The AI Tool Tax

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I want to start with a number that should be uncomfortable for most growth teams reading this.

The average B2B growth team in 2026 is spending three to five times more on AI tools than it was two years ago. The output has not scaled with the spend. The team is faster at some specific things, slower at others, and structurally more dependent on a stack that costs more every month while delivering less every quarter than the optimistic 2024 case studies suggested it would.

This is not a story about AI not working. AI works. The base models, Claude, ChatGPT, and Gemini, are genuinely transforming what a single operator can produce in an hour. Anyone who has actually used them seriously knows this is real.

The story I want to tell is about everything sitting on top of those base models. The 30 to 60% of the typical AI stack that is, on close inspection, paying for the base model twice. Charging you 49, 59, or 99 dollars a month for a template library and a slightly nicer interface on top of a 20-dollar API you could access directly.

This is the AI tool tax. And the brands quietly winning at AI are the ones who figured out how to stop paying for it.

To understand why the tax exists, you have to look at what the AI tool market actually looks like underneath the marketing.

Almost every dedicated AI growth tool, Jasper, Copy.ai, Writesonic, Rytr, Anyword, Notion AI, and the long tail of writing wrappers run on the same underlying models. They call OpenAI's API. They call Anthropic's API. They call Google's API. They are not building their own intelligence. They are routing your prompts to the same base models you can access directly for 20 dollars a month, wrapping the output in templates and brand voice settings, and charging you a premium for the wrapping.

In 2022, this premium was defensible. The base models were not yet good enough for non-technical users to prompt effectively. The wrappers added real value by lowering the skill ceiling. A small business owner who did not know how to write a good prompt could pick a Jasper template, fill in three fields, and get usable output. That was worth paying for.

In 2026, the picture is different. Claude Pro and ChatGPT Plus, at 20 dollars a month each, now handle 95 % of what the wrapper tools were built to do. The required skills have dropped. The base models have memory features that approximate the brand voice persistence that the wrappers used to charge a premium for. The interface improvements that justified the markup have been matched or exceeded by the base products themselves.

The wrappers have not adjusted their pricing to reflect this. Jasper still charges 49 dollars for the Creator plan and 69 dollars for Pro. Copy.ai charges 49 dollars a month. Writesonic charges 16 to 33 dollars. Notion AI adds 10 dollars on top of your existing Notion subscription. Grammarly Premium runs 12 dollars a month. Each of these is doing, with diminishing differentiation, what Claude or ChatGPT now do natively as part of a 20 dollar subscription.

Multiply this across a typical growth team. Three or four wrapper tools at 30 to 60 dollars per seat per month. Five seats. That is 600 to 1,200 dollars a month for the capability you already have inside the base model subscriptions you are already paying for.

This is the tax. And almost nobody is running the audit to see how much of it they are paying.

Here is the audit I have been using when I sit down with growth teams to look at their AI spend.

For every AI tool in the stack, ask four questions. Each one is small. Together, they reveal where the wrapper tax is sitting.

The first question is what base model does this tool run on. If the answer is GPT-4, Claude, or Gemini, you are paying twice. Once for the base model subscription you are already running. Once for the wrapper that calls the same model with different prompts. The wrapper is only worth keeping if the templates, brand voice, or workflow features genuinely save time you could not save by improving your prompting in the base model directly.

The second question is, what does this tool do that I cannot do in Claude or ChatGPT with the right prompt? This is the most uncomfortable question because the honest answer for most wrapper tools in 2026 is "nothing." The templates produce outputs you could replicate with a 200-word prompt in the base model. The brand voice features are now matched by Claude's Project Knowledge and ChatGPT's Custom Instructions. The workflows are usually three steps that you could execute manually in the base model in under five minutes.

The third question is who specifically uses this tool, and how often. Most growth teams discover, when they run this honestly, that two or three people on the team account for almost all the usage of any given wrapper tool, and the rest of the seats are paid for but unused. Sometimes one person uses the tool a lot. Sometimes, nobody actually uses it at all, and the seat exists because someone added it during onboarding and nobody removed it.

The fourth question is what would replace this tool if it disappeared tomorrow. The honest answer for most wrapper tools is "Claude or ChatGPT, with the right prompt." This is not a sign that the wrapper was useless. It is a sign that the wrapper was useful when prompting was hard, and is no longer useful now that prompting is easier.

When you run this audit on a typical 8 to 12 tool AI stack, you usually find 4 to 7 tools that fail at least three of the four questions. These are the cuts. Together, they often add up to 30 to 50 % of the total AI tool spend, redirectable immediately.

Some specific patterns are showing up repeatedly across the teams I have seen audited honestly in the last few months. These are the cuts most often producing the largest savings.

The writing wrappers. Jasper, Copy.ai, Writesonic, Rytr, Anyword, and the smaller tools in this category have lost most of their meaningful differentiation against Claude and ChatGPT in 2026. The teams that have audited this category and cancelled the wrapper, then invested an afternoon in building better prompt libraries inside Claude or ChatGPT, almost always report better output quality at a fraction of the cost. The exception is marketing teams with 10 or more writers needing strict brand voice enforcement at scale. For those teams, Jasper or Writer can still justify the premium. For everyone else, the wrapper is paying for itself with money you could keep.

The grammar tools. Grammarly Premium made sense in 2022. In 2026, pasting a paragraph into Claude or ChatGPT and asking for grammar and tone refinement produces equal or better results, with the additional ability to ask follow-up questions about why specific edits were suggested. The Grammarly browser extension is still useful for catching typos in real time inside Gmail or Google Docs. The premium tier is not.

