ChatGPT reached one million users in five days. One hundred million in two months. No consumer product in history has achieved that adoption velocity. The capability it demonstrated — fluid, contextually aware, genuinely useful natural language generation — landed with the force of an inflection point.
What followed was entirely predictable: a gold rush. And like every gold rush, the noise is already drowning out the signal.
What’s Actually Happening Right Now
Within weeks of ChatGPT’s launch, thousands of startups have begun building on top of OpenAI’s API. Product Hunt fills daily with new AI tools. Investor inboxes overflow with decks featuring GPT-4 as a core technology component. The energy is real, the enthusiasm is genuine, and a significant portion of what’s being built will not survive the next eighteen months.
The pattern is familiar from earlier technology waves — the Web3 enthusiasm, the no-code explosion, the mobile app gold rush of a decade prior. Each wave generates a large volume of products that replicate accessible capability with minimal differentiation, alongside a smaller number of genuinely novel applications built by founders with domain insight that the underlying technology alone cannot replicate.
The current AI moment is following the same arc — but compressed. The feedback loop between capability release and product launch is shorter than any previous wave. That compression makes the signal harder to identify in real time, which is precisely why the distinction matters more now.
The Wrapper Problem
The most common failure mode visible in the current AI startup wave is what might be called the wrapper economy — startups building thin interface layers on top of OpenAI’s API, charging subscription fees for access to capability that is already available directly, differentiated only by a slightly different prompt or a cleaner UI.
The business model logic is understandable: the underlying capability is extraordinary, the barrier to building a basic product is low, and the appetite for AI tools is demonstrably high. But the structural vulnerability is equally clear.
A startup whose core value proposition is access to a large language model has no defensible position when the model provider improves the base product, reduces the API price, or launches a competing interface directly. OpenAI’s own product releases have already disrupted several wrapper categories that appeared viable just months earlier. This is not a theoretical risk — it is already happening.
The useful test for any AI product at this moment: if OpenAI ships a new model version or a native interface tomorrow, does the business get stronger or weaker? If the honest answer is weaker, the differentiation work hasn’t been done yet.
Where Genuine Defensibility Lives
The AI startups worth paying close attention to are not those with the cleverest prompts — they’re those bringing something to the table that the underlying model cannot supply on its own.
Genuine defensibility in the current AI landscape tends to cluster around a few distinct sources:
- Domain expertise — deep, specialized knowledge of a specific industry, workflow, or problem set that general-purpose models don’t have and can’t easily acquire; the model is a component, not the product
- Proprietary data — access to training or fine-tuning data that produces meaningfully better outputs for specific use cases than a general model can achieve
- Customer relationships and workflow integration — being embedded deeply enough in how a customer operates that switching costs are real, regardless of what the underlying model does
- Distribution advantages — reaching a specific customer segment through channels, partnerships, or trust relationships that new entrants cannot easily replicate
The founders building on these foundations are treating large language models the way previous generations of founders treated cloud infrastructure — as a powerful capability layer that enables the product, not as the product itself.
What the Venture Capital Moment Reveals
The capital flowing into AI right now is accelerating both the good and the bad outcomes simultaneously. Genuine AI companies with strong differentiation are getting funded faster and at better terms than they would in a neutral market. Wrapper companies with no defensible position are also getting funded, because the hype cycle creates an environment where investors move faster than their diligence normally allows.
This is consistent with the pattern observed in every major technology wave — from the dot-com era through Web3. Hype accelerates capital flow. Capital chasing hype funds weak ideas alongside strong ones. The sorting happens later, when the market matures and competitive pressure exposes which products have genuine foundations and which were riding the wave.
The same stress-test questions apply: Would users engage with this product if the AI novelty factor were removed from the equation? Is retention being driven by genuine utility or by the excitement of a new technology? The answers tend to predict which products survive the hype cycle and which don’t.
The Questions Worth Asking Before Building
For founders entering the AI space right now, the most valuable exercise is not technical — it’s strategic. Before committing to a product direction, the questions worth sitting with honestly:
- What do you know that the model doesn’t? Domain expertise, customer insight, industry relationships — what specialized understanding can you bring to an AI-powered product that a general-purpose model cannot replicate?
- What data do you have or can you build? Proprietary datasets that improve model outputs for specific use cases are among the most defensible assets in the current landscape
- What happens when the base model improves? If the next model release makes your product redundant, the differentiation isn’t in the product — it’s in something else that needs to be identified and built explicitly
- Who is the customer and why would they choose you specifically? Distribution and customer trust are as defensible as technology, and often more durable
The founders who answer these questions compellingly before they build are in a fundamentally different position from those who answer them after they’ve already shipped.
Where do you see genuine defensibility being built in the current AI wave — and what looks like wrapper risk to you? The gold rush is loud right now. The signal is worth finding. Let’s keep learning — together.
harish.balasubramanian 
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