Does it even matter?

Well it does, if you are building products and services which are either consuming or evolving IP in this space. Models vary in quality, consistency and intended purpose. But they are already in many ways commodified.

There is no moat for the model providers and as DeepSeek proved can be built without the extreme funding that went into OpenAI and Anthropic. Token costs will drop, choice and flexibility is key, ripping out integrated services is expensive. Strategies and roadmaps take time and investment to deliver on. Understanding where to position your vendor offerings or which services are the best long term play in terms of procurement and consumption may add or subtract % points from your operating costs.

The industry has converged on a common thesis: Anthropic, OpenAI, Google, and Microsoft all agree that the harness is the product — meaning the infrastructure around the model (agents, memory, orchestration, compute) is becoming as important as the model itself. Pricing and packaging, and its future given the imminent IPO’s is a whole other topic.

Bundling

Consumer & Productivity Ecosystems

Rather than charging $20/month solely for text-based chat, consumer-facing premium subscriptions now bundle specialized, vertical tools directly into the base fee.

This makes sense since we have seen the most growth on the direct to consumer side. In order to tackle enterprise spend however where the real margins have traditionally been, bundling is also combined with a more traditional software distribution mechanism.

Examples:

• Google’s Lifestyle Bundling: Google combines its cloud storage, advanced Gemini models, and specialized AI verticals under a single subscription. For instance, the Google One AI Premium Plan bundles “Google Health Premium” (an AI personal fitness and sleep coach) alongside workspace integrations.

• OpenAI’s Feature Compounding: OpenAI continually expands ChatGPT Plus into an all-in-one platform by natively bundling multi-modal capabilities (voice, image generation via DALL-E, advanced coding environments) and a centralized Assistants API / GPT Store.

2. Multi-Model Enterprise Platforms

Platform giants are packaging models as “operating software,” moving away from absolute exclusivity to offer multi-model bundles where different AIs handle distinct tasks.

• Microsoft’s Multi-Model Copilot: To avoid sole dependence on OpenAI, Microsoft bundles Anthropic’s Claude models into Microsoft 365 Copilot alongside GPT-4. GPT for creative generation and Claude for complex spreadsheet math or visually rich PowerPoint slides. [

• Cloud Hyperscaler Aggregation: Platforms like Google Vertex AI and Amazon Bedrock bundle proprietary models with third-party open-weight models (like Meta’s Llama or Mistral). Enterprises pay one cloud bill for model hosting, vector databases, safety guardrails, and compliance tracking.

3. The “Agentic Stack” and Cross-Platform Standardization

Instead of simple inquiry-response prompts, providers bundle infrastructure that allows AI agents to perform end-to-end organizational workflows.

• The Shared Skills Directory: Major providers like OpenAI, Anthropic, and Microsoft have aligned on standardized formats to bundle context folders and executable “skills”. These are packages of metadata and methodologies that can be used interchangeably across enterprise ecosystems to complete multi-step tasks natively.

• Full-Stack Orchestration: API providers like NVIDIA via NIM microservices pack inference code, security guardrails, and connection tools into single software containers.

4. Industry-Specific (Vertical) Bundling

Model providers increasingly partner with telecom, healthcare, and financial institutions to offer tailored bundles which cater for the sector.

• Telco Action Models: Providers like Anthropic collaborate with global networks (e.g., SK Telecom) to offer industry-specific language models. These bundle traditional cloud infrastructure with specialized data embeddings to automate customer service, network routing, and technical support out-of-the-box.

Unbundling

There are broadly 3 layers to unbundling.

1. The Provider: Cloud provider, NeoClouds.. Who is sitting on infrastructure services to spin up that GPU or compute consumption (AWS, Google Cloud, Azure, Cerebras, your own Mac). They facilitate connectivity to a range of different models.

2. The Model Providers: Anthropic, OpenAI, Alibaba Cloud, Gemini (GPT-5.1, Claude Opus, Qwen3).

3. The Tool: Where you type. (IDE, CLI, or a background agent), so Cursor, Windsurf, Copilot, (anyone who forked VS Code at that critical market hype moment).

How do the major players break this down?

OpenAI — Unbundled Products are distinct (ChatGPT, Codex/Operator, API/SDK) with separate use cases and pricing. Explicitly avoided adding a runtime fee, keeping token-based pricing modular. The Microsoft restructure further decouples distribution.

OpenAI is looking for volume of engagement and consumption. Splitting out product offerings is generally associated with distribution based mechanisms.

Microsoft — Bundled The clearest bundler. Copilot is embedded across the entire M365 suite (Word, Excel, Teams, Outlook, PowerPoint) and positioned as inseparable from the platform. Azure AI Foundry unifies development tools under one roof. Some unbundled signals exist — Foundry Agent Service uses consumption-based billing per tool — but the strategic direction is tight integration.

Well this makes sense given most of Microsofts customer base is large corporates and they have to date always taken this approach to their services. Thus making it difficult for other cloud providers to compete with their offerings.

Google — Mixed NotebookLM and Workspace AI are bundled into existing subscriptions. Gemini is woven across Search, Workspace, and enterprise. However, Vertex AI Agent Engine bills each capability (sessions, memory, code execution, observability) as separate line items — a deliberately unbundled billing model sitting inside a bundled product ecosystem.

Google has only a 14% share of the cloud provider market. They need distribution reach more than Amazon or Microsoft to acquire business who simply add services and revenue to ongoing customer relationships.

Anthropic — Mixed Claude.ai is a standalone interface. Managed Agents bundle compute, state, and orchestration into a single session-hour fee (a bundled pricing unit). The recent announcement on Blackstone/Goldman-backed enterprise JV is a distinct, services-led offering to enable integration with big enterprises an approach very much out of the Palantir playbook. Anthropics offerings really are a core of a modular stack with one grouped pricing tier.

