On today's show we create a business to show you the huge improvements in gravitating beyond prompt engineering to the new community of practice we call context engineering.

You'll be rocked by the results when you join us for a deep dive into this remarkable rabbit hole of agent communication.

Don't forget to leave a review for the show on Apple Podcasts. We appreciate it!

Two of the most important voices in AI spoke out this week. Andrej Karpathy, one of the algorithm's greatest philosophers, was in conversation with Dwarkesh Patel talking praisingly and cautiously about the Cambrian explosion in cognition but an inconsistency and lack that foresees a ton of work ahead to get us where AI deserves to be. His 'decade' of agents, tells us all we need to know about today's limitations.

Meanwhile, Google's CEO Sundar Pichai talks effusively about AI being the great equaliser right now. There's a commercial necessity in promoting what's available to the AI practitioner in 2025.

These conflicting commentaries aren't making life easy for business leaders.

So we had a debate - on today's episode of AI Today!

If only that was the real name. After all this time begging frontier labs to build an LLM that learns from its mistakes and applies its discoveries at inference time...

Welcome to AI Today!

The Dragon Hatchling is a remarkable research paper that reboots modern AI as a model that approximates how our brains work.

Today's show is a fascinating discussion and I implore you to both enjoy it and then chat about it and ask your questions on NotebookLM.

It's the one thing every business leader needs to know.

If I put AI to work in my organisation, will it screw everything up?

While we should all be in experiment mode right now - until someone figures out how to make the probabilistic, deterministic - OpenAI researchers have been putting AI to work on real tasks.

The results are spectacular. Spectacularly good, and spectacularly bad.

But just like Tim Henman, you have to give it a chance. And maybe great things will follow - for AI.

On today's show we look wider, at many ways we've tested AI in organisations and across functions and disciplines - and how it's fared.

And then we zoom in on GDPval, which sounds like someone your gran knows who reads The Economist but is actually that OpenAI research paper that explores LLMs in the context of the organisation. We hear the pros and cons and whether now's the time to execute agents, or execute our dreams that AI is ready to replace us all.

Enjoy the show.

A fantastic research paper published in this month's Nature Computational Science suggests a solution may be in our midst for the incredible inefficiency in generative AI.

Large Language Models' (LLMs) transformer architecture requires the next token (generally part of a word) be predicted based on all the output tokens before it.

Power demands for this process are huge. Shuffling data between memory and processors isn't an easy pipeline, and when you need it to work quickly, those energy demands quickly stack up.

And in an AI arms race, where everyone wants bigger and better models, requiring increasingly powerful compute is required to stretch their limits, the dependence on energy to power, and cool, those processing units grows exponentially.

But what if there was a different, and better, way, to make AI work? That's the driving force behind work of Nathan Leroux and his team proposing a totally different paradigm: analog in-memory computing.

And that's exactly what we're discussing today.

Zip yourself in that flame retardant suit: things are about to get hot in here...

Ping me at dave@wordandmouth.com to get on the show or talk about AI in your world.

30 years in journalism has sharpened my mind.

I've spent years in AI.

And months researching China and the US as they fight silently for AI supremacy.

$500bn in The Stargate Project does not come close to the value China has created integrating AI into every aspect of its society and economy. But the truth is, they won before the US even woke up to AI's potential. China's superapps - forget homescreens, because you only need one icon to run your life in the Republic - were simply laying the foundation for where we are, today.

But let's have a debate, nonetheless.

East v West: which is best?

I've been working on a business intelligence platform leveraging AI and 30 years in journalism and content strategy. It's the toughest professional project of my career. And I have no idea if I will win. But just like life, Zeitgeist is all about the journey, not the destination. What I am learning is more important than any long form feature we might generate. Knowledge graphs, ontologies, taxonomies, and patience. Hope you will stick around on this crazy adventure.

I'm Dave Thackeray - leadership coach, content strategist, and endlessly curious Berlin-based berk.

Email me at dave@wordandmouth.com to test Zeitgeist for your business.