[Updated link] www.juxt.pro/blog/xt25-fi...

Find someone who looks at you the way Skoda designers look at Mary Poppins

So for me, the question isn't whether AI can think like us. It's whether we can design it to help us think better.

It's not just about commercial success: in every regulatory framework—now and for the foreseeable future—humans are accountable. To exercise effective oversight we need context, not just conclusions.

The fascinating thing is that while the science goal dominates headlines and captures public imagination, the commercial AI successes so far have come from the innovation side combining sophisticated models with human control and creativity.

Yet there's always been a parallel 'Innovation Goal' focused on extending human capabilities instead of replacing them. William Ross Ashby wrote about Intelligence Amplification, (IA, not AI), back in 1956.

Ever since the earliest days of cybernetics we've been captivated by what Ben Shneiderman calls the 'Science Goal' of building systems that can replace human judgement entirely.

And this long-imagined future where autonomous agents and robot teammates take over our roles may finally be upon us.

*nods*

Are you sure you know which are the ‘predictable and routine’ tasks which can be safely delegated? Are you practicing reliable, safe, and trustworthy #AI use?

The flexible nature of current AI interfaces means we choose our relationship with it via our prompts: an autonomous assistant, a reasoning partner, and everything in between.

Yet AI has also scaffolded our own understanding of new frameworks with examples and visualisations, and facilitated debate about competing designs before we commit.

As leader of JUXT’s AI Chapter, I’ve observed for myself how easy it is for us fall into ‘vibe coding’, accepting AI suggestions uncritically and gradually losing our situational awareness.

The sweet spot (except for predictable, routine tasks) combines high automation with high human control. He calls these systems "reliable, safe and trustworthy”.

Ben Shneiderman, author of the book 'Human-Centered AI', argues that the choice between human control and computer automation is a false dichotomy. They are actually orthogonal dimensions creating four distinct regions.

The difference in outcomes was striking. The second approach, where AI built on investors’ own ideas, led to better portfolio diversification, fewer but more strategic trades, and significantly higher satisfaction: 67% versus 43%.

In the first approach, AI generated recommendations for investors to review. In the second, investors developed their own investment thesis first then received feedback on their reasoning from the AI.