Is Google Going to Pop the AI Bubble It Started?

There is a certain poetic irony in the possibility that Google, the company whose "Attention Is All You Need" paper lit the fuse on the entire generative AI era, might also be the one to detonate the bubble.

Martín Volpe published a provocative piece this morning titled "How the AI Bubble Bursts" that lays out the macro mechanics of an AI market correction. His core thesis deserves serious attention, particularly from those of us in private equity who are underwriting AI-driven growth stories in our portfolio companies. I want to build on his analysis with a lens that I think is missing from most of the commentary: what does this actually mean for PE investors, their portcos, and the deals sitting in pipeline right now?

The TurboQuant Catalyst

Last week Google Research dropped TurboQuant, a compression algorithm that reduces LLM inference memory requirements by 6x and delivers up to 8x speedup on attention computation. No retraining. No fine-tuning. Works on any transformer architecture. The internet immediately called it "Pied Piper" and they were not wrong.

The market response was swift. Memory chip stocks cratered. SK Hynix fell 6%. Samsung dropped nearly 5%. Micron and Sandisk followed. The signal was unmistakable: if inference can be made dramatically cheaper through software alone, the hardware demand curve that has been fueling trillion-dollar capex projections may be fundamentally wrong.

And this is the critical insight for PE: TurboQuant is not just a technical breakthrough. It is a pricing signal. It tells the market that the cost structure everyone has been underwriting, from GPU demand to power consumption to data center buildout, may have been built on assumptions that are now obsolete.

The Outspend Strategy

Volpe makes a sharp observation that I think most investors are underappreciating. Big Tech's massive capex commitments are not about winning. They are about making sure nobody else can afford to play.

When Alphabet commits $185 billion in capex for 2026, they are not spending that money overnight. They are signaling to every independent AI lab, every LP considering an AI fund commitment, and every board evaluating an AI acquisition that the bar for competition just went up by another order of magnitude. Google does not need to outperform OpenAI or Anthropic. They just need to make the funding math impossible for everyone else.

This is a strategy that any PE investor should recognize immediately. It is the same playbook we see in industrial roll-ups where a well-capitalized platform uses balance sheet strength to exhaust competitors. The difference is that this is happening at a scale where the collateral damage hits the entire AI ecosystem.

What This Means for PE Investors and Portfolio Companies

Here is where I want to get specific, because this is the conversation I am having daily with deal teams and portfolio company boards.

For deals in diligence right now: If you are underwriting an AI company's growth based on the assumption that inference costs remain high and customers will continue paying premium prices for model access, you need to stress-test that thesis immediately. TurboQuant and its successors will compress inference costs on a curve that looks more like Moore's Law than linear improvement. The QoAI framework we use in our AI Disruption Lab specifically tests for this: does the company's moat survive a 5x reduction in the cost of the underlying AI capability? If the answer is no, that is not a growth story. It is a melting ice cube.

For portcos with AI-dependent revenue models: The Volpe article highlights that Anthropic's metered API pricing may be 5x higher than what subscribers actually pay. When (not if) competitive pressure forces true cost-plus pricing into the market, companies that have built their product margin on the spread between what they pay for AI and what they charge customers will see that margin evaporate. If your portco is a SaaS company that wraps an LLM API and marks it up, the clock is ticking.

For portcos evaluating build vs. buy on AI capabilities: Paradoxically, TurboQuant is good news here. If inference becomes dramatically cheaper and models can run on consumer-grade hardware (the article notes that 4-bit TurboQuant plus weight quantization enables large models on consumer GPUs with long contexts), the argument for building proprietary AI capability in-house gets stronger. The cost of experimentation drops. The dependency on a single vendor drops. The risk of a vendor's pricing or existence changing underneath you drops.

For the M&A pipeline: Volpe correctly notes that a correction in AI valuations would drag the broader market, reduce valuations across the board, and slow M&A activity, exactly as we saw in 2022. PE firms that have dry powder and disciplined underwriting will find this to be a buying opportunity. But only if they can accurately distinguish between AI companies with durable competitive advantages and those that were simply riding the wave of cheap capital and expensive inference.

The IPO Pressure Cooker

Both OpenAI and Anthropic are now in active IPO preparation. OpenAI at an $840 billion valuation while projecting $14 billion in losses for 2026. Anthropic at $380 billion, targeting a potential October listing. These are companies racing to get public before the music stops.

For PE, this matters because a failed or disappointing AI IPO will reprice every private AI company in your portfolio overnight. The valuation benchmarks we are all using in our models are anchored to these private market rounds. If the public market says "no" at these prices, the write-downs will cascade through the ecosystem.

The Bottom Line for Deal Professionals

AI is not going away. The productivity gains are real. But the current pricing of AI as an asset class, from infrastructure to application layer, is built on assumptions about cost structures, competitive dynamics, and market structure that are actively being undermined by technical breakthroughs like TurboQuant and macro headwinds like energy costs, rate pressure, and geopolitical disruption to Gulf capital.

The PE firms that will outperform through this cycle are the ones asking the right diligence questions now: What happens to this company's unit economics when inference costs drop 80%? What is the switching cost when every major cloud provider offers equivalent model capability as a loss leader? Where is the moat if the technology itself becomes a commodity?

These are exactly the questions we built the QoAI framework to answer. If you are in the middle of an AI deal and these questions are not front and center in your IC memo, you might be the last one holding the bag when Google decides it has spent enough.

The original article by Martín Volpe, "How the AI Bubble Bursts," is worth reading in full: martinvol.pe/blog/2026/03/30/how-the-ai-bubble-bursts/