X : You know that project you spent a day mapping for us and said we would fail in these specific contracts.
Me : Gosh, that was seven years ago.
X : Well it failed in exactly the contracts you pointed to.
Me : You didn't change the contracts?
X : No.
Me : Why?
X : We spent five months writing those contracts, you spent a day mapping.
Me : You thought that time spent was somehow associated with correctness? The more words the better?
X : I'm not going to end up an X am I?
Me : No-one will ever know.
Me : Look on the bright side, a lesson learned. Next time map your project, apply contracts to the map and then chalenge whether you're building it the right way.
X : An expensive lesson.
Me : I won't ask. Water under the bridge. You're not getting those millions back.
Me : Out of curiosity, what shoud I have charged you for that day?
X : A few million would have been cheap if we had listened.
Me : Hmmm, that 's encouraging. Something for the future. Cheap you say? Oh, I won't pour salt into the wound. Lesson learned.
X : Is this the worse you've seen?
Me : Even if we're talking a few hundred million then it's not even close. About $1.2 billion tops the leaderboard for doomed projects that were obviously doomed before they started.
X : What happened?
Me : They showed me the project and I gave them an alternative for $25M with the same result.
X : What was the alternative?
Me : Me, sitting on a beach drinking mojitos and giving them a call in five years to say "We failed"
X : What happened?
Me : They asked me to leave.
X : I guessed that. What happened with the project?
Me : Well, I got a call six years later from one of the execs who said they wish they had paid me $25m.
X : Vindicates your work?
Me : I'd have preferred $25m.
X : Do you still do that?
Me : What? Help people analyse their projects?
X : Yes
Me : No, it's a waste of my time. I prefer teaching people to map then at least they might challenge themselves. A lot less headache.
The general lesson ... don't expect people to simply agree with what you map for them. It's a far better idea to teach people to map because only then will they challenge their own ideas.
This is also why case examples can be counterproductive. The benefit or not from mapping depends upon context and willingness of people to challenge their own ideas. It's better to spread slowly by word of mouth, by use and with one practitioner teaching another.
X : How much will mapping save us?
Me : No idea.
X : How much benefit will it create?
Me : No idea.
X : What is the ROI?
Me : No idea.
X : So what's the point?
Me : Some find it useful.
• • •
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We will be entering a phase in which the US high-tech industry (including the military complex) is highly dependent upon China, whilst China is not dependent upon the US.
For those who doubt how clear the intentions were ... go read Made in China, 2025.
China's government made its intentions evident in 2015. The US sabre rattling of sanctions reinforced that purpose whilst the US essentially continued with a misguided "market knows best" policy.
A couple of prompts with Claude 3 creates a Wardley Map for economic sovereignty in the defence space.
Not bad at all -
On par with political, military and defence folk I've spoken to. I'm also finding I can have a reasonable discussion about mapping with Claude 3.onlinewardleymaps.com/#clone:XvHskIi…
It's not perfect but it's not bad. There's more I want to interrogate Claude over ... i.e. the link to secure sourcing, the positioning of some components etc. But it's almost good enough that I can start a discussion over strategy and investment.
Anyway, upshot is that Claude 3, from my perspective, has left ChatGPT4 in the dust. Of course, I'll use Claude, ChatGPT, and Gemini to cross-compare for now but if I do start building anything more complex then the obvious path is AWS Bedrock which gives me Mistral etc.
dX: What is the single most significant problem facing AI today? Safety? Lack of skills? Inertia?
Me: Overinflated expectations by the business.
dX: You don't think AI will become widespread?
Me: Of course, it will; industrialised components are rapidly becoming cost of doing business. Don't confuse that with expectations. There will be an awful lot of disappointed businesses hoping it would create some advantage.
dX: I don't understand.
Me: Imagine you're just finishing off your plan for how AI will revolutionise your business. Six months for budget approval, one year to build team, 18 months to deliver something ... that's 3 years from now. Any advantage you thought of is long gone.
For those who don't know, I'm working increasingly on and with Glamorous Toolkit - ... I have become fascinated by our willingness to blame humans for problems that are created by our toolsets ...gtoolkit.com
... I saw this last night at Cloud Camp. Apparently, the issues with understanding, explainability and observability in AI are down to humans' inability to deal with complex environments... no, they're not. The problem is with the tools and the type of tools we are creating ...
... we've imported concepts from a physical world where tools are constrained by physics - hence a hammer is a hammer, a drill is a drill - into a world without such constraints. Rather than building contextual tools, we've built constrained tools.
Faulty products, harm to users, executives profiteering, fighting compensation ... what is truly bizarre about the Fujitsu Horizon case is that the public seems to think that this is an isolated example rather than the normal way that traditional corporations act.
Just take a look into any industry, pick something like retail with BNPL (by now pay later) to EWA (earned wages access) to use of slave labour. It's story after story of despicable behaviour, of exploitation of both workers and consumers in pursuit of profit.
Or pick something like energy, where misinformation and self-interest abound from carbon capture to hydrogen - both technologies which are not primarily for the benefit of consumers or the environment but instead prolong a fossil fuel industry and all the harm it causes.