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Also, the OpenAI Anthropic IPO 2026 race is reshaping how founders should think about their AI infrastructure dependencies. The AI industry is sprinting toward the public markets. OpenAI and Anthropic are both circling IPO territory. For founders building on their platforms, this matters more than most realize.

Furthermore, this is not just a finance story. It is a product strategy story. When the platforms you depend on go public, their priorities shift. Understanding that shift is how you stay ahead of it.

What is Actually Happening

Moreover, openAI has been exploring a full for-profit conversion. Reports suggest a valuation target north of one trillion dollars. Anthropic is not far behind, with investors watching closely.

In addition, both companies have raised enormous amounts of capital. Both are burning through it at scale. Going public is the natural next step. It gives them liquidity, credibility, and a public scorecard.

However, that public scorecard is the part founders should pay attention to.

How Public Markets Change Platform Behavior

Specifically, private companies optimize for growth and survival. Public companies optimize for quarterly earnings and analyst expectations.

Consequently, that shift changes everything about how a platform behaves. Pricing becomes a lever for margin expansion. API access gets tiered more aggressively. Free tiers shrink or disappear. Enterprise contracts get prioritized over indie developers.

Therefore, this is not cynicism. It is just how public company incentives work. Wall Street rewards predictable, growing revenue. That pressure flows downstream to every decision the platform makes.

Indeed, founders who built on Twitter’s API learned this the hard way. Founders who built on Facebook’s platform learned it earlier. The pattern repeats because the incentives never change.

Three Specific Risks for AI Founders

1. Pricing Volatility

In fact, right now, AI API pricing is artificially low. OpenAI and Anthropic are subsidizing access to drive adoption. That subsidy ends when they need to show margin expansion to public investors.

Meanwhile, if your unit economics depend on current API pricing, you are building on a foundation that may not hold. Model the worst case. Assume prices double or triple. Does your business still work?

2. API Stability and Deprecation

Similarly, public companies move fast to retire costs. Older model versions are expensive to maintain. When OpenAI or Anthropic needs to cut infrastructure costs, older APIs become targets.

Certainly, you have probably seen this already. Models get deprecated with relatively short notice. That timeline is likely to get shorter post-IPO, not longer. Build abstraction layers that let you swap providers quickly.

3. Terms of Service Tightening

Of course, public companies have legal and compliance teams that grow fast. They get conservative. Use cases that are fine today might require special approval tomorrow.

Besides, this is especially relevant for anyone building in healthcare, finance, or legal. The platforms will want to reduce liability exposure. That means more restrictions on what you can build.

The Opportunity Side

Still, there is a flip side to all of this. IPOs bring resources. More capital means faster model improvements. It means more stable infrastructure. It means better documentation and developer support.

Public companies also become more predictable in certain ways. Roadmaps get published. Earnings calls reveal strategic priorities. You can actually plan around what they are building.

The risk profile changes, but so does the information asymmetry. That is worth something.

What Founders Should Do Right Now

Audit Your Platform Dependency

Yet, how much of your product relies on a single AI provider? If the answer is most of it, that is a concentration risk. Not all concentration is bad, but you should know your exposure.

Also, map out which features use which APIs. Identify which ones have alternatives. Prioritize building abstraction layers for the highest-risk dependencies.

Model the Price Sensitivity

Furthermore, run your unit economics at 2x and 3x current API costs. If your margins collapse, you need a plan. That plan might be switching providers, fine-tuning your own model, or repricing your product.

Moreover, the time to figure this out is before it happens. Not after your API bill doubles.

Diversify Across Providers

OpenAI and Anthropic are not the only options. Google, Meta, Mistral, and dozens of open-source alternatives exist. Some tasks are better handled by smaller, cheaper models anyway.

A multi-provider strategy is more complex to maintain. However, it gives you negotiating leverage and resilience. Both become more valuable as these platforms go public.

Build Toward Data Ownership

The most defensible AI startups are not the ones with the best model access. They are the ones with proprietary data. Your data moat is the one thing OpenAI cannot replicate after their IPO.

