The Next Battlefield Isn't a Model Card: Bankers Push OpenAI and Anthropic Toward a Bond Market That Wasn't Built for Them

On September 8, 2024, the Financial Times slipped a quiet bomb into the AI narrative: investment bankers for both OpenAI and Anthropic are already pushing for top credit ratings after a future IPO. Not a new model release. Not a new safety framework. Not a breakthrough in reasoning benchmarks. The news, at first blush, reads like a spreadsheet error. OpenAI and Anthropic are still burning extraordinary amounts of capital. Neither has produced a consistent annual profit. Both are racing to spend billions on compute that might be obsolete in eighteen months. And yet the most sophisticated financial minds in the world are preparing these companies to borrow money at the kind of interest rates normally reserved for utilities, hospitals, and companies with decades of audited earnings. The ledger remembers what the hype forgets. This is not a story about artificial intelligence. It is a story about capital structure, and the AI industry is about to learn the same lesson crypto learned in 2017, in 2020, and again in 2022: the market will let you ride the peak of the ape mania wave for a while, but eventually it asks who pays the debt. Let me put the headline into perspective, because most of the coverage I have seen in the Web3 echo chamber missed the real signal. The report says the banks representing the two biggest AI labs are laying groundwork so that, after their IPOs, the companies can obtain the highest possible credit ratings. This is not a casual conversation. Credit ratings are the passport to the bond market. Investment-grade ratings, and especially the top tier of AAA or AA, would let OpenAI and Anthropic issue debt at yields around 4 to 5 percent. Compare that to the implied cost of equity that these companies have been selling since their founding. When a venture capitalist puts money into a frontier AI lab, they are usually pricing in a 15 to 25 percent annual return. That gap is the most underreported number in the entire artificial intelligence sector. If OpenAI or Anthropic can borrow at 5 percent, then every additional dollar of growth capital gets dramatically cheaper. They can fund their GPU arms race without handing over more equity. They can extend their runway without recruiting new shareholders. They can, in effect, turn a hot startup into a cold, disciplined, leveraged machine. The timing matters. The report arrived at a moment when OpenAI's annualized revenue had already passed 3.4 billion dollars, according to the FT's own earlier reporting. Anthropic had crossed the 1 billion dollar annualized revenue threshold around the same period. Those numbers are impressive for young AI companies, but they are almost irrelevant in the context of the capital already committed to their missions. Microsoft has poured enormous sums into OpenAI, and its cloud is effectively the distribution arm of OpenAI's product strategy. Amazon and Google have each become strategic participants in Anthropic's ascent, supplying both cloud credits and capital. The structure is strange, almost feudal: the largest AI labs are not independent utilities. They are dependent on the very hyperscalers who also compete with them, directly or through frontier model rivals. A credit rating agency looking at OpenAI and Anthropic has to decide whether those strategic patrons are a source of implicit credit support or a concentration risk that makes the borrower weaker. I have been watching treasuries, DAOs, and protocol balance sheets long enough to know that when a single counterparty controls your compute, your distribution, and your funding, calling you an independent company is a legal fiction. The rating agencies will have to decide whether to believe the fiction or price it. The first thing to understand about credit ratings is that they are not issued in a vacuum. Rating agencies like Moody's, Standard & Poor's, and Fitch will not simply wake up one morning and hand OpenAI a AAA rating because the company is famous. A rating is a judgment about the likelihood that a borrower will repay its obligations under all kinds of stress scenarios. That means the bankers pushing this agenda are not making a phone call; they are starting a process that can take six to twelve months or more. They need financial history. They need audited financial statements. They need a governance framework that survives contact with public markets. They need a narrative that explains why an AI company that currently loses money will someday generate enough free cash flow to service not just its equity but also its debt. In other words, the banks are already telling us something important: the internal financial models at OpenAI and Anthropic have allegedly matured enough to survive that kind of scrutiny. The actual IPO might be months away, or it might never happen in the exact form the press expects. But the fact that bankers are willing to put their own reputations behind a credit rating push suggests that there is something real inside the financial planning of these companies. That is information gain that no model benchmark can give you. The word that matters most in the headline is push. You do not push for a rating when the