A few months ago, I was trying to understand why one high-protein formulation puffed well during low-moisture extrusion while another did not.

I gave ChatGPT the formulation details, expansion results, rheological measurements and observations from the extrusion trials. Then I started asking what might explain the difference.

One line of reasoning concerned critical strain — broadly, how much deformation a material could tolerate before its structure began to break down. A plausible hypothesis was that greater deformation tolerance might help a material expand.

There was a problem. One of the materials with the highest critical strain had performed poorly in expansion.

So I pushed back: if that explanation was right, why did the experiment show the opposite?

We reconsidered the interpretation, brought in other observations and explored alternative explanations. Eventually, the important lesson was not that one rheological measurement could predict puffing. It was that several properties and processing events had to be considered together.

That exchange made me realise that I was no longer simply using AI to retrieve information.

I was using it as a sparring partner.

Not just returning the ball

I used to play table tennis, and a good sparring partner does not simply return the ball comfortably.

One shot goes to your forehand, another to your backhand. Sometimes the ball goes to a corner you would rather not cover. That is how you discover the weaker parts of your game.

That increasingly describes how I work with AI.

I can put forward an explanation. ChatGPT offers another possibility. I question it. It challenges an assumption. I add another experimental observation. Sometimes the original explanation survives; sometimes it does not.

And sometimes the correct conclusion is simply that we do not yet have enough evidence to decide.

The value is not that AI gives me the answer.

It is that the exchange helps me reach better questions.

There is an understandable concern that easy access to AI-generated answers may make us think less. My experience has often been the opposite. Used this way, AI makes me interrogate my own thinking more.

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Getting up to speed faster

This has become particularly useful in my advisory work.

I sometimes need to evaluate research and innovation projects spanning disciplines outside my own technical specialisation. Before I can assess the scientific basis of a proposal, what may be genuinely novel, or what questions should be put to the researchers, I first need to understand enough of the subject.

Previously, entering an unfamiliar technical area could be slow. I would search for background material, begin reading, encounter terminology or mechanisms I did not understand, search again and gradually assemble a mental map of the field.

AI has shortened that first stage considerably.

I can begin with elementary questions. If an explanation introduces another unfamiliar concept, I can ask immediately. I can compare mechanisms, ask why one interpretation is preferred over another, and keep probing until I know what I should be reading and questioning.

It does not evaluate the project for me. I still need to examine the original material and evidence and make the judgement myself.

But it gets me to the point where I can ask useful questions much faster.

For me, AI has reduced the cost of being curious.

When collaboration becomes trust

AI also saves time in more obvious ways. I use it to organise long documents, compare versions, restructure technical material and work through scientific manuscripts.

But efficiency creates another issue: trust.

Recently, I worked through many versions of a long document with ChatGPT. This was not a case of handing over the task and accepting whatever came back. I questioned arguments, requested specific changes and repeatedly refined the direction.

After each round, ChatGPT summarised what it had changed.

Gradually, I realised that I had begun trusting those summaries. If it told me that a particular point had been corrected, I did not always reopen the entire document and verify every amendment before proceeding to the next issue.

The collaboration had become useful enough that I trusted the account of what had been done.

Also Read: Before you use predictive AI, decide what decision it should change

I became conscious of the risk when I later reread part of a scientific manuscript. The writing was coherent, but an important part of my scientific intention was not sufficiently prominent.

Nothing was obviously wrong. But it was not quite telling the scientific story I intended.

That is a boundary I now pay much more attention to.

AI can help me construct an argument. It can challenge it, revise it and tell me what it has changed.

But if my name is attached to the work, I still have to ask: Is this accurate? Is this what I mean? Am I prepared to stand behind it?

AI cannot make that decision for me.

Covering more of the table

I therefore find myself asking a different question from when I first started using AI.

Not simply: What work can AI do for me?

But: What kind of thinking does working with AI allow me to do better?

Sometimes it helps me enter an unfamiliar field more quickly. Sometimes it helps me manage an overwhelming amount of information. And sometimes it sends a question back across the table that exposes an assumption I had not realised I was making.

That is why I keep returning to the table-tennis analogy.

A good sparring partner does not play the match for you. It does not remove the need for technique, practice or judgement.

It makes you cover more of the table.

The best use I have found for AI is not outsourcing my judgement. It is giving my judgement a better sparring partner.

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