Every customer interaction, every feedback loop, every dataset you collect is compounding. Invest in your data infrastructure now. It will matter more later.

The Bigger Picture: What the OpenAI Anthropic IPO 2026 Race Means

The OpenAI Anthropic IPO 2026 race is a signal, not just an event. It signals that the AI infrastructure layer is maturing. The land-grab phase is ending. The consolidation phase is beginning.

In every previous technology wave, the companies that survived consolidation had one thing in common. They were not dependent on a single platform for their core value.

The database layer consolidated. In addition, the cloud layer consolidated. The mobile platform layer consolidated. Each time, the startups that built on top survived by having something the platform could not easily absorb.

For AI founders, that something is customer relationships, proprietary data, and vertical expertise. The model is a commodity. Your context around the model is not.

A Note on Timing

The IPOs are not a cliff edge. The changes described here will happen gradually. Pricing will shift over quarters, not overnight. Terms will tighten incrementally.

That gradual pace is actually the danger. It is easy to ignore slow-moving risks. By the time pricing doubles, you have already built deep dependencies that are painful to unwind.

Start adjusting now, while the cost of change is low. A small investment in platform resilience today buys a lot of optionality later.

Final Thoughts

OpenAI and Anthropic going public is genuinely exciting. These are transformative technologies, and broader access to capital accelerates what they can build.

At the same time, founders need clear eyes about what it means for them. Platform risk is real. It has ended promising startups before. It will again.

The founders who thrive through this transition will be the ones who used the current window wisely. They built their data moats. However, they diversified their dependencies. They modeled their downside scenarios.

The AI IPO race is happening whether you are ready or not. The question is whether you have positioned your startup to benefit from the maturation, rather than get squeezed by it.

Want to think through your platform exposure? Read our build vs buy framework for a practical starting point. You might also want to review our other founder resources on building resilient AI products. For the broader context on AI market dynamics, Sequoia’s analysis of the AI token economy is worth your time.

Meta Just Blinked. You Should Pay Attention.

Also, meta reportedly delayed their next flagship AI model, codenamed “Avocado”, after sinking billions into training it. Indeed, the delay is framed as a technical setback. But I think it’s a signal. A very loud one. We are watching AI diminishing returns play out in real time, at the highest level.

Furthermore, this isn’t a story about Meta. Of course, Meta will be fine. After all, they have the money to try again. This is a story about the arms race eating itself. And what it means for everyone building with AI.

The Math Behind AI Diminishing Returns Is Brutal

So here’s what the training cost curves actually look like. GPT-2 cost around $40,000 to train. GPT-3 cost around $4 million. GPT-4 cost somewhere between $50M and $100M. The next generation? Estimates run north of $1 billion for a single training run.

Meanwhile, what did end users actually gain from GPT-3 to GPT-4? Meaningful improvement. From GPT-4 to whatever comes next? Most users cannot tell the difference on real-world tasks. The benchmarks improve. The user experience often does not.

That is the definition of AI diminishing returns. Each dollar buys less capability. Each capability gain delivers less real-world value. Yet the narrative keeps saying: you must chase the frontier or you’ll fall behind.

Moreover, who benefits from that narrative? The GPU manufacturers. Not you.

Why “Good Enough” Is Wildly Underrated

In fact, there’s a model available right now that writes production code. It summarizes documents, analyzes data, and generates marketing copy. Moreover, it costs fractions of a cent per call. It runs reliably. It’s probably not the newest model.

However, for the vast majority of business use cases, it’s completely sufficient.

In addition, think about the actual jobs companies are automating with AI. Drafting emails. Extracting data from PDFs. Summarizing support tickets. Categorizing feedback. Answering FAQ questions. For example, none of these tasks require frontier-level reasoning. They require a decent model plus a well-designed workflow.

Moreover, the gap between “good enough” and “frontier” is shrinking. Models that were considered cutting-edge eighteen months ago are now open-source and free to run locally. The frontier moves, but the useful middle keeps getting better too. AI diminishing returns means the frontier costs ten times more for improvements that matter ten percent more.

