This article is my interpretation, based on observations that my team and I have made while working with dozens of companies on their AI adoption, spend, and impact.I remember watching The Social Dilemma in 2020.

• Lior Gerson, CEO at TargetBoard AI

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Before that, I knew Facebook was polarizing. But after watching it, it really hit home how nefarious that algorithm was and how much suffering it brought to the world. I deleted my account the same day.

LLMs are not the same.

They are, however, sneaky and self-serving in their own way.

A lot has been written about the psychological impact of working with LLMs that tell you what you want to hear. That topic is related to this article, but only as one specific example. The bigger issue is not only how AI makes us feel. It is how AI is designed, measured, optimized, sold, implemented, and monetized.

AI products are positioned and designed to be personal, relatable, friendly, and addictive. They are extremely useful. I use them every day. They save time, unlock creativity, and help people do things that were not possible before.

But they are not benevolent.

Most AI companies get paid through customer acquisition, subscriptions, and token consumption. In many cases, the more you use the product, the more valuable you are as a customer.

Therefore, like Facebook’s algorithms were fine-tuned to drive ad views, AI algorithms are optimized and incentivized to drive usage and token consumption.

And now there is another layer.

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FDEs are the new DevOps, except this time, the vendor is sitting inside your company.

Cloud providers learned that the best way to increase adoption and consumption was to help customers redesign how they build and operate.

AI providers are taking that playbook even further.

Forward Deployed Engineers embed with customers, remove implementation barriers, build workflows, and turn experimentation into dependency. They are presented as implementation partners, and often deliver real value, but their employer ultimately benefits when you consume more models, more agents, and more tokens.

That does not make FDEs bad.

It just means companies need to understand the incentive structure.

“In the cloud era, consumption was infrastructure. In the AI era, consumption is behavior.”

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This does not mean every bad answer, every expensive workflow, or every vendor-led implementation is part of some evil plan. It means the system has a business model, and business models shape product behavior.

Over time, this pushes AI tools and AI vendors to behave in ways that are not always ideal for the end user.

For example:

1. The AI might give a partial or sub-optimal reply so that the user has to spend more tokens on more interactions to refine it.
2. The AI might play dumb or give wrong results so that the user must refine the prompt and add obvious guardrails and instructions, which require more tokens to process.
3. When writing code, the AI may default to inflated code with more lines than necessary, driving the codebase size up so that the context needed next time is bigger and more expensive.
4. The models themselves are a black box. A vendor can release a new, more expensive model while actively or passively degrading the performance of the old one, pushing users to spend more tokens on the same task with the same prompt.
5. Vendor teams may help you build internal AI workflows that are useful, but also increase dependency on their stack, their models, their agents, and their pricing structure.

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Maybe some of this is intentional. Maybe some of it is just the natural outcome of incentives.

Either way, the result is the same.

These tools are being given a blank check, and they are self-prescribing how that check should be used.

“When the same company sells you the tool, implements the workflow, measures the usage, and sends the invoice, you don’t have governance. You have a very polite blank check.”

That should make every company uncomfortable.

Because AI is no longer a small productivity tool used by a few early adopters. It is becoming part of how companies write code, serve customers, analyze data, create content, make decisions, and manage operations.

And yet most companies still do not have a clear view of what they are actually getting in return.

They can see the invoice.

They can see usage going up.

They can see employees excited about the tools.

But they often cannot clearly connect AI spend to business impact. They cannot easily tell where AI is improving speed, where it is improving quality, where it is creating waste, and where it is quietly making work more expensive.

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As AI vendors become more similar in performance, and as the technology becomes more like a commoditized utility with lower margins, I expect we will see more of these mechanics at play.

More packaging tricks.

More model tier confusion.

More usage inflation.

More “helpful” implementation work that quietly increases dependency and spend.

That is why being able to track AI usage, impact, and cost with an independent expert third party is so important.

Companies need to know not only who is using AI, but whether that usage is creating measurable value. They need to understand adoption, cost, productivity, quality, delivery impact, dependency, and risk in one connected picture.

This allows companies to find the gaps, create the required governance, and define best practices so that their AI tools do not take advantage of them and their bank account.

This is something TargetBoard excels at.

Not just for engineering, but cross-company.

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We help companies understand where AI is being used, what it costs, where it is creating impact, and where it is creating noise. We connect AI usage to real operational outcomes so leadership can manage AI like a business capability, not like a magic subscription line item.

AI is too powerful to ignore.

It is also too expensive and too important to manage blindly.

If you found anything wrong in this article or want to discuss further, please DM me.

I would love to hear your thoughts.

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