rating agency is eager to give it to you. You push when there is friction. And there is enormous friction in this particular assignment. Think about what a traditional rating analyst has to evaluate when looking at OpenAI or Anthropic. There is no historical default data for artificial general intelligence companies. There is no clean comparable in the software industry, because software companies do not typically need to raise billions of dollars a year for raw compute. There is no established methodology for valuing a company whose moat depends on a research lead that could evaporate if an open-source model catches up, if a regulatory framework shifts, or if a handful of star researchers decide to leave. The rating agencies have methodologies for banks, for airlines, for oil and gas producers, for municipalities, and even for crypto exchanges after the industry matured. They do not yet have a methodology for a company whose core asset is the intelligence of a few hundred researchers, deployed through models that change every year. That is why the bankers are starting early. They are not merely requesting a rating; they are trying to shape the methodology itself. There is a cultural irony here that I cannot shake. Crypto spent the better part of a decade trying to build a parallel financial system that did not depend on credit rating agencies. DeFi protocols used overcollateralization instead of trust. Stablecoin issuers used reserve attestations instead of balance sheet opinions. DAOs issued bonds to their own communities and called it social consensus. And yet, after all that experiment, the two most important AI companies on Earth are preparing to kneel at the altar of Moody's, S&P, and Fitch. If they get their investment-grade ratings, they will do something most blockchain projects never could: they will access the deepest pool of global capital without giving away any more equity. Maybe that is the real lesson from the AI world to the crypto world. Rather than trying to invent a new category of credit, the AI labs are simply taking the old one and bending it to fit their new reality. I have said for years that the real driver of financial innovation in emerging markets is not blockchain ideology, it is inflation and the desperate search for survival tools. Something similar is happening with AI frontier labs. The driver is not ideological purity. It is the brutal arithmetic of compute costs. When your training run costs more than the GDP of a small nation, you will gladly spend a year in rating agency meetings if it means your next billion dollars costs five percent instead of twenty. The divergence between OpenAI and Anthropic is where this story gets subtle. A casual reader might assume that both labs are in the same position and will receive the same treatment from the rating agencies. That assumption is wrong. OpenAI and Anthropic have different revenue profiles, different customer bases, different strategic patrons, and different philosophies about how to structure themselves. Those differences will matter enormously in the credit analysis. OpenAI is building a platform that reaches both consumers and enterprises. It has driven down the price of tokens dramatically from GPT-4 to the more efficient GPT-4o family, signaling that it wants to win on cost curve and scale. For a rating analyst, that narrative looks like a classic software platform story: winner-take-most dynamics, growing usage, falling marginal costs, and a powerful distribution partner in Microsoft. Anthropic is following a different playbook. It has positioned itself as the safe, reliable, enterprise-grade AI vendor. Its pricing is generally higher than OpenAI's and its focus is on high-stakes industries like finance, healthcare, and law. For a rating analyst, Anthropic starts to look less like a consumer platform and more like a high-end SaaS company with strong recurring revenue, high switching costs, and customers who care more about trust than about saving pennies per token. Those are very different credit stories. One emphasizes growth elasticity; the other emphasizes predictability and retention. A rating agency cannot treat them as twins. Then you have to factor in the governance and mission question. OpenAI's legal structure is famously awkward. It began as a nonprofit with a capped-profit subsidiary, and it has been through a very public boardroom drama that included the brief ouster and reinstatement of its CEO. Senior safety leaders have departed in highly visible fashion. I covered enough crypto governance crises to know how a story like that reads on a credit committee's agenda. Credit analysts are not technologists; they are pattern recognizers. When they see a company whose board can fire the founder on a Friday and rehire him on a Tuesday, they start to wonder about the stability of decision-making for the next twenty years. When they see multiple safety researchers resigning and accusing the company of prioritizing speed over caution, they do not just file those headlines away as public relations noise. They ask whether those departures signal a risk that the company's model quality could degrade, or worse, that a future safety failure could trigger lawsuits, regulation, and existential reputational damage. In a