AI economics: How can SaaS startups prepare for the market correction?

The question isn't "Will the AI bubble pop?"

The question is, "What will be left standing when it does?"

SaaS founders are conditioned to look for 80% gross margins and infinite scalability with near-zero marginal costs.

Generative AI, in its current iteration, breaks that model. There is a non-zero chance that the current hype cycle collapses, leading to higher prices and a messy consolidation.

Fortunately, a total crash where the technology vanishes is unlikely.

Instead, we are staring down the barrel of a massive market correction, similar to the Dot-com bust, where the noise dies and the real infrastructure remains.

Here's my take on the future of AI economics, without the usual doom or hopium.

1. The "pop" is actually a consolidation event

Is there a bubble? Yes, and it's already deflating.

But we must distinguish between a valuation bubble and a utility collapse.

AI’s usefulness isn’t going away, but its current business models are.

The death of the AI wrapper

The most vulnerable players are the "3 ChatGPTs in a trench coat" startups.

These companies have no distribution advantage and no data moat. They're relying on arbitrage—selling a simplified interface for a model that is rapidly becoming accessible to everyone.

They will die, pivot, or be acqui-hired. Their features will be absorbed into foundation models themselves (PDF analysis, chatbots, basic copywriting—already happening).

If your moat is just calling an API… you don’t have one.

The enterprise reality check

Enterprises overspent early. They ran dozens of pilots, POCs, and experiments that generated zero revenue impact and zero headcount reduction.

Now we’re seeing CIOs tightening budgets, consultants retreating, and procurement asking the only question that matters: “Does this move a number we care about?”

Funding has already shifted from “spray and pray” to “show me the ROI.”

Similarities to the Dot-com purge

In 2000, the internet didn't go away; Pets.com did, and AI will see a similar trajectory.

Likewise:

• 90% of AI-washed products will vanish

• Vertical specialists will survive and thrive

• Infrastructure giants (Google, Amazon, Microsoft/OpenAI) will remain

Eventually, "AI" will stop being a category and simply become the electricity powering the SaaS grid.

2. OpenAI will likely struggle with the unit economics of intelligence

For a SaaS founder, the scariest realization is that marginal cost in AI is not zero.

Unlike traditional software, where adding the millionth user costs pennies, adding the millionth user to a frontier model costs significant compute.

AI providers face two massive challenges:

The CAPEX problem: Training frontier models requires billions in capital expenditure (GPUs, energy, data centers).

The OPEX problem: Inference at a global scale costs billions more.

Margins are going to get squeezed

Model providers face a brutal squeeze.

Customers want GPT-4 intelligence at GPT-3.5 prices. Meanwhile, open-source models (Llama, Mistral) are dragging the price floor down.

If "good enough" intelligence becomes free via open source, OpenAI’s pricing power on the low end evaporates.

Can they become profitable?

Plausibly, yes, but not with traditional SaaS margins.

Option 1: Become the "AWS of reasoning": If they become the underlying infrastructure for the world's software, volume compensates for margin.

Option 2: Use AI as a loss leader: The biggest risk to pure-play AI labs is if Google or Microsoft decides to treat intelligence as a loss leader to sell cloud contracts or Office 365 seats. This would distort the market and make it incredibly hard for independent players to compete on price.

3. Will AI get more expensive?

The answer is nuanced. We are heading toward a bifurcated market.

Scenario A: The short-term spike (the "premium" tier)

Reasoning depth may get more expensive before it gets cheaper.

• GPU shortages

• Supplier consolidation

• Frontier features gated behind enterprise plans

• Higher inference costs for advanced reasoning and agents

Frontier intelligence could become a luxury commodity.

Scenario B: The long-term deflation (the "commodity" tier)

Meanwhile, broad-access AI will get cheaper:

• Distillation makes smaller models smarter

• Sparsity reduces compute used per token

• Hardware efficiency improves

The result: A two-tiered world

The frontier tier: Expensive, slow, highly intelligent models used for complex coding, legal analysis, and high-stakes decision-making.

The mass-market tier: Cheap (or free), fast, "good enough" models embedded in every OS, browser, and device for summarizing emails and basic tasks.

4. Strategic implications for SaaS founders

If you are building in this space, your strategy cannot rely on "AI" being your differentiator. That differentiation is eroding every day.

My 5-year outlook

The days of raising seed rounds on a slide deck and a waitlist are over. VCs will demand real unit economics.

Expect the foundation model layer to solidify around 3-4 giants, so don't over-attach your tech stack to a single vendor that might get acquired or shut down.

Intelligence will also become a utility, much like bandwidth.

Nobody markets their startup as “powered by electricity.”

Soon, nobody will market it as “powered by AI” either.

How can SaaS startups prepare?

1. Decouple from AI vendors

Build an abstraction layer. If OpenAI raises prices or degrades performance, you need to be able to swap to Anthropic or Llama with minimal friction.

2. Build "real" moats

Your moat is no longer the LLM. It is your proprietary data, your workflow integration, and your distribution.

For example, don't build a contract generator. Build a legal workflow management platform that happens to use AI to draft clauses, but keeps the lawyer in the app via superior UX and integrations.

3. Prepare for pricing volatility

Don't lock your customers into fixed-price contracts that assume API costs will only go down.

Build buffer into your margins or use usage-based pricing to pass potential infrastructure cost spikes to the customer.

4. Don't bet on AI going away (it won't)

But don't bet on the current "cheap and easy" era lasting forever either.

Build for a world where intelligence is abundant, but high-quality reasoning is a premium asset.