly, I will go on record and be very clear that the Census Bureau numbers that they're publishing, suggesting that adoption in firms is around 10% are straight up wrong. I owe it to myself and to you guys to dig more into exactly how those numbers are sourced, but they are completely different from anything else that we're seeing. The idea that more than 40 or even 50% of American adults are using AI, but only 10% of companies are, does not carry water. And so ultimately, I think that is just an incorrect statistic, and leads people to a misunderstanding of what's actually going on. Lastly, this idea that most firms have yet to see meaningful bottom line impact, if you listen to any amount of the results of our AI ROI Benchmarking Survey, I think that conventional wisdom is once again growing a little long in the tooth. Now, it still may be that even with all that, that conclusion that markets are priced in revolution, not evolution, may be true. But I think that the basis for that argument that they're sharing is dramatically oversimplified and based on frankly incorrect information. Like I said, though, what's much more interesting to me is the core idea that AI eats its users, that under pressure to generate revenue a number of leading AI companies will adopt business models that threaten social and political stability following social media's playbook. This is super interesting to me because it is something that I can see happening, and I think that we do have some early evidence for this. This was frankly why there was such acrimony around the launch of Sora, not the second version of the model, of course, but about the application. To some, it felt like OpenAI, although saying that they weren't about just consuming as much of our attention as possible, was running back the same playbook that social media companies had used to consume as much of our attention as possible. I do think that to the extent that that second part of the prediction is right, that markets have priced in revolution, not evolution, and that starts to create pressure on the big hyperscalers to get performance at any cost, this risk that they travel down pathways that are not the ones that are actually ultimately that good for society, even if they make more money in the short term, is a real and very interesting risk. Still overall, a super fascinating conversation, and I'm glad that the Eurasia Group and these others are having it. Now, as you might imagine, there is a big overlap in the commentary overall between markets and what they expect from Fed policy, as well as how they see AI playing out. Speaking with CNBC's Squawk Box on Monday, Minneapolis Federal Reserve President Neil Kashkari said that AI is starting to impact hiring plans at the companies he speaks with. He believes, however, that these effects are stratified across the economy, arguing that AI is really a big company story. He argues that smaller companies are yet to see AI drive down hiring, but they're also not participating as strongly in AI-derived productivity gains. Now, if that assertion makes you decide to stop paying attention to anything Kashkari has to say, I can't say that I particularly blame you. However, speaking to the idea of an AI bubble, Kashkari believes we're over the horizon and beginning to see tangible benefits. He said, there's no question that there's some misinvestment or malinvestment that's going on, but there are too many anecdotes of businesses using this and actually seeing real productivity gains. Businesses that I talked to two years ago that were skeptical are saying, no, we're actually using it now. Overall, the takeaway is that AI is absolutely on the Fed's radar as a real force that's reshaping the economy. We're not yet at the point where Fed officials are talking about rate cuts to address AI-related layoffs, but the issue is starting to factor into monetary policy discussions. Legendary investor Ray Dalio also commented on AI recently. In a recap of the year in Markets on X, he wrote, Obviously, the AI boom that is now in the early stages of a bubble had a big effect on everything. Dalio said that he would soon publish an explanation of his bubble indicators so he didn't get too deep on the topic. Now Dalio has been known as a bit of a doomsayer in macroeconomics over recent years. However, he's not of the view that we're living through a repeat of the dotcom boom that will inevitably collapse under its own weight. Instead, he's most concerned about inflation and the rising national debt, viewing a booming stock market as a symptom of the dollar losing value. Indeed, while many are wary of an AI bubble about to pop, Dalio seems to be calling for AI stocks to continue their strong performance in 2026 Not even necessarily because of fundamentals, but because of structural macroeconomic forces that encourage financial bubbles to grow. And yet, for all of this, the market is still hungry for AI debt, despite some wobbles at the end of last year. You might remember in my predictions episode that I said that this is the key thing to watch when it comes to AI bubble conversations, and is much more important frankly than any random commentary along the way. Throughout 2025, we saw the data center build out transition from free cash flow funding to a debt funding model. Oracle was emblematic of this trend and began functioning as the canary in the coal mine towards the end of the year. Bloomberg's Matt Levine, however, highlighted that although this debt funding is coming from private credit firms, it's categorically different to the traditional role that private credit has played. Usually, private credit would fund buyouts and roll ups, relatively small deals that don't get a lot of attention. Instead, we now have deals like Metta's $27 billion in debt issuance to fund their Louisiana data center. Levine wrote, when AI firms borrow infinity zillion dollars to build data centers, they will sometimes do so in the bond market, but for structuring flexibility reasons, they will often do it from private lenders. And when they do that, everyone in the financial industry and possibly everyone in the world will think a thought like, oh, AI, I would like to get in on that deal or else, oh, that's stupid, I would like to short that deal. Everyone knows about this stuff and there is a diversity and intensity of opinions. The upshot is that these very loud Wall Street opinions are ensuring that debt funding is plentiful for the time being, if only as a trading vehicle. Rohan Lateef, Global Head of Credit Trading at Morgan Stanley said, I view it as very much the biggest single opportunity coming into 2026 Every single time a new market is created, there's a little bit of a lag before the secondary market kicks off. The reality is, this is the right time for it to happen. Something we will keep an eye on throughout the year. For now, however, that is going to do it for today's headlines. Next up, the main episode.