Similarly, most builders are optimizing for the wrong variable.

The Companies Actually Winning Aren’t Model Chasers

Meanwhile, look at the AI companies generating real revenue. They are not building their own frontier models. They are building workflows, interfaces, and integrations on top of existing models.

Specifically, they’re winning because they figured out the hard parts. The hard parts are not the model. The hard parts are reliable data pipelines. Consistent prompt engineering. Context management at scale. Human-in-the-loop checkpoints. Error handling. User trust. Fast iteration cycles.

In other words, the moat is operational. It’s about execution. It’s about ten thousand small decisions. Specifically, those decisions make AI useful inside a real product, for real users.

Indeed, a startup that spends six months chasing the best model is burning runway. A startup that spends six months building a rock-solid workflow on a good-enough model is building a business. The difference in outcomes is dramatic.

Furthermore, when a better model drops, workflow-first companies upgrade in a day. They swap the model out like swapping a supplier. But model-first companies are structurally dependent on the frontier. They have to chase it forever or die.

What This Means for Startups Specifically

In fact, if you’re a startup building with AI right now, here’s my honest take: stop reading model benchmark leaderboards. They are not your business.

Instead, ask these questions. What workflow are you automating? Notably, what data do you have that others don’t? In fact, what does “good enough” look like for your specific user, on your specific task? How do you make the output reliable enough that users actually trust it?

Because of this, the best AI startups I’ve watched succeed all share one trait. They got obsessive about the workflow long before they got obsessive about the model. They treated the model as infrastructure, important, but not the product.

Also, there’s a financial argument here. Running GPT-4-class models via API at scale is expensive. Running slightly older but still very capable models is a fraction of the cost. That margin difference compounds fast. A company with 40% AI infrastructure margins can reinvest in product. A company burning on frontier API costs cannot.

Still, I know what you’re thinking. But what about the use cases that actually need the frontier?

The Counter-Argument, And Why It’s Narrower Than You Think

Specifically, yes, frontier models matter. I’m not arguing they don’t. But let’s be honest about which use cases genuinely require them.

Consequently, complex multi-step reasoning chains. Novel scientific research assistance. High-stakes legal or medical document analysis where nuance is critical. Agentic systems making dozens of interdependent decisions. These are real. These exist.

However, these represent maybe 10-15% of actual AI use cases in production today. The other 85-90%? They’re doing things like: classify this support ticket, generate this product description, summarize this call transcript, answer this FAQ. For those tasks, frontier models are overkill. Sometimes they’re even worse, slower, more expensive, and prone to over-thinking simple tasks.

Yet most companies build as if they’re in that 10-15%. They justify frontier costs by imagining future complexity. In other words, they pay for headroom they never use.

Consequently, they’re essentially buying a Formula 1 car to commute to work. It’s impressive. It’s expensive. It does not make the commute better.

The Real Competitive Advantage in the Next Two Years

Likewise, here’s where I think this goes. The winners in the next wave of AI won’t be the companies with the biggest models. They’ll be the companies with the best workflows, the best data flywheels, and the fastest iteration loops.

Moreover, as open-source models continue to improve, the competitive advantage of proprietary frontier models keeps shrinking. Llama-class models are already good enough for a huge range of tasks. That range will expand. The gap between “free, open, local” and “expensive, proprietary, frontier” will narrow.

Therefore, any strategy that depends on permanent frontier access is fragile. The ground keeps shifting. The only stable ground is operational excellence.

Build great workflows. Own your data. Move fast. The model is a commodity. Your process is the product.

What Meta’s Delay Actually Tells Us

Meta’s “Avocado” delay isn’t embarrassing. It’s inevitable. When you’re spending billions to eke out marginal gains on benchmarks that don’t map cleanly to user value, delays happen. Because the returns are diminishing. Because the problems are getting harder faster than the compute is getting cheaper.

