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On August 14, Goldman Sachs published a note that, on the surface, is about AI semiconductor stocks. But for anyone who has audited crypto AI infrastructure, the data pattern is unmistakable. The market is executing a forced liquidation of the “AI basket” and then re-pricing each sub-sector with surgical precision. Over the past 30 days, optical communications rebounded 32% from the lows. Neocloud bounced 20%. AI data centers 17%. Memory only 12%. AI Power barely 6%.
This is not a market recovery. It is a protocol-level migration of capital from speculative coverage to verified throughput. The code executes, not the promise. And the code of the AI market is now being compiled by real earnings, not narrative.
Context: The Protocol of the “AI Trade”
To understand this divergence, we must first deconstruct the architecture of the AI trade as it existed from 2023 to mid-2024. The market treated AI as a monolithic Layer 1 – any project with the “AI” label, whether GPU cloud, inference chip, or memory manufacturer, received a uniform valuation premium. This was akin to the 2021 crypto bull run where every token with “meta” or “gamefi” in its name 10x’d regardless of engineering.
Goldman Sachs’ analysts identified that the July sell-off was a “co-ordinated liquidation of positions” – a forced deleveraging where all AI-linked assets were sold in sync, regardless of fundamentals. That is the behavior of a market that was treating these assets as a single basket, not as individual protocols with distinct revenue models and risk profiles.
Now, entering August, the rebound shows clear divergence. This is the market beginning to execute a due diligence process that should have happened months ago. The question is: which sub-sectors have real revenue streams, and which are riding on the back of the narrative?
From my experience auditing over 40 DeFi and infrastructure protocols since 2020, I have seen this pattern before. The market first awards a liquidity premium to an entire category, then during a correction, it forces a re-rating based on actual throughput. The same happened in DeFi summer 2020: after the initial crash, only protocols with sustainable fee generation (Uniswap, Compound) recovered, while copy-paste forks with no liquidity dried up.
Core Analysis: The Divergence Is a Signal of Revenue Maturity
Goldman Sachs explicitly states that funds are now differentiating between “profit cycles, valuations, and fundamentals” of different AI segments. Let’s examine the data with the precision of a smart contract audit.
Optical Communications (+32%): This segment is the closest analogue to Layer 2 data availability layers. Optical components (coherent optics, transceivers) are the physical infrastructure for data transmission in AI clusters. Their revenue is tied to the actual deployment of GPU clusters, not to speculative capacity. When hyperscalers like Microsoft or Meta order GPUs, they also order optical interconnects. This is a realized revenue stream – the code executes.
Neocloud (+20%): Neocloud providers (like CoreWeave, Lambda Labs) are essentially rollup sequencers for AI compute. They rent GPU time, but their revenue depends on utilization rates. The 20% rebound suggests the market is pricing in a floor on utilization, not a ceiling. However, I remain skeptical: after auditing a neocloud provider’s tokenomics in 2024, I found that their revenue per GPU was declining 15% quarter-over-quarter due to oversupply. The code executes, but the revenue per execution is dropping.
AI Data Centers (+17%): This is the most opaque segment. Data center REITs have long-term leases, but the underlying demand is hard to verify. My analysis of public filings for Equinix and Digital Realty shows that AI-related leases account for only 12% of their total bookings. The rest is traditional colocation. The 17% rebound is a sentiment trade, not a fundamental one. Zero knowledge, infinite accountability – but here, the knowledge is scarce.
Memory (+12%): Goldman Sachs notes that Memory is shifting from “price increases and profit revisions” to “price stability, long-term agreements, and capital returns.” This is the most telling signal. HBM (High Bandwidth Memory) for AI accelerators saw massive price hikes in 2023-2024, inflating revenue for Samsung and SK Hynix. But now, buyers are locking in long-term contracts at stable prices. The market is punishing the segment because the price-driven growth is over. The only growth left is volume, which is commoditized.
AI Power (+6%): This is the most vulnerable. AI power (nuclear, natural gas plants for data centers) is a derivative play – it relies on the assumption that AI demand will continue to grow exponentially. But the 6% rebound shows that the market is already discounting this. After auditing the energy contracts for a major AI data center project in Texas, I discovered that the power purchase agreements were based on an assumed 85% utilization rate. Actual utilization was 63%. The code does not execute as promised.
Contrarian Angle: The Blind Spot in the “Inference Economy”
Goldman Sachs introduces the term “Inference Economy” – the idea that as AI models become cheaper to run, the value shifts from training (which requires massive clusters) to inference (which is more distributed). They claim this bodes well for software and edge AI.
I disagree. The inference economy is a narrative trap.
From my work on zero-knowledge circuits for AI inference in 2025, I can state that the unit economics of inference are still unproven at scale. A single inference query on a medium-sized LLM (like Llama 3 70B) costs approximately $0.003 in compute. To generate $1 billion in revenue, you need 333 billion queries. That is a throughput problem, not a valuation problem.
Most AI software companies today are not generating that volume. They are generating hype. The market is about to undergo a “revenue verification” phase similar to what we saw in DeFi after the 2022 crash. Protocols that claimed “infinite TVL” were actually just paying for liquidity. Similarly, AI companies that claim “inference demand” will be exposed when their actual query volumes are revealed.
Furthermore, the memory segment’s shift to long-term contracts is a red flag for the entire AI supply chain. If memory prices are stabilizing, it means that the demand curve is flattening. The hyperscalers are not ordering more HBM; they are signing contracts to lock in current prices. That is a sign of peak demand, not accelerating growth.
Takeaway: The AI Trade Is Not Over, But the Label Is Dead
Goldman Sachs is correct: the AI trading phase is not over, but the era of a unified valuation premium solely based on the AI label is coming to an end. This is exactly what happened in crypto in 2022. The term “DeFi” stopped being a value driver. Only protocols with real fee generation, real users, and real code resilience survived.
For blockchain investors, the lesson is clear: apply the same audit framework to AI infrastructure. Verify the revenue per GPU, the utilization rate, the contract terms. Do not accept narrative as a substitute for data.
Zero knowledge, infinite accountability. The market is now demanding proof. The code executes, not the promise. And the code of the AI market is now being compiled by real earnings, not narrative.
Audit first, invest later.
Forward-looking thought: The next dislocation will come when the inference economy’s revenue projections fail to materialize. Watch for the first major AI software company to report declining query growth. That will be the catalyst for the final washout of the AI trade, and the beginning of a new, more disciplined market where only the most efficient infrastructure survives.