Milk Road AI(@MilkRoadAI):The Fed just admitted that AI demand is now big enough to move INFLATION across the entire United States economy. New York Fed President John Williams said today that one more rate hike may be appropriate this year but he currently sees no need for any further increases in 2027. That is notably less hawkish than the market, which had been pricing a December hike plus two more hikes next year. Bonds liked what they heard, with the 10 year Treasury yield falling about 2.5 basis points to 5.219% and the 30 year dropping about 3.4 basis points to 5.565%, which is a small relief for $TLT holders. But the part that really caught my attention is what he said about AI. Williams said AI infrastructure demand is pushing up chip prices, construction costs, and inflation, and that supply is unlikely to catch up for SEVERAL YEARS. This is not a new view from him either, because back in July he said the economy is in a race between available supply and surging demand, with sharp price increases in semiconductors, power transformers, and other technology essential for the AI buildout. In September he went even further, saying that so far demand is winning, and that those higher costs are starting to show up in the prices consumers and businesses pay for other products. When a central banker is telling you that chips and power equipment are scarce enough to show up in national inflation data, that is the AI supply shortage thesis being confirmed at the highest level. It is exactly why memory names like $MU, power infrastructure names like $VRT and $GEV, and optical names like $COHR and $LITE have pricing power right now, because customers cannot get enough of what they sell. Now, there is another side to this, and it is worth taking seriously. Higher rates are tightening borrowing conditions, Williams himself noted that higher long term yields are doing some of that work, and a 10 year yield above 5% makes it more expensive to finance the massive data center buildout. New York Fed survey data also shows one year inflation expectations climbing to 3.9% in September. If AI driven price pressure proves sticky, the market could be right to price more hikes than he does, which would be a real headwind for $SPY and richly valued names like $NVDA. Williams still expects AI productivity gains to eventually help bring inflation down, and he says the broader economy is not overheating and labor markets remain stable. My take is that the Fed is now openly saying AI demand is outrunning supply, and as long as that stays true, the companies selling the scarce stuff should keep winning.

2026.10.13 19:51

The Fed just admitted that AI demand is now big enough to move INFLATION across the entire United States economy. New York Fed President John Williams said today that one more rate hike may be appropriate this year but he currently sees no need for any further increases in 2027. That is notably less hawkish than the market, which had been pricing a December hike plus two more hikes next year. Bonds liked what they heard, with the 10 year Treasury yield falling about 2.5 basis points to 5.219% and the 30 year dropping about 3.4 basis points to 5.565%, which is a small relief for $TLT holders. But the part that really caught my attention is what he said about AI. Williams said AI infrastructure demand is pushing up chip prices, construction costs, and inflation, and that supply is unlikely to catch up for SEVERAL YEARS. This is not a new view from him either, because back in July he said the economy is in a race between available supply and surging demand, with sharp price increases in semiconductors, power transformers, and other technology essential for the AI buildout. In September he went even further, saying that so far demand is winning, and that those higher costs are starting to show up in the prices consumers and businesses pay for other products. When a central banker is telling you that chips and power equipment are scarce enough to show up in national inflation data, that is the AI supply shortage thesis being confirmed at the highest level. It is exactly why memory names like $MU, power infrastructure names like $VRT and $GEV, and optical names like $COHR and $LITE have pricing power right now, because customers cannot get enough of what they sell. Now, there is another side to this, and it is worth taking seriously. Higher rates are tightening borrowing conditions, Williams himself noted that higher long term yields are doing some of that work, and a 10 year yield above 5% makes it more expensive to finance the massive data center buildout. New York Fed survey data also shows one year inflation expectations climbing to 3.9% in September. If AI driven price pressure proves sticky, the market could be right to price more hikes than he does, which would be a real headwind for $SPY and richly valued names like $NVDA. Williams still expects AI productivity gains to eventually help bring inflation down, and he says the broader economy is not overheating and labor markets remain stable. My take is that the Fed is now openly saying AI demand is outrunning supply, and as long as that stays true, the companies selling the scarce stuff should keep winning.

Milk Road AI@MilkRoadAI

2026.10.09 21:31

This is EXACTLY why I remain so bullish on the broader AI infrastructure buildout. Look at what is happening with GPU rental prices compared to AI token prices. Since July, the cost of AI tokens has collapsed, yet H100 GPU rental prices have remained elevated at roughly $2.70 per hour and this is a perfect example of Jevons Paradox. As AI becomes cheaper to use, developers and businesses can afford to use significantly more of it. Applications that were previously too expensive to run at scale suddenly become economically viable. Think about it, if the cost of running an AI agent drops significantly, companies can afford to deploy thousands of agents instead of hundreds. The same applies to coding assistants, AI search, customer support, and pretty much every application that relies on inference. And while each individual task might become cheaper, the total number of tasks being performed could grow significantly faster. That means more demand for GPUs, memory, networking equipment, data centers, and electricity and we're already seeing signs of this in the chart. Even as token prices have fallen sharply, H100 rental prices have remained relatively stable since July. That suggests demand for compute is still strong enough to support pricing despite the improvements we're seeing in AI efficiency. Now, there is an important distinction here. Cheaper tokens don't automatically guarantee higher GPU prices. Hardware supply, utilization rates, and newer GPU generations all play a role. But the fact that compute pricing has remained this resilient while token prices have declined tells us something important about underlying demand. I also think investors are underestimating just how much additional AI usage lower prices could unlock over the next several years. We're still relatively early in the adoption cycle, and many businesses have barely started integrating AI into their daily operations. This is why I continue to favor the companies supplying the infrastructure behind AI rather than trying to predict which model provider will ultimately dominate. Regardless of whether OpenAI, Anthropic, Google, or open source models capture the most market share, someone still needs to provide the compute required to run all of these models. And as long as AI usage continues growing faster than efficiency improvements reduce compute requirements, the broader infrastructure buildout should continue benefiting. Bullish on the broader AI infrastructure buildout. If you want to see how I’m investing across compute, memory, networking, and power, check out my full Milk Road Pro portfolio below:

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