The Industry of Lies, or What Leaders Need to Know About AI

Maria Sukhareva

Jun 21, 2026

∙ Paid

The other day I talked to a department lead who was very eager to adopt AI and introduce it to his employees. He did not have a technical background, and his entire department consists of experts in an area completely unrelated to AI.

He asked me what he should know about AI as a people’s leader, and what the most frequent reasons an AI use case fails are.

AI is the industry of lies. (image generated by chatGPT)

Since that conversation, I have been thinking about it. What should a people’s leader practically know? Obviously, not that AI is so transformative and revolutionary — they have been hearing that in dozens of presentations. In fact, AI has been somewhat forced on employees:

Accenture now tracks how often senior employees utilize artificial intelligence on a weekly basis, according to recent reports. The firm links these adoption metrics to promotion opportunities for veteran staff, to ensure they embrace the growing role of technology in the workplace.

In recent years, AI has turned into an industry of lies: benchmaxxing, hallucinated consulting reports, overpromising, underdelivering, AI experts who have never heard of a loss function — barely any other area is as filled with lies as AI.

It is not easy to comb through these braids of lies.

What are the real things one needs to know? That is what this article is about.

In this article, I will refer to LLMs as AI, because that is what is frequently understood by it. So further on, we talk exclusively about state-of-the-art LLM-based approaches.

AI is not cheap

One needs to budget for it, and, without proper controls, an AI application that is actually useful can end up costing more than a full-time employee. The other problem is that the prices are not predictable. Three months ago, one might have budgeted roughly €1,100 a month for GitHub Copilot Enterprise for a team of 30 developers. Now, that same budget might not be enough for a single heavy user: since GitHub switched to usage-based billing in June 2026, costs are calculated per token at the API rate of whichever model handled the request — and as reasoning models become the default, individual monthly bills are already being reported in the hundreds. How much one actually needs to budget is also hard to predict: there is no reliable way to calculate the cost upfront.

$500M on consumption in a month

Many AI tools — particularly coding assistants and enterprise platforms — have moved from flat subscriptions to consumption-based pricing, which means one pays per million tokens. AI APIs are affected too even though they have always been consumption-based. The cost of those tokens has been going up. Anthropic's Claude Fable 5 ships with a new tokenizer that generates roughly 30% more tokens from the same input text, meaning the same system prompt and conversation history that cost 10,000 tokens yesterday costs 13,000 today, with no change in what you're sending. OpenAI's GPT-5.5 is priced at double GPT-5.4 on every billing line.

One can pay per million tokens up to $50.

How much can one do with a million tokens? That is something impossible to predict.

The price includes reasoning tokens — the internal deliberation of the LLM when trying to solve a problem. A response that looks like 500 tokens in the output may have consumed 50,000 or more. The price of every request depends to some extent on the skill of the programmer to word tasks in a way that helps the LLM reason less and produce the solution faster. But, in general, prompting skill is not the decisive factor. What matters more is how much context is sent with every request: the whole chat history, agent descriptions, tools, MCP servers, and system prompts — all of it is billed on every single call. But the most important factor is luck. If the LLM happens to generate the right reasoning traces quickly, you save money. If it does not, it can ramble indefinitely trying to self-correct. The choice of model also matters: some reason longer than others. The banned Fable could burn $50 or more from a single prompt easily.

No one can tell you that your employees need X tokens a month. The best way to figure this out is to calculate what is economically feasible for you: the trade-off between productivity and cost. If your developers run out of quota within three days, two things will happen: they will ship much slower for the rest of the month, or they will use their private accounts. 68% of employees already use personal AI accounts for work tasks, with 57% of them entering sensitive company data and the average cost of a shadow AI data breach is $4.2 million.

All in all, the important steps here are: make the prices and quotas transparent to your team; train developers in best practices for reducing token consumption — prompt optimization, context management, and caching alone can cut costs and consider alternative options, e.g., local deployment of a MiniMax model. A local model will not entirely replace proprietary models, but you can route simpler tasks to them to keep costs down.

Benefit from the heavy competition in AI

AI has become a highly competitive space. Anthropic, OpenAI, and Google are constantly challenging each other. Chinese providers are undermining Western prices massively — releasing capable models available locally, on the cloud, or via API. OpenAI is considering drastic price cuts to avoid losing enterprise market share to Anthropic. You should keep an eye on all of this, diversify your providers, and renegotiate offers.

I have met many companies that simply use the provider their developers picked one or two years ago. By switching, you could prevent shadow AI, have uninterrupted access to models, and save thousands. For example, GLM-5.2 — an open-weight model released three days ago by Z.ai — costs $1.40/1M input tokens and $4.40/1M output tokens. It just claimed the #1 spot on Design Arena, a crowdsourced HTML design benchmark, beating Claude Fable 5 (which, at $50/1M output tokens, is currently unavailable due to a US government export control order — though give it some time). As Andriy Burkov, author of The Hundred-Page Machine Learning Book series, put it on X: he’s been running GLM-5.2 with OpenCode instead of Codex for three days and “doesn’t see any difference” and has already cancelled his Anthropic subscription.

That said, it is still not that easy: force your developers to use a model too weak for their tasks and you will create massive technical debt. Replacing a strong proprietary model with a cheap open-weight alternative risks a far greater drop in product quality than the cost saving justifies. Consult your internal experts, introduce rigorous evaluation pipelines, and rely on validated research and reliable benchmarks. AI Realist offers a service to help companies optimise their AI stack and consumption but whether you choose us or not, go in with a clear understanding of the risks: underperforming models, data privacy constraints, the overhead of maintaining your own infrastructure, throughput, and more.

Further in the article: the most impactful approach to AI adoption, dealing with the user acceptance problem, why AI has become an industry of lies, and why return on investment is not anywhere in sight.

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