credit rating, downside scenarios matter more than upside scenarios. An equity investor can afford to dream about a trillion-dollar future. A bondholder simply wants to get paid back. That asymmetry changes everything. Anthropic, meanwhile, has adopted a Public Benefit Corporation structure that formally allows its directors to consider safety and societal impact rather than narrowly maximizing shareholder value. To a certain kind of crypto purist, this looks noble. To a credit analyst, it raises a different question: if the company ever faces a situation where mission and money collide, which one wins? Debt holders generally prefer a borrower that is ruthlessly focused on debt repayment. A governance structure that deliberately restrains profit-seeking could make a rating agency nervous, or it could be seen as a stabilizing framework that prevents reckless behavior. No one knows, because no rating agency has ever tried to model a public benefit corporation whose product is potentially dangerous software. This is uncharted territory, and sometimes uncharted territory is exactly where the rating agencies get it wrong. I remember watching the agencies cling to outdated assumptions about structured credit in the run-up to the 2008 financial crisis. I also remember how late they were to recognize the risks embedded in crypto lenders before the contagion of 2022. The agencies are not omniscient. They are mapmakers who rely on previous maps. For AI, there is no previous map. The other unreported angle is the employee angle. An IPO creates liquidity for founders, early employees, and venture investors. But it also creates a new problem: newly wealthy shareholders who want to sell their stock without crashing the price. One of the quiet uses of a high credit rating is that it allows insiders to borrow against their wealth at cheap rates instead of dumping shares on the open market. If OpenAI or Anthropic can establish an investment-grade credit profile, the founding team and key employees may be able to use low-cost margin loans or other credit facilities to monetize their holdings gradually. That is a smoother exit than a mass sell-off. So the rating push is not just about funding data centers or financing working capital. It is also about giving the people inside these companies a way to diversify their personal balance sheets without triggering the kind of sell-off that torments public tech stocks after their lock-up periods expire. The banks involved in the IPO stand to collect fees on the offering. The same banks, or their affiliates, can collect additional fees on the debt offerings, on the rating advisory work, and on the private wealth management products sold to newly liquid employees. That is a web of conflicts that nobody wants to discuss in the press release, but it is central to understanding why the bankers are pushing so hard. They are not neutral messengers. The most important contrarian thought, however, is that this rating push might not be about an IPO at all. At least, not in the way we imagine. Consider the timing. The FT report landed in a market that was still euphoric about AI, yet both OpenAI and Anthropic are continuing to raise massive private rounds. Anthropic, in particular, has been in conversation with sovereign wealth funds and other private investors. When a company is negotiating with private investors, the ability to say we have an alternative, we can go public, we have a credit rating pathway ready is a very strong negotiating weapon. If a sovereign wealth fund starts to demand strict valuation terms, the company can point to its bank relationships and say: push us too hard and we will seek cheaper public capital instead. The credit rating push, in that context, is not a countdown to an S-1 filing. It is a carefully placed anchor that changes the terms of private negotiation. This is a poker move. And in poker, sometimes the best card is the one you show your opponent without ever intending to play it. There is also a more arcane financing path that the coverage has largely missed. If OpenAI and Anthropic need to fund compute-intensive projects, they do not necessarily need a top credit rating to borrow money. They might be able to secure equipment-backed financing, or so-called asset-specific structures, where the GPUs themselves serve as collateral. This is analogous to the ABS market that auto lenders and equipment financiers have used for decades. A data center full of Nvidia GPUs is a physical asset with real resale value, especially in a market where every AI startup on Earth is desperate for compute. Banks could create special-purpose vehicles that own the GPUs, lease them to OpenAI or Anthropic, and issue bonds backed by those leases. The credit analysis for that kind of deal depends on the value of the hardware, not on the long-term fate of the AI company itself. If the AI market collapses, the GPUs can be sold or leased to someone else. That dramatically changes the conversation. A rating agency might be deeply skeptical of a AAA rating for OpenAI as a company, but entirely comfortable with a AAA rating for a GPU-backed vehicle that has contractual payments from OpenAI. The article does not mention this possibility, and neither do most of the hot takes I have read. But it is