The Journey

The most repeated number in enterprise AI turns out to be a paraphrase of unnamed opinion surveys, quoted in a magazine profile of a vendor.

Nineteen headline AI failure statistics, traced to source. Seven are not measurements of anything that happened.

The 15% drawdown halt was wired into two live code paths and could never fire. So I checked all 26 risk controls. Seven were dead. A control you cannot observe firing is not a control, it is a belief.

Three incidents, one headline, three very different severities. What the primary reports actually say, and what to do about it.

A skill file is your judgement, written down and executable. Who owns the repo decides whether that's an asset or an extraction.

Most AI projects fail on the brief, not the model: six people nod at one sentence and mean four different projects. Soft Systems Methodology, built at Lancaster from 1969 for exactly that, in plain English: rich pictures, root definitions, CATWOE, and how to run it on the next thing on your roadmap.

Swapping the two LLMs behind an autonomous trading bot from Opus 4.7 to 4.8, live, without it missing a beat. The trick was replaying 78 real prompts first.

More than 80% of AI projects fail, and RAND's leading root cause is not the technology: nobody agreed what problem was being solved. Soft Systems Methodology, built at Lancaster from 1969 for exactly that failure, gives you a way to surface the disagreement in an afternoon instead of a year.

I judge AI models by how often they make me swear. Opus 5 turned swearing into punctuation, and the reason is more interesting than I first thought.

My auth check said healthy for three weeks on a dead credential. The pattern behind it costs more than the bug did.

An audit of 116 instruction files found one stale copy telling twenty repos they were a different project, two files no session has ever read, and 453 lines loaded twice. Here is what to delete from your own CLAUDE.md, and the method that beats judgement.

The most rigorous idealist alive gives the non-duality audience far less than they want: a universal consciousness that is instinctive, has no plan, and is not looking back at you. What he actually claims, why he says it cannot be proven, and the one part that survives whoever turns out to be right.

Seventeen finished pieces of work were invisible to my own tracking system, and the cause wasn't effort or memory. A Berkeley talk gave me the name for what fixed it: an ontology. Here's the version that fits a one-person company.

Last week I showed the demand numbers for AI consultancy are dire. This week a veteran operator explained the part I missed: the demand isn't dead, it's mute. Nobody buys AI. They buy outcomes, told concretely enough to defend to a board.

The YC president gave a talk describing, almost item for item, the AI system I already run. The validation lasted about ten minutes. Then I noticed the word neither of us was saying.

I got phished from my own verified domain, using an API key I'd leaked myself: what actually leaked, how fast it happened, and what I changed.

A friend said it should be a business. I spent forty years around operations and resilience, so I assessed it honestly, decided it was not, and built it anyway, for free. Here is the reasoning, and why the map of your estate should be a file no company owns.

A $1,000-an-hour AI consultancy playbook is doing the rounds. The demand data, and a friend who sells everything except this, disagree.

Same spec, same model, two methods: a seven-part goal prompt against a full agent framework. The lean prompt tied on correctness and won on time, cost, and clutter. Here are the numbers.

Eleven days of unattended trading. Five losing trades, one winner covered them, +$132 net. What the system did right, what drifted, what I fixed on return.