AI model providers and the long tail..
How are the AI model providers approaching this?
What the Long Tail Means in AI
In Chris Anderson’s original framing, the long tail describes how platforms make more aggregate revenue from thousands of niche products than from a few blockbusters. In AI, the equivalent is: the cumulative value of millions of specialised use cases across every industry, function, and workflow vastly exceeds the value of a handful of general-purpose applications. The providers who figured this out earliest are building accordingly.
Vertical Specialisation — Owning Niche by Niche
There is growing expectation of a number of smaller, highly specialised players that lead in specific vertical domains — and the major providers are racing to either acquire them or become the platform they build on. The strategy is to let the ecosystem build the long tail while the provider captures the infrastructure revenue underneath it.
Well the SaaS market was similar, with investors looking for businesses that took over a specific sector vertical and landed 20% market capture.
Non-AI companies that will be impacted by AI far outnumber their AI-focused counterparts — this collective impact is what constitutes the long tail of AI, divided into companies building independent models, leveraging existing models, building on open source, or integrating third-party tools.
Market analyst Benedict Evans consistently cites the lack of moat that the model providers have. But this ignores that they are investing like they are building infrastructure. Cloud providers are hard to compete with due to their size and scale for example. Many investors compare AI to the launch of the personal computer or semi conductors. Being early in this guarantees returns regardless of other player coming along later to chip into your market share.
API-First Distribution — Selling to Builders, Not Just Buyers
The most powerful long tail mechanism is the developer API. By making models available via API at declining token costs, providers are effectively distributing into every vertical simultaneously through third-party builders — without needing to understand each use case themselves.
Software companies have long used API services for distribution. Inability to consider this as a product construct also limits your returns.
Incumbents are pushing native assistants into every surface so the default experience is “good enough” without leaving the platform — AI in every Google Workspace document, a helper on every Salesforce screen. This embeds the provider invisibly into thousands of workflows at once.
The Agent Runtime as the Long Tail Monetisation Layer
40% of enterprise interactions are expected to be handled by autonomous agents by the end of 2026 — agents that research, negotiate, and buy on behalf of humans. But I struggle with these numbers as we are in the 5th month of the year now and given most interactions to “negotiate and buy” are deterministic how will an inconsistent predictive technology handle this? Each of those agent interactions is a billable token event for the underlying model provider. Well then you’re still paying aren’t you?
Teams that want bundled infrastructure can benchmark their internal operating demands against Anthropic Managed Agents at eight cents per session hour plus tokens — a per-use pricing model that scales directly with the breadth of use cases deployed, not just the number of enterprise seats.
Consumption based pricing may yet be tested as a long tail strategy. Given how much subsidy is flowing into customer acquisition right now. If prices go up significantly the customer churn rate will be high. The business model as it stands isn’t sustainable.
The Marketplace / App Store Model
Incumbents are pushing native assistants into every surface, with tighter access, more friction, and more integration hurdles — making dependence on another company’s distribution a real strategic risk for startups. This is the classic platform playbook: create the app store, take a cut of everything sold through it, and make it progressively harder to go around you.
OpenAI’s GPT Store, Anthropic’s Claude integrations, and Google’s Workspace Marketplace all follow this logic — each third-party integration extends the provider’s reach into a niche they would never have addressed directly.
Usage-Based Pricing Compounds Long Tail Revenue
AI providers using modern usage-based pricing had 40% higher gross margins and significantly lower churn than those sticking to old models. Usage-based pricing is the financial engine of the long tail — providers collect more as usage deepens across more workflows, without needing to win new customers.
The model here is the infrastructure providers. Amazon long since invented usage based pricing for infrastructure. But the context is different here and DeepSeek proved you can build an LLM for a lot less..
Forward-Deployed Engineers — Seeding the Long Tail Enterprise by Enterprise
Both OpenAI and Anthropic are launching enterprise joint ventures where an engagement might begin with engineers sitting with customer and IT staff to build tools that fit into the workflows that staff already use. This is the Palantir playbook applied to AI — get inside a client’s workflows, build something embedded and sticky, then expand use case by use case. Each deployment seeds the long tail within that organisation. It’s also an expensive model. I will break this down in another post.
The Consulting Gap This Creates
Companies implementing AI solutions are still figuring out what their use cases might look like — Accenture generated $3.6 billion in AI bookings in one quarter, and BCG projected 40% of its 2026 revenue would come from AI integration projects. The long tail of use cases is where the consulting opportunity lives — because the model providers are building infrastructure, not solving specific workflow problems for specific industries.
Recent deals with consulting firms and PE backing wjth OpenAI and Anthropic bare this out.
You cannot out-compete massive tech incumbents on generalised tasks. Hard to compete here.
But what does this mean in practise?
The model providers are building the rails. They need the long tail to be populated with successful deployments to justify their infrastructure spend. That means there is a structural incentive for them to surface, recommend, and partner with consultants who specialise in specific verticals or workflow categories.
The firms that win consulting work in this environment will be the ones that own a specific problem category well enough that an AI tool — or an AI provider’s sales team — will recommend them by name. If you’re wondering what the next generation of Big4 consulting looks like this might just be the start of it.
Data centers and the big bets
In the US 50 states have a current ban on building new data centers, 4 of them permanent ones. In other countries there are local protests to slow down build of what is very power and water hungry infrastructure. A long tail strategy is predicated on view that infrastructure will follow the insane growth of the software driven technologies which demand it. People often point to the first dot com boom and bust. Then too there was a demand for fiber and telco infrastructure which didn’t keep up with what was promised. But fiber doesn’t depreciate as fast as chips do. Invidia H100’s were impossible to get hold of, until they started to age..
I still think LLMs are fundamentally a step on the evolutionary curve. An important one, and that AI is here to stay but certainly not in its current form. The long tail may be won not by the model providers of 2026 but whoever solves and delivers on the more fundamental efficiency driven challenges and value that come after them.