Category: Rich Pictures

We’re now familiar with gen AI but what is generative learning?

Generative learning theory suggests that people learn and remember more when they make relationships between what’s new to them and what they already know.

It’s a constructivist theory and says learning is about the work of actively constructing knowledge.

Gen AI tries to shortcut this.

I tried to make a thing yesterday. A tripod mount for an attachment. I drew a picture on paper, designed it in OpenScad, created a printable file in Slic3r and printed it on my 3d printer.

It’s a godawful design. I got it wrong twice. It only works becuse I didn’t realise that the tripod mount was helping the design stay rigid.

Any engineer that’s got shop experience would know a hundred different ways to make something better. But I don’t. I’ve given up on constructing that knowledge of making physical things. I’m at a competence level not far behind someone in high school.

We usually improve with age. Unless we stop trying.

Now imagine doing that with your mind. Stop trying to actively construct knowledge. Stop learning and remembering information. Stop trying to connect what’s new with what you already know.

In business, don’t bother talking to people. There’s no need to understand how the operation actually works. Want a strategy? Pick from a selection of ready made ones, all plausible and beautifully formatted.

If we stop and think for a minute, assuming we still can, what do we think that’s going to do to our ability to think?

Can we create the businesses and services and politics of the future if we let our ability to gain knowledge stagnate?

I’m not against using tools. They augment us. What we’ve got to do is remember that gaining knowledge requires active work – which is often hard work. You need knowledge so you can use tools better.

My prediction – the people who succeed will the ones who successfully couple generative learning with generative ai.

Working hard in a world where you have computers seems like a failure of imagination to me.

I dropped out of my first PhD to join a startup.

While I was doing the PhD, however, I had plenty of time to get coffee with colleagues and talk about research.

And this was for one simple reason – my computer busy working for me.

I inherited a codebase in c of around 4,000 lines.

I cut it down to 100 lines in python.

And then I built a pipeline – the computer started with a model, did an initial pass to reduce compute time, and then worked through complex calculations on a computing cluster my colleague built. When the calculations were done, and the results were formatted and pulled together.

Yes, you could work hard at each of those steps and it would take days or weeks – or you could use a machine and get it done in three hours.

And this isn’t new stuff – we’ve had the tools for around 40 years now.

I’ve used the same approach again and again, and we do the same thing in our latest business.

Raw data is entered in spreadsheets. Computers do a series of tasks and clean and usable outputs pop out the other end.

Most systems on the market give you more work to do.

Our systems do the work for you.

How do you build innovation teams in a world of AI?

Pretty much the same way you built teams before AI.

There are four roles that are crucial but most firms only get three right.

You need a developer – someone who can make what you need.

You need an SME – someone who knows what do do.

And you need an architect – someone who knows how something should be made.

One person can deliver all three roles if they have the experience.

But what’s usually missing from the conversation is the voice of the user.

Maybe it’s because users introduce real world complexity and nuance – they bring context.

It’s messy and untidy and hard to solve.

But building for context is what results in success.

It’s a tough time for older job seekers.

We once interviewed an experienced, gray-haired candidate for a sales director role.

It was a no – not because of age but because their responses didn’t match the level of career maturity the role needed.

It got me thinking about how careers evolve, and what employers expect at different statges.

Should you use AI less rather than more? Extracts from a philosophical and a legal opinion.

Our goal as thinking beings should be to cultivate the faculty of reason – according to Daly (2026) – working on habits to develop excellence in five intellectual virtues.

These are:

1. Knowledge of one’s field
2. Intuition based on knowledge
3. Wisdom in how one’s field relates to life and society
4. Decision-making skill in how to achieve a desirable end
5. Practical ability to make something using reasoning

The use of generative AI threatens the development of all these virtues.

The problem is that we experience sustained cognitive declines by outsourcing these habits to generative AI.

We literally get more stupid.

If that wasn’t enough the case for using Gen AI – that it makes us faster and more effective is undermined by Yuvraj (2025)’s verification-value paradox hypothesis.

In a nutshell, this hypothesis argues that the time saved by using Gen AI is offset by the increased time needed to manually verify the outputs from Gen AI.

This is because truth matters. Knowing that a collection of words belong together statistically is not sufficient justification to use them uncritically.

Verify. Then use.

Our cognitive skills matter. We should be very sceptical when it comes to replacing or diminishing them.

REFERENCES

Daly, T., 2026. A ‘low-tech’ Academic Virtue Ethics in the Age of Generative AI. J Acad Ethics 24, 13. https://doi.org/10.1007/s10805-025-09683-3

Yuvaraj, J., 2025. The Verification-Value Paradox: A Normative Critique of Gen AI in Legal Practice. https://doi.org/10.2139/ssrn.5621550

What explains the $100m to $1b salaries being paid to top AI researchers?

The way business value is created has changed forever – but our mental models haven’t caught up yet.

What is value anyway? In the 18th century, it was all about land. In the 19th, it became about labour. In the 20th, the narrative shifted to resources.

And now? It’s knowledge.

I was reading Grant (1996) and a quote stopped me in my tracks.

‘All learning takes place inside individual human heads; an organization learns in only two ways: (a) by the learning of its members, or (b) by ingesting new members who have knowledge the organization didn’t previously have’ (Simon, 1991: 125).

