Why AI in 2026 Makes the Same Waste Visible as Meetings
AI is not a toy anymore
AI – or more precisely: LLMs – has become established. Everyone uses it. Some use it well, some less well. But that’s not what this is about. I’m concerned with the economic side of using AI.
The OpenAIs of this world have managed to keep token prices low since 2022 (roughly in the range of $2–$10 per 1 million tokens, depending on the model). As a result, the costs for many use cases were practically negligible.
I believe that will change in 2026. Time to look at the implications.
We will see a mix of pay-per-token and volume contracts. And in that mix, costs will become relevant – so relevant that they will force decisions. That’s good, because the discourse shifts: away from “AI can do everything” toward “Is this use of AI worth it for us?”. Only then does a feedback loop emerge.
As soon as AI costs money, usage patterns become visible – and assessable.
The return of a familiar kind of waste
The nice thing is: we already know this situation. I’ll use a different example to make the point clear: meetings.
Meetings have low per-instance costs. The frequency is high. And often there is no feedback on impact. That is exactly why meetings are rarely questioned consistently.
Why is that? Meetings convey activity. Coordination feels like work – and therefore like value. But soberly speaking, a meeting is not automatically valuable.
With AI it’s similar. Each individual prompt is written quickly. And the responses from LLMs are designed in such a way that they often create a feeling of success and progress. We have the impression that we are being productive.
Waste rarely comes from big wrong decisions, but from frequently repeated, barely examined micro-costs.
AI without a work-feedback loop
In every activity – large or small – there is a work-feedback loop. Without feedback, we may have done work, but we don’t know whether it was meaningful.
When using AI, we see typical cases in which exactly this feedback part is missing:
- Summarizing without a decision
- Analyzing without consequences
- Generating without use
Important: Output is not the same as impact. That shows: AI amplifies systems – it does not correct them.
Put more precisely: tokens do not create value.
Value only arises when a decision is made or a system state changes. If that doesn’t happen, tokens are a cost item without value creation. And that happens often – precisely because AI is so easily available. We are subject to the false assumption that more information automatically leads to better results.
Value does not arise from knowledge, but from effective decisions.
Visibility is not progress
There is another level: psychology.
AI is also readily used for psychological reasons. Besides the mechanism that LLMs often deliver agreement and a “sense of progress,” motives such as control, modernity, and the feeling of momentum come to the fore. We already know these feedback effects: status meetings, dashboards, or agile rituals – likewise often without real impact.
Too often it’s about narrative work instead of system work. Many AI applications buy reassurance, not flow.
When costs become visible
What happens when costs rise? In that moment, waste becomes measurable: How many tokens did we need for this ticket description? How many for this meeting summary? How many for this decision?
Then you quickly find: under budget pressure, the evaluation of AI use turns out differently. The flow problem – AI as an activity generator without impact – was already there before. Relevant costs only make it impossible to ignore.
Economics doesn’t discipline – it exposes.
What AI will actually demand from teams
What does that mean? Teams must clearly distinguish between work that moves systems and work that only describes systems. With AI use, the central question will become louder: does it make our decision better or faster – or just better justified?
This is not a tool question. It is a system question.
AI forces teams to focus on impact – not efficiency.
Closing thought
AI does not destroy bad systems. It makes their costs visible. If you didn’t have flow before, you now get a bill.
It won’t be AI that will change companies, but the clarity about what they are burning money on.