Alex Karp was asked how you write unlimited liability into an S-1. His response was simple: “You’re assuming there will be an S-1.” That comment raises a much bigger question about OpenAI and Anthropic. The issue is not simply whether their models are valuable or how high their valuations can go. It is what happens when enormous AI spending, private credit, liability, and public-market scrutiny all collide.
Karp’s comment does not establish that OpenAI or Anthropic will never complete an IPO. OpenAI has already submitted a confidential draft S-1, but submitting the filing does not mean the company will ultimately proceed with a public offering. Our position goes further: we think the combination of financial obligations, liability, and changing competition could prevent either company from reaching a conventional IPO.
An IPO Puts the Risk in Daylight
Going public subjects a company to extensive financial reporting, audited financial statements, risk disclosures, and regulatory scrutiny. That means issues surrounding AI systems cannot simply stay in the background. Copyright problems, chatbot behavior, agent breakouts, and other potential risks become part of the conversation when the company has to disclose its exposure.
The bigger issue is liability. If the downside becomes large enough, the question changes from how much the company is worth to who ultimately carries the risk. Our argument is that this level of scrutiny could expose financial and liability problems that are much harder to keep in the background once a company enters the public markets.
The Debt Problem
OpenAI and Anthropic are also being discussed in the context of private credit. Our argument is that their financial obligations and continued spending on AI development create serious pressure around the IPO question. That is an interpretation of the economics, not a claim that every detail of their financial position has been independently established here.
The contrast is with companies that already have major revenue-producing businesses. SpaceX has rockets and Starlink revenue. Facebook has revenue. Other large technology companies are also presented as having established systems that generate money. OpenAI and Anthropic, by contrast, are being viewed through the combination of their technology, spending, financial obligations, and expectations around future value.
The government question adds another layer. The discussion around Treasury Secretary Scott Bessent is framed around the government not serving as a liability shield for AI companies. Our broader argument is that if there is no government backstop for the downside, the companies have to carry more of that financial and liability pressure themselves.
The Liability Is the Real Problem
The AI safety discussion is closely connected to this financial argument. Powerful systems create risks involving copyright, agents, and unexpected behavior. Those risks can become real liabilities for the companies building and deploying them.
The Hugging Face incident is presented as an example of why responsibility matters. The position expressed is that humans managing these systems remain responsible rather than simply blaming AI agents for what happens. The concern is therefore not simply about an AI system acting like a science-fiction villain.
The real concern is what happens when powerful technology is deployed, something goes wrong, and the company behind it has to carry the legal and financial consequences. That potential exposure becomes particularly important when a company has to disclose its risks as part of going public.
Open-Weight Models Are Changing the Trend
The growth of open-weight models creates another pressure point. Figures from Vercel AI Gateway are cited showing open models at 78% of token volume compared with 21% for closed models. The focus here is the trend rather than the exact dollar amounts.
There is an obvious limitation: Vercel users are more technical, so the figures do not necessarily represent everyone using AI. The average consumer is not necessarily building a local AI stack. But technical users are increasingly able to work with open-weight models and build their own infrastructure around them.
That creates a different path from simply renting access to a closed AI platform. Developers can use open-weight models and create systems that give them more control over how their AI stack operates. If that trend continues, control of the surrounding infrastructure becomes increasingly important.
The Harness Can Be as Important as the Model
The model itself is only part of the equation. The infrastructure around it can determine how useful that model becomes. Anthropic’s models were great, but Claude Code became even more powerful because of its harness. That same idea is behind OpenMonoAgent.ai.
OpenMonoAgent.ai is a terminal-native coding agent powered by a local LLM. It provides a framework for running an agent on your machine and using an open-weight model while keeping control of the stack. The point is not simply to have access to a model. The point is to have the infrastructure that makes the model useful.
A strong open-weight model combined with the right harness gives developers another way to build and run AI systems. Instead of depending entirely on a closed provider, the stack can be operated locally.
AI Should Be Infrastructure You Own
AI should not simply become another subscription. Our thesis is that AI can become infrastructure that sits on your own machine, serves your code, and answers to you. That means having more control over what you run and how you run it.
That is the role OpenMonoAgent.ai plays in this approach. It is described as open source, terminal native, and powered by local LLMs, with zero API costs, zero telemetry, and full ownership. The goal is to take some of the power away from companies such as OpenAI and Anthropic and put it back into the hands of developers.
This creates a different relationship with AI. Instead of continuously renting access to someone else’s infrastructure, developers can run their own stack using open-weight models. The more accessible that approach becomes, the more important ownership and control become.
The Model Race Is Getting More Competitive
OpenAI and Anthropic also cannot rely forever on simply having the best models. New models are entering the market, and the competition includes companies such as Meta, xAI, and Google. The broader position is that the frontier model race is becoming more competitive.
That matters because benchmark performance alone does not solve the financial problem. If other companies can produce highly capable models while developers can also use open-weight systems locally, then the value of owning the infrastructure becomes more important. The model is only one part of the stack.
Our argument is that OpenAI and Anthropic therefore face pressure from both sides. Their financial obligations and spending create pressure, while the technology itself is becoming more competitive and accessible through open-weight models.
What Happens to OpenAI and Anthropic?
The prediction is direct: we think OpenAI and Anthropic may never reach a conventional IPO. That is a stronger position than Karp’s “You’re assuming there will be an S-1” comment. Karp raised the question of how unlimited liability could be handled; our conclusion is that the combination of liability, financial pressure, and changing competition could make a conventional IPO unlikely.
There is also a prediction that the companies could eventually be sold off for parts that other companies want. Their technology and different pieces of their businesses could attract buyers, while companies with established revenue remain in a different financial position. These are predictions, not established outcomes.
The larger question is whether companies carrying this level of financial and legal exposure can make the transition from private AI labs to public companies without exposing problems that become much harder to manage once the books and risks are subject to public scrutiny.
Build What You Can Control
The practical takeaway is less about predicting the next corporate move and more about how businesses should build. Solid engineering still matters. Database architecture, API design, system integration, scalable infrastructure, and software that actually works remain the foundation.
When AI belongs in a solution, it can be built into the architecture rather than simply bolted onto someone else’s API. The goal is to create infrastructure that a business can understand, control, and integrate into its own environment. OpenMonoAgent.ai fits directly into that approach by focusing on local AI, open-weight models, and greater control over the stack.
The AI conversation often focuses on models, benchmarks, and valuations. The more important question is what sits underneath all of them: who owns the infrastructure and who carries the risk? For businesses building serious software, those questions shape how much control they have over the technology they depend on.
If your organization needs custom software, strong engineering, and AI integrated where it actually makes sense, StartupHakk builds around those principles. The focus is on technology that works within the business rather than simply adding another dependency.
Technology should not only be powerful. It should be infrastructure you can own, control, and build on.