AWS — Bundled Bedrock bundles multi-model access through a single API, and AgentCore adds a runtime primitives layer on top. The co-creation of a Stateful Runtime Environment with OpenAI, delivered through Bedrock, deepens the bundle. The explicit goal is making AWS the default deployment layer — a classic infrastructure bundling play. Bedrock enables access to multiple model providers but this keeps you in the AWS ecosystem. Hard to impossible to change cloud provider anyway once established.

IBM have fallen behind on AI investment. They don’t have the investment of the hyperscalers, or model providers, but are still a major player in terms of big corporate customer base. Providing mainframe systems to big banks, telcos etc is still profitable business. Their offerings are focused on integration.

IBM watsonx - Orchestrate for multi-agent orchestration, IBM Confluent for real-time data, IBM Concert for intelligent operations, and IBM Sovereign Core for operational independence — positioning IBM as the enterprise governance and compliance layer on top of frontier models.

Tooling concerns

A lot of organisations want consulting on tooling. At the mid manager level there is a plethora of options, you and your team are busy learning what AI tools to exploit and how to use them. But for you as a technology leader, this just risks increasing your application inventory. Whilst for you as a more specialist vendor or SaaS offering, integration with big strategic players may be essential to enable your distribution. Offerings that already work well with existing enterprise services may have the edge on sales distribution.

Strategic patterns and risks

There are some obvious considerations if you are a large org investing in your businesses consumption of AI services.

1. Bundling creates lock-in risk for clients — when customers use multiple integrated services, switching costs increase substantially. Enterprises need independent advice on which bundles suit them and how to avoid overexposure to a single vendor.

2. Gartner predicts over 60% of enterprises will access AI capabilities through bundled subscription services rather than individual point solutions by 2026 — creating a significant advisory gap around vendor selection and contract negotiation. Well this makes sense if they are to survive as service offerings.

Technical leadership should direct and navigate vendor selection, avoid lock-in, and extract ROI from bundles they’re already paying for but underusing.

Most medium to large organisations have a plethora of application inventory but only exploit a proportion of it per individual department or business unit. Given that both the model providers and the infrastructure providers who have funded them will look to claw back those CAPEX costs, it is imperative that organisations start to look forward. Both to limit operating overheads and exploit new models of business that AI offers to enable.

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.

What Quantum Superposition applied to sound waves looks like.. from the Quantum exhibition at the science gallery earlier this year.

• An Early Universe by Alistair McClymont 2025

Photo by Jukan Tateisi on Unsplash

I have worked with a lot of early-stage start-ups and scale-ups. There are numerous things they get wrong, and most people get the same things wrong.

But what I have noticed through my recent experiences is that, despite the fact that many large orgs have a good customer base and may be comfortably profitable, they don’t understand the fundamental equations that have achieved that.

Early-stage businesses talk of product-market fit. But it’s too obvious when you have it. They struggle because they don’t yet understand the demands of defining a customer, which is always a narrow construct to begin with, and how that relates to a likely volume of sales and, therefore, returns based on solid data.

Mature businesses also struggle here. There are some simple truths you need to understand;

You need around a 20% market penetration in any one sector or your business will die. 20% of any market or segment is significant. But for mature businesses, the challenge here can be further growth. What do you do when you have already grabbed 20%?

Well in either case, you need to identify other customer profiles which may benefit from the solution. What aspects of the product will you need to adapt, or add in order to serve that customer? What about new markets? How many of those customers are in that market? What factors might impact that segmentation? Where can you get or find a reliable source of data to model this?

The equation is simple:

Specific customer profile within X addressable market = enough market share?

If it doesn’t, then you need to consider your customer profiles and segments.

Pitfalls

Too many cooks.

In a business model that relies on investment rounds, you need to have a handle on forecasting. The earlier the stage the more critical this is.

A lot of Execs will say Oh, we need to coordinate across multiple departments. No, no, you don’t. For late-stage businesses that have reached corporate levels of people, systems, processes, etc., you will have armies involved in generalised management consulting. In Startups and Scale-ups, this has to be owned and led by the Execs.

As an Exec, you should have a model for this. The teams may work with the finer details, but you should work this out. In a business model that relies on investment rounds, it is absolutely essential that you have a good handle on this.

In an investor-backed model, you should have a financial model which outlines the financing rounds and the shareholding dilutions, and what it will take to get you to exit. This is an Excel spreadsheet exercise and a bit of modelling effort to execute.

If you don’t know what you will do with the product, how many sales do you need to generate then how much money will you need to get to the next financing round? How do you know what product you are building? How do you know how the solution will serve that? How do you know what skills and resources you will need to build it? Due diligence will pick this up, or should. Don’t wait for the DD before you consider it.

Haven’t really considered the data

A typical pitfall is also not to really scrutinise data from a reputable source. This is essential. The majority of business leaders find out only too late that their market isn’t as big as they thought. Good leadership tackles this head-on and tries to answer the problem.

A couple of example’s of this in action is below:

Not understanding how small your market actually is and how few customers you actually have to sell to is one of many reasons for business failure.

Product development is not just about solving the user’s problem. Its about finding business value that will not only monetise but will offer sustainable, scalable returns.

Product as a discipline is often poorly defined. You cannot be a product leader and not have some sense of these basic commercial functions. Yet they are so often missing from the conversation.

I often feel in business and more specifically product-led software business that no one articulates the basics to you, and you’re often expected to work this out via osmosis. It’s a specific discipline, and one that, if you haven’t got the experience of building within this particular context, it takes time and practice to get good at it.

Clarity on the fundamentals and owning that is what will give you the best chance of success.