AI Companies Don’t Have a MOAT – Unless They Stop Picking Sides

The uncomfortable truth about AI products: your competitive advantage has a shelf life measured in weeks, not years.

While foundational AI labs pour billions into models that take years to build, application-layer companies are discovering that moats don’t exist in the traditional sense. Your killer feature? Replicated by Friday. Your technical edge? Gone by next quarter. The diversity of players, the accessibility of foundational models, and the speed of innovation have created a market where being first, being best, or being different no longer guarantees survival.

But there’s a counterintuitive way out: stop trying to win with technology, and start building the capacity to survive it. The real moat isn’t in the AI you use – it’s in your ability to use any AI.

The Foundational Divide

There’s a foundational layer – the large models like ChatGPT, Grok, and Gemini. Several dozen models, trained differently, each possessing its own advantages. But this is fundamental, research-intensive work: engineers laboring for years, requiring massive resource investment. Each of these possesses a distinct moat – otherwise, the resource expenditure cannot be justified. This is precisely why attempts to poach engineers from OpenAI are so well-publicized: they possess unique expertise that cannot be rapidly cultivated at any price.

But at the application level, things are completely different. Far fewer resources are needed, though slightly more creativity is required to fine-tune an LLM and solve a business problem. Everyone has their own game, their own approach, their own product. The diversity of players kills any possibility of having a distinct moat in any market – text, audio, or image. Business solutions based on foundational AI emerge daily, companies appear regularly, and often they’re indistinguishable from one another.

Possible differentiators in the voice industry illustrate this evolution: initially, everyone tried to make voices sound maximally human, then speed became the question and everyone started solving the same task quickly. Now we’re in the era of emotional tags. In speech recognition, the main metric – word/error rate – has notably improved with the emergence of LLMs capable of understanding contextual word appropriateness.

In brief, the absence of a moat is explained by the lack of depth in any aspect of an application-level product’s existence: it’s shallow both in the AI component and in its business application. Just as the moat of a foundational product is explained by the depth of its development.

But do application-level projects need a moat? If you’re working in a relatively large market, and you have less than 30 competitors – you can leave everything as is. Of course, competitors can be large ones like OpenAI and Anthropic – but here you need to rely on a subjective sense of market size and dynamics, whether there’s enough food for everyone or not. But if the market is relatively small and competitors are sprouting like mushrooms – then you must very clearly position your competitive advantage. It doesn’t matter if competitors quickly adopt it.

Distribution as the Real Moat

I suspect that to some degree this is a valid assertion and the real moat lies in the distribution domain, not the technology itself. What matters more is how quickly you scale your presence with clients and whether the product’s value ensures good LTV. Otherwise, you could build some B2C application for users to play with, and they might even spread it virally, but then simply stop using it when the next new app appears.

The Two Types of Advantage – And Why Only One Survives

There are two types of competitive advantages. The first one lets you win here and now with a clear edge – thanks to some unique know-how or killer feature that competitors simply don’t have. The second one lets you avoid losing in the long run, because you’re building sustainability.

With AI products, real-world practice is already showing that the first type of advantage gets erased extremely quickly: competitors close the gap at a terrifying speed.

That’s why it makes sense to focus on the second type: maximum product durability. This is achieved by building a product that can work with any LLM provider and switch between them instantly – the moment the current model your business is built on starts clearly lagging behind the next best one.

Given this, the measure of independence from underlying LLM layers becomes a stronger moat than marketing or technical efforts alone. Being vendor-agnostic isn’t just a nice-to-have – it’s the only defensible position when the ground beneath you shifts monthly.

The Hidden Complexity of Multi-Model Strategy

While vendor agnosticism offers long-term protection, implementation reveals significant challenges. As Alexey Aylarov explains, “it’s not easy, since all models have their own specifics/issues.”

The Core Problem: LLMs are not interchangeable. Output varies with the same input – even within the same LLM, but far more dramatically when switching between providers. Each model reacts to prompts and instructions differently: some follow guidelines better, others worse; performance can be language-specific or goal-specific.

A Concrete Example: Consider image/video generating services like Sora or Veo. Give them identical input and you’ll get completely different results. This variance applies across all LLM applications.

The Tuning Challenge: To maintain multi-model compatibility, you must:

• Create separate prompts/instructions for each LLM that produce your desired result
• Know how each LLM differs and tune inputs accordingly
• Engage in work that’s often creative rather than routine
• Accept that this process is “relatively hard to automate in most cases”

It requires substantial tuning effort for each model. The upfront investment is significant: you must develop prompts for all LLMs before you can switch freely between them. Moreover, this preparation only covers existing models – when new LLMs emerge, the tuning process begins again.

The moat comes from having invested in the testing infrastructure, prompt engineering expertise, and operational discipline to actually maintain compatibility across multiple LLMs – and to repeat this process as the landscape evolves. This capability becomes a form of technical depth that competitors cannot easily replicate, even if they understand the strategy.

The Paradox: Your Moat Is in Not Having One

Here’s what makes vendor agnosticism so powerful: it’s the only competitive advantage that gets stronger as the market gets more chaotic.

When your competitor builds their entire product on GPT-4 and a better model drops, they’re facing an existential redesign. When you’ve built the infrastructure to switch models, you’re facing a Tuesday. The companies that survive won’t be the ones that picked the right model – they’ll be the ones that never had to pick at all.

Yes, building for multiple LLMs is expensive upfront. Yes, it requires creative engineering work that’s hard to automate. Yes, you’re essentially maintaining parallel prompt strategies for each provider. But this is exactly what creates the barrier to entry. The moat isn’t in the technology itself – it’s in the operational muscle memory of managing technological change.

Most AI companies are optimizing for winning today. The agnostic ones are optimizing for still being here tomorrow. In a market where yesterday’s breakthrough is tomorrow’s baseline, that distinction is everything.