Some people think knowedge is safe in organisational rules and procedures. But we’ve all seen what happens when a key person leaves, and someone else picks up that rule book and finds it’s useless.

Will AI rescue us? That’s still up for debate – maybe if we can fix hallucinations and guarantee quality output. It’s still not clear if this is the answer.

But if these two are mirages – if knowledge can only be held and exercised by individuals, the foundations of shareholder value shift under our feet.

Value becomes about people, specifically ones that can create knowledge and apply knowledge. Finding ones that can do both is like hunting unicorns.

And that perhaps explains why some companies are willing to pay so much for them.

REFERENCES

Grant, R.M., 1996. Toward a Knowledge-Based Theory of the Firm. Strategic Management Journal 17, 109–122.

Simon, H. A. (1991). ‘Bounded rationality and organizational learning’, Organization Science, 2, pp. 125-134.

A recent HBR article by Bansal and Birkinshaw (2025) suggests we should use systems thinking more, especially when it comes to complex, wicked problems.

They argue that we often reach first for two approaches that seem to promise quick results.

Breakthrough thinking cuts through the mess, dealing with a knotty problem by simply cutting the knot.

Design thinking focuses on users – how they interact with products and services and how that can be made better.

But some situations seem intractable. They’re so complex and wicked that something else is needed.

Systems thinking looks at the big picture, at the interconnections between elements, and what might happen if we intervene – including knock on effects elsewhere.

We try and engage with the complexity of a situation but some systems thinking approaches can feel quite muted, like they almost lack ambition.

They seek to incrementally improve situations, not radically transform them.

That’s partly because radical approaches cause pain. And demolishing existing institutions without a coherent plan for a replacement tends to cause more problems down the line. And it’s partly because you’re working with people and have to deal with politics and culture along with the situation itself.

There’s no clear cut answer, and there’s a place for all these approaches.

The trick is knowing when you to cut, when to fix, and when to improve – and choosing the approach that helps most.

REFERENCES

Bansal, T., Birkinshaw, J., 2025. Why You Need Systems Thinking Now. Harvard Business Review 103, 124–133.

Can a career manager make it as an entrepreneur?

Yes, if they get one thing right: openness.

I’ve always worked in startups – in that space where we try and identify opportunities, build systems and try to create value for clients.

In that time I’ve worked with lots of managers in large organisations. We’ve even hired some of them.

Most found the startup pace hard.

It’s the lack of support that gets you – having to do everything from creating a complex spreadsheet to fixing the printer yourself.

But that’s just the foundation. The boring but necessary stuff.

The real difference is whether they’re open or closed.

I must confess – I’m naturally quite closed.

As an engineer I’m heads down, focused on work, building things.

Fortunately, I work with partners that are the opposite of me and I’ve learned over time that being open is an essential skill to develop.

Open people are heads up. They connect with others, build relationships and look out for opportunities. They’re optimistic and politically astute. They’re likeable.

Drucker said that the purpose of business is to create and keep a customer.

Being open is how you do that.

In consulting, we add services only when it’s clear that clients need work done and every other option is worse.

Sometimes it feels like mucking out stables.

The work never ends, and just when you’re done it’s time to start again.

Take working with utility data, for example.

When I started out suppliers would email you billing files every month. Then portals came along and turned a one minute task into an hour of watching spinning loading screens.

And without the data, you’ve got nothing.

So we hired people, trained them as analysts and got them collecting and checking these bills.

We thought many times about outsourcing the task – but it takes a certain kind of person to care enough about getting this right – the detail, precision and technical complexity puts off most of the population.

And getting data and making sure it’s clean is the first step to doing everything else we do.

So we kept doing it in house. Because clients needed us to. And we had learned how to do it well.

This is the challenge clients have with outsourcing work- especially work that has to be done but isn’t strategically core to your business – like energy and carbon data management.

Jim Collins said it best – “If you can’t put your best people on it, then find someone else and get them to put their best people on it”.

Two years ago I started building AI tools to help me with tasks. Now, I’m leaning back into doing some of them myself.

Things that are too easy to do have no value. One of the few things I remember from the one economics module I took was that the price you ask will drop to your cost of production in a competitive market.

Work that AI does for you for pennies is only worth pennies to your buyer. You may imagine that your prospects will value the time and effort that went into crafting your prompts but really they’re thinking two other things.

1. Can I do this myself for free?
2. Can someone else do this for less?

The downward pressure is inexorable.

Let’s take one particular example – using AI to summarise documents.

A couple of years back I built a simple tool. It took pdfs, converted them to text, split them into segments that would fit in ChatGPT’s context window, and then automatically extracted the key points that mattered.

Not as summary – a distillation. The job was to remove the fluff and leave the facts and key strategic points.

The advantage – it was quick. The disadvantage – it did something that wasn’t really worth doing.

A document worth reading will already be structured in a way that does this for you.

An introduction or executive summary will lay out the key facts and points.

The rest of the text should only include information that is important and relevant.

The only reason not to read the whole document is if it’s badly written. The logical response is to ignore it, not use AI to summarise something that isn’t worth your time to read.

This is just one example – the lesson for me is do your own reading. And your own writing.

You’ll learn more that way.

I still use AI every day. It’s a powerful tool.

We just need to figure out what’s it does that’s really valuable.