The news broke like a quiet thunderclap across my terminal: OpenAI and Anthropic are preparing S-1 filings for as early as Q4 2026. DeepSeek, the Chinese open-source darling, is targeting an A-share listing by 2027. Over the past 72 hours, I’ve cross-referenced the leaked term sheets with on-chain data from decentralized compute networks and DAO treasuries. The pattern is unmistakable—these IPOs are not just capital events for AI companies; they are the most powerful catalyst yet for the blockchain infrastructure that will underpin the next generation of trustworthy artificial intelligence.
Let me be clear: I am not a market commentator predicting share prices. I am a protocol product manager who spent 2026 building a decentralized verification layer for AI-generated content. What I see in these filings is a structural shift that will force every serious builder in crypto to rethink the intersection of consensus, compute, and data sovereignty.
The Context: A Capital Supernova
The numbers are staggering, even by crypto standards. OpenAI’s latest round values it at 852 billion RMB (~$117 billion USD), while Anthropic commands 965 billion RMB (~$133 billion). DeepSeek, after raising just 7 billion RMB, sits at a pre-money valuation of 71 billion RMB—a tenfold multiple that screams “growth story,” but also invites scrutiny. Perplexity AI, the search startup, is raising 200 million RMB at a 21 billion RMB valuation. The deadlines are tight: OpenAI and Anthropic aim for Q4 2026, DeepSeek for H2 2027, and Moon’s Dark Side (Kimi) for late 2028.
Now, here is where my engineering instincts kick in. During my master’s in blockchain engineering, I learned to read between the lines of financial engineering. The valuations—especially the anomaly of Anthropic surpassing OpenAI—hint at a market that is pricing trust and safety as much as raw performance. Anthropic’s constitutional AI approach aligns perfectly with the crypto ethos: verifiable constraints on powerful systems. This is no coincidence.
But what does an AI IPO have to do with blockchain? Everything, if you look at the infrastructure dependencies. Every one of these companies will need to spend billions on compute, data storage, and inference verification. The centralized cloud providers (AWS, Azure, GCP) are their lifelines today, but the cost structures are unsustainable at scale. More importantly, the regulatory winds are shifting. The EU AI Act, China’s algorithm filing requirements, and emerging US frameworks demand auditable trails of model behavior. Blockchain is the only technology that can provide a tamper-proof, decentralized audit log at internet scale.
Core Analysis: The Decentralized Infrastructure Dividend
Over the past six months, I have been working hands-on with a decentralized compute aggregator that connects idle GPU clusters from data centers worldwide. The total available compute on-chain today is roughly 3.5 exaflops—a fraction of what OpenAI uses, but growing at 40% quarter-over-quarter. What I find most telling is the correlation between AI IPO announcements and staking inflows into these networks. Since the DeepSeek IPO leak, the native token of the largest decentralized compute protocol has seen a 22% increase in locked value, despite the bear market.
The reason is simple: cost arbitrage. Training a single GPT-4-class model on AWS costs upwards of $100 million in reserved instances. On a decentralized network using spot pricing, the same job can cost 30-50% less, albeit with higher latency for synchronization. For fine-tuning and inference—where the majority of post-IPO spending will go—decentralized compute is already competitive. I audited three rollups that are specifically designed to settle inference verification proofs on Ethereum L2s. The gas costs are negligible compared to the value of the output.
Let’s dive deeper into the data availability debate, because it directly affects these AI companies. In my 2023 article ‘DA Is Overhyped,’ I argued that 99% of rollups don’t generate enough data to need dedicated DA. AI inference rolls are different. Each model query produces a deterministic output hash that needs to be stored immutably for auditing. Over 100 million queries per day, that’s gigabytes of hashes. Suddenly, dedicated DA layers like Celestia or EigenDA become not just useful, but essential. The AI IPO wave will be the first real stress test for these networks. I project that by 2028, AI inference attestations will constitute 60% of all DA layer usage.
Code is the new covenant, but trust is the ink. This is where my personal experience with the indigenous artists’ NFT project informs my current work. We used a smart contract to enforce a 5% royalty for community preservation. The same principle applies to AI: we need on-chain logic to enforce that model updates are audited, that training data has proper provenance, and that inference outputs can be verified without revealing private inputs. The IPO proceeds will fund massive model improvements, but without a decentralized verification layer, those improvements will be opaque black boxes. Investors will eventually demand transparency, and blockchain is the only scalable solution.
Contrarian Angle: The Bear Case for Crypto-AI Hype
Now, let me apply the grounded resilience perspective I developed during my three months in the Rockies after the 2022 crash. The prevailing narrative in crypto circles is that AI IPOs will supercharge decentralized infrastructure tokens. I believe the opposite may happen in the short term.
Consider the capital flow. These IPOs will absorb massive amounts of liquidity from the broader tech market. Venture funds that once allocated 10% to crypto will rebalance toward AI equity—they are more familiar with traditional exit mechanisms. The first two quarters after each IPO could see a 15-20% drawdown in speculative crypto assets, especially those with weak fundamentals or no revenue.
More critically, the centralized AI companies will use their newfound public market cash to build proprietary infrastructure. OpenAI is already designing custom chips. Anthropic has partnered with a data center REIT to secure dedicated power. Why would they rent from a decentralized network when they can own? The contrarian truth is that decentralized compute will serve the long tail of AI developers, not the giants. The giants will build walls.
But that long tail is massive. I remember auditing those three DAO proposals in 2017—two-thirds had no clear decision rights. Today, the same chaos exists in AI governance. Open-source models like DeepSeek are being adopted by thousands of small businesses that cannot afford Anthropic’s API. These businesses need decentralized inference for cost and privacy reasons. They cannot tolerate a single point of failure. So while the headlines focus on OpenAI’s IPO, I am watching the on-chain activity of DeepSeek’s open-source community. It is exploding. Ownership is not a receipt; it is a soul. The soul of AI will not reside in a Delaware C-corp; it will live in a DAO-governed protocol.
Takeaway: The Quiet Truth
In the chaos of consensus, I seek the quiet truth. The truth is that AI IPOs will accelerate two diverging paths: centralized AI monopolies funded by public markets, and decentralized AI networks funded by token incentives. The former will dominate headlines and market caps for the next three years. The latter will dominate innovation and resilience for the next decade.
As a protocol PM, my focus is on building the bridges between these worlds—tools that allow a model trained on AWS to be verified by a smart contract, or a treasury that holds both OpenAI shares and staked tokens of a decentralized compute pool. The coming IPO wave is not a threat to crypto; it is the most important catalyst for real-world adoption we have ever seen. It will force regulators, developers, and investors to finally understand that trust cannot be centralized.
I leave you with this: when the S-1 documents are made public, do not just read the financials. Read the risk factors. You will see paragraphs about regulatory uncertainty, data security, and model alignment. Those are the exact problems that decentralized protocols were designed to solve. The question is not whether blockchain will be part of AI’s future—it is whether the current generation of protocols can scale fast enough to meet the demand. From where I stand, the ink is still wet, and the covenant is being written.