the kind of elegant structural workaround that bankers love, because it lets them earn fees while sidestepping the hardest question, which is whether an unprofitable AI lab is actually creditworthy. Beyond the structural details, there is a larger cultural consequence. Public bond markets require disclosure. When a company issues public debt, it must open its books to the world in ways that private AI startups have never had to do. We will learn the real gross margins of the API business. We will learn the true cost of a single training run when allocated over the useful life of the hardware. We will learn how many enterprise customers actually retain their subscriptions after the first year. We will learn whether all that revenue is concentrated in a handful of whale clients or spread across a durable base of demand. That information will not just shape the credit markets; it will reshape the equity narrative. The same transparency captured in the ledger remembers what the hype forgets. As soon as public disclosures arrive, the AI industry loses the ability to hide behind vague metrics like token volume and headcount. It will have to answer the question that bond analysts have been asking since the dawn of modern finance: where does the cash actually come from, and how reliable will it be in a downturn? This is where I see the ghost of Ethereum's rise, fall, and resurrection. I was there in 2017 when the Ethereum network was going through its own version of a credibility crisis, complete with a time-lock vulnerability and an ICO market that looked like a monster ready to consume itself. The smartest people in that ecosystem believed that cryptographic trust could replace institutional gatekeepers entirely. And yet, by 2020, the same ecosystem was building mechanisms that mirrored the old system: automated market makers instead of exchanges, vaults instead of banks, governance tokens instead of equity. The evolution from raw decentralization toward a more hybrid form of capitalism was unstoppable. Uniswap taught us that code can create culture, but culture still needs a boring ledger at the end of the day. The AI industry is about to travel the same arc in reverse. It started as a mission-driven research pursuit, moved into a venture-backed product boom, and is now preparing to enter a phase where debt capital, credit ratings, and quarterly disclosures will constrain what these companies can do. The people who built the crypto and Web3 markets will recognize the pattern immediately because they have already lived it. It is the process of a movement becoming an industry and an industry becoming an institution. There is a painful irony for AI safety advocates in this transition. Credit rating processes are designed to evaluate the probability of extreme downside. For AI, some of the most extreme downside scenarios involve not just financial losses, but catastrophic deployment risks, model leakage, misuse by bad actors, and societal harm. In theory, a rigorous rating process could force AI labs to take safety more seriously, because safety failures would begin to appear as credit risks. That is a new incentive that the labs have never had. At the same time, public market discipline introduces a different kind of pressure. Once a lab has debt investors and quarterly revenue expectations, it must show growth every quarter. The kind of patient, open-ended research that underpins long-term alignment work could be sacrificed in favor of faster product releases and bigger sales numbers. A bond market that rewards near-term cash flow will not naturally reward investments in interpretability or red-teaming that might not pay off for years. So the credit rating push cuts both ways. It might make the labs safer by exposing them to external scrutiny, or it might make them more reckless by pushing them to chase the highest-pressure, highest-revenue opportunities at the expense of their own missions. The truth probably lies somewhere in between, and the rating agencies are not equipped to judge that balance. The next six to eighteen months will tell us whether this event is a historic inflection point or just another bankers' fantasy. I have compiled a short list of signals I will be watching. First, watch for a formal announcement from OpenAI or Anthropic naming the banks that will lead their IPO underwriting. If that announcement comes within the next few months, the credit rating push is real and connected to a concrete deadline. If it does not come, we should assume the rating exploration was a negotiation tactic oriented toward private markets. Second, watch for the rating agencies themselves. Moody's, S&P, and Fitch will probably publish methodology papers or issue public requests for comments if they are serious about developing an AI-specific credit framework. If they stay silent, they are still in the learning phase. Third, watch for any unsolicited rating, which the agencies can produce even without a fee arrangement. A leaked or unsolicited evaluation would reveal a lot about how traditional credit analysts view the frontier lab business model. Fourth, watch the fundraising rounds. If OpenAI and Anthropic keep accepting giant private checks from sovereign funds and strategic investors, that is a signal that an