AI Today

When AI ingests all your company's documents and makes it easy for every colleague to get answers on every facet of their job, are we empowering people - or lobotomising them?

Is the training struggle, valuable? Is there some kind of masochistic delight in completing that bungled 400-page PDF on workplace ethics?

Or is traditional training effective as that weekly meeting where no one gets anything done, except that report that no one reads?

Today's episode offers enlightening ideas about how teams tackle training now the oracle is in town.

Three companies. Three crises. One unsettling question about what happens when you let machines do the remembering.

In this episode: how a cheese shop and a 313-ship fleet solved the same problem from completely different angles.

Why the line between 'surface the answer' and 'execute the action' is the most important decision any operations team is making right now.

And why a CEO's essay about replacing middle managers with AI might be exactly right — and completely wrong — at the same time.

Sources referenced in this episode:

• Rebel Cheese shipping recovery case
• Hapag-Lloyd / Amazon Bedrock feedback pipeline (AWS blog)
• Melbourne Airport incident response agents
• IBM Db2 Genius Hub
• ServiceNow Context Engine
• Jack Dorsey / Block: 'From Hierarchy to Intelligence'
• The Knowledge Base Is Not the Moat — The Loop Is
• Codifying Tacit Knowledge

Your organisation generates a staggering mountain of data every single day. Slack threads ping, Jira tickets multiply, and emails fly back and forth at the speed of light. You have terabytes of perfectly preserved, pristine text.

But ask your team why a crucial vendor decision was made three months ago, and what happens?

The Panic Search.

Three people spend an hour digging through an analogue graveyard of Zoom transcripts and buried PDFs, only to realise that the actual "why" vanished the moment the meeting ended.

We call this Corporate Amnesia.

Your company has a flawless photographic memory for documents, but zero memory for context. It’s a giant, highly profitable goldfish.

In this week’s mind-bending episode of the AI Today podcast, we’re explaining why simply slapping an AI chatbot on top of your chaotic Google Drive won’t save you.

Standard AI search (RAG) hits a "relational ceiling."

it can read the polite fiction of a company wiki, but it can’t understand the messy, human reality of how your business actually operates.

If you feed a broken filing cabinet to an AI, you don’t get a supercomputer.

You just get a faster idiot.

To actually evolve, business leaders need to stop buying glorified search tools and start building a Company Brain—a true digital nervous system.

Here is the three-layered architectural map to building an intelligence platform that will make your executive team obsessed:

Layer 1: Factual Memory (The Compiled Truth + The Receipts). We explain how to cure the "staleness" problem. A true Brain maintains a single, hyper-accurate "Compiled Truth" of a situation (like a live Wikipedia page), whilst keeping an immutable, chronological timeline of the exact receipts (the messy Slack messages and emails) that led there.

Layer 2: Interaction Memory (The Context Graph). This is where the magic happens. We explore how to map the invisible human web of your company using an Ontology. The Brain learns to understand the vibe, the dependencies, and the unwritten rules—connecting a broken pipe on the 4th floor to a VIP client's discount code.

Layer 3: Action Memory (The AI Actually Doing Things). Using the Model Context Protocol (MCP), we look at what happens when your AI agents stop just summarising text and start safely executing workflows, bounded by strict permissions.

The Ultimate Plot Twist? Building a Company Brain isn’t just an IT upgrade; it is a cultural revolution. When an organisation possesses a flawless, objective memory of how and why every decision was made, the dark arts of corporate politics and blame-shifting simply evaporate.

Tune in to discover how to stop micromanaging the past, cure your company's amnesia, and finally free your human workforce to do what they do best: imagine the future.

(Warning: After listening, you will have an overwhelming urge to burn down your current folder structures and build an ontological context graph. Proceed with excitement.)

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 [email protected] 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?