AI has made a particular kind of thinking cheap: competent-looking first-pass output.

A memo. A plan. A summary. A translation. A draft of code. A diagnostic explanation. A structured recommendation. Work that once required hours of junior professional time can now arrive in seconds, often fluent, coherent, and useful enough to create a dangerous impression: that intelligence itself has become abundant.

It has not.

The distinction matters because organisations are currently making two opposite mistakes. One group treats AI output as if it were settled knowledge. The other dismisses it because it is not. Both miss the point. AI has dramatically changed the economics of producing a first pass. It has not abolished the work of establishing whether that first pass is correct, appropriate, complete, or safe to act on.

The three precisions

The phrase “cheap thinking” needs three qualifications.

First, it is cheap at delivery, not at creation. The systems producing frontier AI remain among the most capital-intensive technologies ever built. Training, infrastructure, energy, chips, data, and specialised research talent are highly concentrated. But the capability created at that frontier becomes progressively cheaper to deliver. This produces a structural funnel: extreme concentration at the top, broad access below.

Second, it is cheap per unit, while total spending can still rise. Lower cost does not necessarily reduce demand. Cheap computation expanded what organisations considered worth computing. Cheap AI expands what organisations consider worth analysing, drafting, classifying, reviewing, and automating. A lower cost per task can produce a much larger volume of tasks.

Third, and most importantly, AI is cheap for competence, not for correctness. It generates plausible answers at a cost that has collapsed. Verification has not collapsed at the same rate. In many high-stakes domains, accountable checking remains expensive because it requires evidence, context, domain expertise, responsibility, and the ability to explain a decision after the fact.

The strategic implication

The relevant question is no longer: “Can AI produce this output?”

For most cognitive artifacts, the answer is increasingly yes. The relevant questions are:

  • What evidence supports the output?
  • What constraints, policies, or source documents were applied?
  • Who owns the decision?
  • How quickly can a human reviewer identify an error?
  • Can the conclusion be traced back to the underlying data?
  • What happens if the recommendation is wrong?

The value of AI is migrating away from raw generation and toward the operating system around generation: trusted data, workflow integration, verification, provenance, monitoring, and accountable action.

What this means for AIxtract

AIxtract should not be understood as another system that produces fluent output. Its role is to turn fragmented documents, data streams, images, logs, and operational signals into intelligence that can be inspected and acted on.

That means every useful output needs a path backward:

Decision → recommendation → extracted signal → source evidence

A recommendation without supporting evidence is an assertion. A recommendation with traceable evidence becomes an operational asset.

A practical rule

Use AI freely to lower the cost of exploration, drafting, extraction, and synthesis. Raise the standard of evidence as the cost of being wrong rises.

Cheap thinking is an opportunity. Treating it as cheap intelligence is a risk.

Adapted from the book Cheap Thinking: What AI Makes Abundant, What It Makes Scarce, and Who Captures the Difference by Daoyuan Li, PhD.