IPO is not imminent, no matter what the bankers whisper to journalists. Finally, watch the AI-specific financial disclosures that emerge from debt offerings or from the private issuance of asset-backed structures. Any deal that reveals the true unit economics of frontier AI will be more valuable than a thousand benchmark reports. Where does this leave the crypto and Web3 audience? More connected than most AI analysts realize. The intersection of AI and crypto has been mostly superficial, dominated by memecoins that pretend to be autonomous agents and GPU marketplaces that have not yet reached meaningful scale. But underneath that surface, the same financial machinery that once drove the ICO boom, the DeFi Summer, and the NFT mania is now turning toward AI infrastructure. The capital needs of frontier AI are so enormous that no single funding source can carry them. Equity, strategic investment, cloud credits, and eventually debt will all have to be combined in a way that resembles the hybrid capital stack of a mature crypto protocol. The bankers for OpenAI and Anthropic are not thinking about decentralized rails. They are thinking about the most efficient possible mix of funding sources. But their work will create a benchmark for every AI startup that comes after them. If the largest labs succeed in obtaining top credit ratings, then smaller AI companies will be judged by the same standard. That will make life easier for those with real revenue and much harder for those living on narrative alone. The crypto world has a lot to teach about surviving the transition from narrative to fundamentals, because it has failed at it so often. I think back to my own experiences in this industry. I was the one who published the panicked Ethereum wallet story back in 2017, moving faster than the auditors and learning the hard way that headline speed does not replace code review. I rode the DeFi Summer of 2020 by translating automated market makers into a party metaphor, turning protocol mechanics into social conversation, building a bridge between developers and curious retail users. I went all-in on the Bored Ape cultural wave in 2021, understanding that NFTs in China and elsewhere were becoming something closer to identity than to finance, and I paid for my enthusiasm when the floor price collapsed because I did not pay enough attention to the patterns in the data. I watched Terra and Luna disintegrate in 2022, and by the time I sat down to write, all I could offer the traumatized market was a reflection on the human cost of collapse rather than a technical diagnosis of the algorithmic failure. And recently I have found myself watching AI agents make trades on Farcaster and decentralized platforms, wondering whether we are decoding actual intelligence or just following footprints in the sand. Every one of those experiences taught me the same lesson: the story that feels the most alive in the moment is often the one with the least stable foundation. Now, looking at the prospect of OpenAI and Anthropic receiving top credit ratings, I see the same dynamic happening at a much grander scale. The euphoria around AI has created a magical window in which unprofitable companies can be treated as national champions and strategic miracles. The banks are simply trying to capture that magic before the window closes by converting narrative value into a cheaper source of debt. That is a deeply human endeavor. It is also deeply fragile. The ledger remembers what the hype forgets, and the ledger is patient. It will record every dollar borrowed, every GPU collateralized, every employee grant vested, and every safety researcher who walked out the door. Credit ratings are not predictions; they are apologies for the past masquerading as confidence in the future. The question is whether the rating agencies can learn to understand a technology company whose product is essentially an unpredictable intelligence, or whether they will simply do what banks always do, which is to issue a rating, collect a fee, and hope the machine keeps running. If they get it wrong, the next financial crisis might have a chatbot at its center. If they get it right, then the AI industry will finally have to grow up and face the same discipline that every other industry faces when it tries to borrow from the future. Either way, the game has changed. The days of thirty-million-dollar seed rounds and infinite strategic cloud credits are not over, but their dominance is fading. The frontier labs are preparing to meet the public markets, and the public markets are preparing to scrutinize them through the lens of debt. It is going to be messy. It is going to force disclosures that many founders would rather keep hidden. It is going to test whether the phrase AI safety can survive contact with a covenant-heavy bond indenture. And it is going to create a new set of winners and losers among AI companies, not based on who has the best model, but based on who can sustain the least cost of capital. In a world where compute is the new oil, the company with the cheapest borrowing capacity may end up with the most powerful intelligence. That is the real race now. It is not a benchmark. It is a balance sheet. And the ledger, as always, will remember everything the hype chose to forget.