The new reality: Big players are playing offense
It’s often stated in startup circles that when a platform gets big enough, it tends to absorb, out-feature or out-price every smaller player in its niche. The field of AI is now entering that phase.
For example:
OpenAI has created separate programs and infrastructure aimed directly at startups (such as their “Converge” program) to invest, enable, or embed them.
OpenAI is strategically moving to lock down investors and resources—reportedly asking capital backers to steer clear of certain competing AI firms.
The cumulative effect is that many smaller AI startups—especially those building “me-too” tools, wrappers around existing models, or narrow automation pieces—are finding their runway vanishing, because the major players now own the platform layer, the distribution, and in many cases, the developer tools and APIs.
On Reddit, this reflects a broader sentiment:
“With one blow, OpenAI has killed all wrappers … they opened the market to the general public.”
“I think there’s still plenty of opportunities, just don’t go for the same low hanging fruit that the whales see?”
So yes: it’s legitimate to say that many early‐stage AI startups are facing existential pressures from the brawnier platform incumbents.
Why your startup could be at risk
If you’re building an AI startup today, here are the main threats you need to be crystal clear about:
Platform displacement: If you build on or wrap around someone else’s model/API (for example OpenAI’s models), the platform owner can bring equivalent features, integrate them deeply, and undercut your value.
Distribution & access advantage: Big players already have massive user bases, ecosystems, and tools. They can push new features into existing products (so users don’t need your separate tool).
Compute & data economics: Building and scaling higher-performance AI models remains extremely expensive. The big players have economies of scale. For a new startup to compete on model performance alone is very high risk.
Investor capital concentration: Investors are now picking fewer winners (and backing large rounds) rather than spreading many bets. So raising and keeping momentum is harder.
Feature blur & shrinking differentiation: If your startup is “AI for X automation” but someone can plug in the same model and build “AI for X automation plus Y plus Z” because they have larger resources, you may get squeezed.
If you ignore these risks, you may find that you need to pivot fast—or worse, shut down.
How to avoid being vaporised: strategic approaches
Okay—so you’re not just going to give up. Good. Here are concrete strategic moves you should lean into.
1. Go vertical-deep vs. horizontal-broad
Instead of building a generic “AI tool for everyone” (which will make you a direct target for platform competition), pick a specific vertical where you can:
- Understand domain-specific data better than anyone.
- Build workflows, integrations, trust and context that general models cannot easily replicate.
Example: an AI in legal-fintech recruiting (which might resonate with your interest in recruitment) where you know the market deeply.
When you’re deeply embedded in a niche, a platform can try to replicate but may lack your domain insights, ecosystem relationships, and customer trust.
2. Own a layer that the “platform” finds hard to dominate
Ask: What elements of your value chain are hard for a large AI platform company to commandeer easily? Consider:
- Proprietary data/labels + domain expertise
- Workflow & enterprise integrations + legacy systems
- Customer trust, regulatory/licensing barriers (especially in legal, life sciences, highly regulated sectors)
- Customization, on-prem or edge deployment, privacy / compliance demanding use-cases
If you focus on those, you are less vulnerable to someone simply “adding you as a feature”.
3. Build business model and revenue-stay first, model-performance second
Many AI startups obsess over “better model” but undervalue “repeatable revenue” and “embedded workflow”.
If you can show clear ROI for customers (cost savings, increased revenue, risk reduction) with manageable compute and client acquisition costs, you’ll survive the churn of feature competition.
Platforms might give you “free model access”, but they won’t give you your customer relationships, domain trust, or vertical credibility.
4. Be nimble, experiment fast and be ready to pivot
If you know your niche well, you can spot adjacent opportunities where the platform hasn’t focused yet. Lean into that. The big players are strong—but slower—and often shift priorities after the “market” already moves.
That gives you a window. Exploit it. But you must be prepared to change direction if you see the platform creeping into your space.
5. Form alliances / leverage ecosystems
If you’re a startup, you might not beat the platform alone—but you can partner with enterprises, incumbents, regulatory networks, domain experts.
Especially in regulated sectors (legal, life sciences) customers often require proof, auditability, compliance—things that the generic AI platform might not prioritize. That’s your opening.
6. Stay paranoid and monitor platform moves
Don’t ignore the platform’s roadmap. What features are they building? What verticals are they pushing into? What APIs are they opening?
If the platform starts offering “AI legal assistant for recruiting” as a plug-in, you need to either move fast or reposition. Feature parity is dangerous.
Why this is encouraging (not just scary)
Even though the competition is fierce and the risk is real, this environment also opens windows of opportunity; if you pick your strategy right.
- The speed of AI improvements means new possibilities will keep emerging. If you’re agile and focused, you can capture spaces before the platform fully arrives.
- Platforms often serve generic needs; they rarely dominate every niche, especially the ones with high specialization, heavy regulation, or vertical friction. That creates durable space for startups.
- Investors and customers are still hungry for “AI plus domain” stories. The big generic models won’t automatically win every use-case.
- As you build in a niche, you can become the trusted provider, not the “also-ran wrapper”. That builds moats: technical, operational, relationship-based.
The final word
If you’re launching an AI startup today, you must assume the big platforms (OpenAI or others) will try to enter your space—and aim to build in a way that they cannot easily replicate you overnight.
That means:
- Domain specificity > model bells & whistles
- Workflow integration, customer relationships, trust & compliance > just “better automation”
- Business model and revenue traction > just “cool tech”
- Agility, focus, and willingness to pivot > just “raise big and scale fast”
You won’t be building in a calm market. You’ll be in the eye of the storm. But if you choose your niche, build defensibly, and keep your eyes open for where the platforms aren’t, you can succeed—even when the giants loom large.