Wall Street trades the Über-dove Fed trial balloon as AI enters its TPU age

US stocks didn’t simply rise on Tuesday — they traded as if someone quietly reached under the market’s chassis and flipped every switch that matters: the Fed chair succession, a fresh volley of AI-supply-chain tremors, and a data deluge that leaned dovish from every angle. It was the kind of Wall Street session where the microstructure felt like a storm beneath calm water — visible only to the desks that watch futures order books like cardiograms.

The S&P 500 finished up 0.9%, but that number hides the journey. The index pinballed between modest red and green as traders tried to make sense of three competing narratives: Trump installing a potential über-dove as Chair at the Eccles Building, AI’s sudden shift from GPU absolutism to TPU pluralism, and a macro tape that looked like it had been pulled out of the freezer after the government shutdown. The Nasdaq 100 staged a full intraday redemption arc — down 1.3% at one point, closing up 0.6% — the kind of whipsaw that tells you systematic flows and discretionary macro were wrestling for control of the same steering wheel.

This wasn’t a slow climb higher but a market trying to re-price the cost of money in real time.

Yields were the clearest tell. The 10-year drifted back below 4% — a level that’s becoming a psychological floor — as traders digested the political bombshell Bloomberg dropped midday: Kevin Hassett, Trump’s longtime economic consigliere, is now the frontrunner to replace Jerome Powell. It instantly felt like the curve was repricing not a mere pivot, but a new regime. A Fed chair who says he’d be cutting rates now, in November, based on the existing data? That’s not “dovish signaling.” That’s the monetary equivalent of driving a forklift through the FOMC’s carefully choreographed forward guidance.

Treasury Secretary Bessent didn’t deny it. Trumpworld didn’t hide it. And Polymarket — the fastest political risk-pricing engine on Earth — took Hassett’s odds to 55% in minutes. In 2025, that’s what counts as confirmation.

And Wall Street knows the Hassett playbook. He is philosophically aligned with Trump to the decimal place: the economy is fine, inflation is licked, rates are too high, and the Fed should be cutting in broad, assertive strokes. A Hassett Fed isn’t a pivot — it’s a doctrine.

The moment that sank in, the entire front end of the Treasury curve re-shaped itself. 2s10s and 5s30s re-steepened. SOFR spread sellers showed up immediately, hammering the March 2026/March 2027 12-month spread — the exact tenor that defines the first full lap of a post-Powell Fed. If you needed a clean model for how a single political headline can reshape the macro surface of the curve, this was your masterclass.

But markets weren’t only feeding on politics — they were drowning in freshly thawed data.

The shutdown backlog released an avalanche of extremely stale prints: ADP soft, retail sales weaker, Core PPI tame, Richmond Fed grim, consumer confidence dismal. None of it is current, none of it is forward-looking. But in a market starving for macro inputs, even freezer-burnt data tastes dovish. Goldman’s economists shaved Q3 GDP tracking to 3.7%, reinforcing the narrative that growth is cooling right into the December FOMC window.

It’s the classic post-shutdown phenomenon: traders don’t believe the data, but they trade it anyway because someone has to reset the models’ baselines.

That’s why stocks and bonds both surged on the same catalyst — a classic bad-news-is-good-news data tape combined with the prospect of a rate-cut crusader at the Fed, effectively resurrecting the entire easy-money beta trade.

And then AI stepped in with its own plot twist.

The chip complex — the real S&P leadership engine in 2025 — experienced a subtle but seismic shift. Alphabet, having flirted with a $4 trillion valuation, was yanked off its session highs by reports that Meta is in talks to buy billions of dollars’ worth of Google TPU-based AI chips.

TPU > GPU for specific workloads has gone from theory to boardroom agenda.

Nvidia, AMD, Oracle — all traded lower, all recalibrating. The market suddenly realized it had been modelling the AI capex cycle as a straight line pointed at NVDA’s supply chain. So now we’re entering Phase 2 — an arms race where hyperscalers might start swapping out GPU stacks for TPU stacks, or a run on hybrid compute that, in either case, could very well erode Nvidia’s monopolistic stranglehold.