In addition, the companies watching that story and thinking we need to keep up are falling for a trap. The race is real. But it’s not your race. You are not Meta. You do not have their cash, their infrastructure, or their strategic reasons for owning frontier AI.

But you do have something they don’t. Instead, you can move fast, stay lean, and build for your users. No billion-dollar training runs required.

Use that.

The era of AI diminishing returns on frontier chasing is here. The builders who recognize it early will win. In contrast, the treadmill runners will spend a fortune to stay in the same place.

Stop chasing the frontier. Start building the workflow.

See also: visual customer support.

For additional context, see OpenAI’s research on AI capabilities.

The Quote That Should Scare Every SaaS Investor

Also, this week, Oracle’s CEO stood up and said AI won’t replace software companies. Similarly, other executives echoed the same line. AI replacing software companies is, apparently, not something we need to worry about. Supposedly, everything is fine. Apparently, the industry is safe.

Furthermore, that’s a lie. And indeed, they know it.

But here’s the thing. Honestly, I don’t blame them. They have to say it. Naturally, their stock price depends on it. Their employees need to hear it. Their board wants reassurance. So they say it, confidently, into microphones, and hope the market believes them long enough to buy time.

Moreover, it won’t work. Let me explain why.

Why CEOs Cannot Tell You the Truth

In addition, public company CEOs operate inside a very specific incentive trap. Their compensation ties directly to stock performance. Their stock performance ties directly to investor confidence. Investor confidence ties directly to narrative.

So when a reporter asks, “Will AI disrupt your business model?”. there is exactly one acceptable answer. No. The variations are predictable. “AI is a tailwind, not a headwind.” “We’re embracing AI.” Their customers need them. More than ever.

However, there’s one thing they cannot say: the honest version. “A large chunk of what we sell is. A UI wrapper around a database. And AI agents are about to make that UI irrelevant.”

Similarly, because that’s the truth. And the truth, in this case, is a stock-price event.

Furthermore, it’s not just the CEOs. Analysts don’t want to hear it. Institutional investors don’t want to write it in their reports. The entire ecosystem has a financial incentive to be wrong. They stay wrong slowly, carefully, until it’s too late to matter.

What AI Agents Actually Do to SaaS

Meanwhile, here’s what’s actually happening under the surface. AI agents don’t need your UI. They call your API directly.

Indeed, think about that for a second. Most SaaS products are essentially a pretty interface on top of a database. Some business logic sits in between. That’s it. The UI exists because humans need it to interact with the data. But AI agents aren’t human.

In fact, an AI agent can authenticate to your API and pull the data it needs. It transforms that data, acts on it, and pushes results. All without a single pixel being rendered. No login screen. Furthermore, no dashboard. Moreover, no filters. No export button. Just a JSON response and a task completed.

In other words, the “product” that millions of users pay for. the interface. becomes optional scaffolding. And optional things don’t get paid for.

Specifically, this is the real threat. Not that AI writes better software. But that AI uses software differently. in ways that bypass the entire value proposition of most SaaS products.

Which Categories Die First

Consequently, not all SaaS is equally exposed. But some categories are walking dead. They just don’t know it yet.

Reporting Dashboards

Likewise, if your entire product is “connect your data, see charts,” you are already obsolete. AI can query your data source directly and synthesize insights in plain language. Moreover, it can do it on demand, in context, without a BI tool in the loop at all. The dashboard is a relic of the pre-language-model era.

Basic CRMs

Besides, a CRM that’s essentially a spreadsheet with email integration is in trouble. AI agents can manage contact records, draft follow-up emails, log activity, and surface deal risks. No human ever needs to open the app. The CRM becomes a data store, not a product. And data stores are commodities.

Template-Based Tools

Certainly, invoice generators. Proposal builders. Contract templates. Form creators. These products exist because generating structured documents used to require software. Now it requires a prompt. The whole category collapses into a single conversation.

Single-Integration Workflows

Obviously, any tool whose core job is “move data from A to B” is in immediate danger. AI agents can write and execute that logic dynamically. You don’t need a no-code tool when the agent itself is the workflow engine.