The note-taking AI add-ons. Notion AI, Evernote AI, and the various productivity tool AI features almost universally fall into the wrapper category. They charge 8 to 12 dollars per seat per month for capabilities you already have inside your base model subscription. The trade-off they offer is convenience; the AI lives inside the tool you are already in. For most users, the convenience does not justify the cost once you build the habit of having Claude or ChatGPT open in a tab.

The meeting summarizer category. There are now 20-plus tools that record meetings and produce AI summaries. Most teams need one. They are paying for two or three because different team members added different ones. Cut to one, pick the one that integrates best with your existing calendar and document system, and cancel the rest.

The "AI-powered" feature creep in existing tools. Many tools you were already paying for have added AI features and bumped their pricing. The audit question to ask is whether the AI features in tool X are doing anything you would not be doing in Claude or ChatGPT anyway. Usually, the answer is no. Downgrade where possible.

This is not an argument against AI tool spending. It is an argument against specific kinds of AI tool spending. There is a small list of categories where dedicated tools genuinely add value above and beyond what the base models can do.

The base models themselves, Claude Pro and ChatGPT Plus, each cost 20 dollars per seat per month. These are not wrapper taxes. They are the actual intelligence, everything else is built on. The teams I see operating well have both, because the two models have different strengths, and the overlap that feels redundant is actually two distinct tools.

Research tools with citations. Perplexity is the example I see most often justifying its place in the stack. The integration of web search with structured output and source citations does something Claude and ChatGPT do less cleanly. For research-heavy roles, this is worth its 20 dollars a month.

Video and audio processing. Descript for podcast editing. Opus Clip for turning long-form video into short form. These are not wrappers. They are doing specific AI tasks, transcription, speaker separation, scene detection, that require infrastructure beyond what the base models do natively. If you produce video or audio content, these tools are pulling weight.

Specialised vertical AI where the use case is narrow and the domain expertise is deep. Sudowrite for fiction writing. Surfer SEO for keyword optimisation. Cursor for code. These tools are not running base models with templates. They are running domain-specific models or wrapping the base models with workflow logic that genuinely changes the output for the specific task. If the task is core to what your team does, these are worth keeping. If the task is occasional, the base models are good enough.

Image generation is when you need a specific visual quality. ChatGPT's image generation handles most casual needs. Midjourney handles the use cases where you need higher quality and have time for the Discord workflow. Canva's AI features cover the templated visual production most marketing teams need. The wrappers in this space, the various "AI image generators" charging 20 to 40 dollars a month, are usually adding nothing; the included image features in your base model subscriptions are already doing.

This is roughly the honest stack for a typical growth team. Two base models. One research tool. One to two media-specific tools, depending on what you produce. One or two narrow vertical tools if your work justifies them. Total spend is somewhere between 60 and 150 dollars per seat per month. Replacing 200 to 400 dollars per seat per month of wrapper tax that most teams are currently paying.

There is a deeper point underneath the cost-cutting that matters more than the dollar savings.

The teams that have moved from heavy wrapper usage to direct base model usage are not just saving money. They are building a different kind of capability. They are getting better at prompting. They are developing internal libraries of prompts that work for their specific use cases. They are building intuition for what the base models can and cannot do, which is the single most important skill for anyone using AI seriously over the next decade.

The wrapper tools were a training wheel for people who did not yet know how to talk to AI directly. They served a real purpose in 2022 and 2023. In 2026, continuing to rely on them is a form of skill atrophy. The team that defaults to Jasper for every blog post is not developing the prompting skill that the team going directly to Claude is. Over 18 months, that skill gap compounds. The Jasper team produces predictable, templated content. The Claude team produces output that gets sharper, more specific, more genuinely useful, because the operator is learning the model.

This is the unspoken cost of the wrapper tax. It is not just the budget. It is a capability. Every month you pay a wrapper to abstract away the base model is a month you are not getting better at using the base model directly.

The brands that will look obvious as winners in 2028 are the ones that started this transition in 2026. The ones that ran the audit, cut the tax, and used the money and the time to go deep on Claude and ChatGPT.

The transition is uncomfortable for the first two weeks. Prompts feel harder. Output feels less polished. The brand voice setting in Jasper that you were used to does not exist in Claude. You have to write your own prompt that establishes it.

By month two, the team that pushed through has a prompt library that is far more flexible, far more specific, and far more powerful than anything the wrapper tools could have produced. By month six, the team has compounded a skill the wrapper-dependent teams will spend the next two years catching up to.

If you want to act on this edition concretely, here is what to do this week.

Pull the list of every AI tool your team currently pays for. Include the seat count and monthly cost for each. Total it.

For each tool, run the four questions. Base model. Differentiation. Actual usage. Replacement.

Identify the 30 to 50 % of the spend that is wrapper tax. Cancel those subscriptions, or downgrade them, depending on the tool's policy.

Reallocate part of the savings to either expanding Claude Pro and ChatGPT Plus seats to the people on your team who are currently underserved, or to specialised tools in categories where the dedicated product actually pulls weight.

Invest the rest of the savings in prompt training. Two or three afternoons for the team to build shared prompt libraries inside Claude or ChatGPT, oriented around the specific use cases your team handles weekly. This single investment produces more compounding value than any wrapper tool will in the next 18 months.

The AI tools market is still in a phase where most of the tools are extracting value rather than creating it. The wrapper tax is real. The teams that recognise it and stop paying it are quietly building capability and reclaiming budget at the same time.

The teams that keep paying it are funding the next round of someone else's marketing.

Pick the side you want to be on.

See you at the next edition, Arindam