It was a mini DeepSeek ripple effect all over again — only this time, the threat wasn’t a new LLM, but a new era of US compute hardware.

Volumes exploded as GOOGL torched the tape in the morning. Then, like a coordinated handoff between two macro gods, the Hassett news hit the screens and the entire complex pivoted from “AI correction” to “Fed liquidity repricing.”

This is the new order of US markets: AI drives the secular narrative; the Fed drives the cyclical oxygen supply; politics acts as the fuse box connecting them both.

The dollar sagged. Oddly, Bitcoin fell through $88,000.

Long-end bonds bid. Short-end yields collapsed. Risk assets closed firm. And traders headed home knowing the US market had just priced in a Fed future that may or may not exist — but that’s never stopped Wall Street before.

Tuesday wasn’t about data, earnings, or even AI.
It was about the possibility of a Fed chair who would treat rate cuts the way traders treat dip-buying: aggressively, instinctively, and without apology.

And that possibility alone was enough to make US stocks trade like the future had just been marked up 50 basis points.

AI enters its TPU age

Google’s Tensor Processing Units — long treated as an internal optimization tool rather than a market threat — are suddenly stepping out of the shadows. After a decade spent quietly powering search, ads, and in-house AI, TPUs are now winning real deals with real hyperscalers, positioning themselves as the first credible alternative to Nvidia’s GPU empire.

The distinction is simple but profound. Nvidia’s GPUs were born for gaming and adapted brilliantly for AI — thousands of parallel cores carrying whatever workload engineers throw at them. That flexibility makes them the Swiss Army knife of compute, but also power-hungry and expensive to operate. TPUs, by contrast, were architected specifically for matrix multiplication — the repetitive, sequential math that drives neural networks. They’re narrower, more specialized, less adaptable — but incredibly efficient. For certain inference and some training workloads, they can outperform GPUs precisely because they don’t carry the extra circuitry needed for general-purpose compute.

Google has quietly iterated seven generations of the chip, feeding lessons from DeepMind and Gemini straight back to its silicon team. The latest Ironwood release is liquid-cooled and deployable in pods of 256 chips or in mega-clusters of over 9,000 — turning what was once a boutique internal accelerator into a hyperscale-ready weapon.

The customer list is suddenly revealing. Safe Superintelligence, Midjourney, Salesforce, Anthropic — and now Meta is in discussions to roll TPUs into its 2027 data centers. The Anthropic deal for up to one million TPUs — backed by more than a gigawatt of Google compute — is effectively Google’s declaration that TPUs are no longer an internal advantage; they’re a commercialized platform capable of scaling alongside Nvidia.

The logic is straightforward: Big AI developers are spending tens of billions on Nvidia hardware, battling shortages, price inflation, and vendor concentration. A second supplier — especially one offering lower energy consumption and predictable performance — is irresistible. And while TPUs today are still tied to Google Cloud, analysts see the Anthropic structure as a stepping-stone toward multi-cloud TPU deployments.

No one is abandoning Nvidia. The hyperscalers can’t. The pace of AI model development demands flexibility and programmability that only GPUs currently provide, which is why even TPU customers continue signing massive Nvidia allocations. Google itself remains a top-three Nvidia buyer for precisely that reason.

But the narrative has shifted. For the first time in years, Nvidia is not battling a startup or a science project. It’s facing a mature, seventh-generation accelerator built by a company with the scale, capital, data centers, and AI research pipeline to actually challenge GPU dominance at the margin.

In other words: the AI Manhattan Project is no longer a single-reactor world. The TPU age is real, the hyperscalers are experimenting, and the demand curve for compute is so explosive that a multi-supplier future is inevitable.

Nvidia is still the center of gravity — but Google’s TPUs are no longer orbiting quietly. They’re altering the map.