What Actually Survives. and Why

Also, here’s where it gets more nuanced. Some software isn’t dying. In fact, some of it gets stronger in an AI world. But the reasons are different than most people think.

Furthermore, the moat isn’t features. Features are copyable. The moat is switching cost and data gravity.

Complex Workflow Software

Moreover, eRP systems, hospital management platforms, supply chain software. these tools embed into operations so deeply. Replacing them isn’t a product decision. It’s a multi-year transformation project. As a result, even if AI agents can technically do what they do, the cost of transition keeps them alive. The switching cost is the product.

Network-Effect Platforms

In addition, marketplaces, collaboration tools, and communication platforms derive value from who else is on them. Slack isn’t valuable because of its feature set. It’s valuable because your team is on it. Therefore, AI can’t replicate that. it can only participate in it. These platforms survive by becoming the substrate AI agents operate within.

Institutional Knowledge Stores

Similarly, software that accumulates years of proprietary data has a different kind of moat. Your CRM’s value isn’t the CRM. it’s ten years of customer interaction history that lives inside it. Moving that data is painful. Losing it is worse. Consequently, the data gravity keeps customers locked even when the UI becomes irrelevant.

Regulated Industries

Healthcare, finance, legal. these sectors move slowly by design. Compliance requirements, audit trails, certification needs. Still, even here, the pressure is building. AI replacing software companies will reach regulated verticals. It’ll just take longer.

The API-First Future Is Already Here

The savvy builders already see this coming. They’re not building UI-first products anymore. Instead, they’re building API-first platforms and treating the UI as an optional layer on top.

This is the right instinct. If an AI agent can use your product as effectively as a human can, you’ve built something durable. But if your product’s value disappears the moment you remove the visual layer. you have a problem.

Furthermore, the winners in this next cycle won’t be the prettiest dashboards. They’ll be the deepest data stores and the most trusted APIs. They’ll be the platforms AI agents choose to connect to, because the data there is irreplaceable.

Think about what that means for product strategy. You’re not designing for human eyes anymore. You’re designing for machine consumption. Your documentation matters more than your onboarding flow. Your API reliability matters more than your color palette.

How to Survive AI Replacing Software Companies

So if you’re building software. or running a software company. here’s the honest assessment.

1. Audit your moat honestly. Is your value in the UI? If so, you’re exposed. What would happen if users could get the same outcome without ever opening your app?
2. Invest in data gravity. Make your product the place where important data lives. The more proprietary, irreplaceable data you accumulate, the harder you are to displace.
3. Build for agents, not just humans. Your API is your product. Treat it that way. Document it obsessively. Make it reliable. Make it the thing an AI agent would choose to call.
4. Increase switching cost deliberately. Integrations, workflows, institutional memory. the more embedded you are in how an organization operates, the safer you are. This isn’t lock-in for its own sake. It’s survival.
5. Stop listening to CEOs whose stock price depends on denial. The incentive to downplay AI replacing software companies is enormous. The people telling you it’s fine are the people who can’t afford to tell you otherwise.

The Honest Version of What Comes Next

AI replacing software companies isn’t a prediction. It’s already happening in the categories where the UI was the only thing. Reporting tools are losing ground to AI-native analytics. Basic CRMs are getting hollowed out. Template tools are collapsing into prompts.

However, the timeline isn’t overnight. There’s still a window. But that window is measured in years, not decades.

The companies that survive will stop pretending the threat isn’t real. They’ll start building like it is. Not because their CEO said so in a press conference. But because the builders inside those companies are honest about what they’re actually selling.

Your product isn’t your UI. It never was. It’s the outcome you deliver, the data you hold, and the cost of walking away. Build around those things. Everything else is at risk.

The CEOs will keep saying everything is fine. Meanwhile, the smart money is already repositioning. Don’t wait for the press release that admits the truth. It won’t come.

See also: visual customer support.

For additional context, see OpenAI’s research on AI capabilities.

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