Ian Shaw (@ShawOnTech1) header
Ian Shaw (@ShawOnTech1) profile picture

CEO @ ScalingSmiths | Ex safety engineer building AI tools for safety engineering firms

Glasgow
Joined March 2021
Photo by Ian Shaw (@ShawOnTech1)Photo by Ian Shaw (@ShawOnTech1)Photo by Ian Shaw (@ShawOnTech1)Photo by Ian Shaw (@ShawOnTech1)Photo by Ian Shaw (@ShawOnTech1)Photo by Ian Shaw (@ShawOnTech1)Photo by Ian Shaw (@ShawOnTech1)Photo by Ian Shaw (@ShawOnTech1)Photo by Ian Shaw (@ShawOnTech1)Photo by Ian Shaw (@ShawOnTech1)
The most useful AI system I've built this year has no gen AI in it (and 2 key constraints)
1
33
This is the the quote I've been citing a lot recently.
you can outsource your thinking but you cannot outsource your understanding
850
4,347
273
46,577
2,779,043
The most expensive words in a tool build: "let's add AI" Expensive 2x Once in trust - output you can't guarantee between runs, where you needed it fixed. Once in cash - hardware and hosting, if the data is commercially sensitive or client-confidential. The habit that works:
1
1
23
Before anyone says those words, ask which parts of the job follow rules you could write down. Code the parts you can. Then look at what's left.
1
10
- the repeatable work stays cheap and checkable - the LLM lands only where the work is fuzzy - the AI spend follows benefit, not because it's in Anyone can add AI to a tool. The judgment is knowing which parts to keep boring.
4
Why I build the AI parts of a tool last Internal AI tools keep disappointing for the same reason: They start from the model, not the job
1
1
14
- AI shoved in before the problem is looked at - demo the model before the job is scoped - feed it everything - hope the answers hold up Here's the order I build in instead: 1. Start with the parts that follow clear rules - plain code, no AI.
1
7
2. List what's left - the fuzzy work code can't do. 3. Price what an LLM adds there, against what it costs. 4. Add AI only where it wins that comparison. Start with the job. The model earns its place at the end - or it doesn't get in.
3
I'd sort your data into four tiers before any AI touched it (3&4 for data giving competitive edge) The four tiers:
1
1
20
3. Built on Microsoft - custom on Azure, or plug-and-play (and limited) Copilot. Limited increase in risk - assuming your firm stores/processes data with Microsoft 4. Never leaves the building - local models only. Dearer, and limited to open weights models
1
15
Most firms argue about tools Sort first - the tool argument mostly settles itself
3
AI is the wrong tool for 95% of work What people expect an AI tool to be: - AI used into every step - a black box that spits out the golden answer What the useful version usually is:
1
1
15
- the repeatable work done by set rules - same inputs, same outputs, every run - the LLM kept for the fuzzy parts
7
5 checks before AI output goes into a report: - every figure or standard cited, traced to the source - inputs are right & assumptions stated - passes the smell test - you can follow & defend the reasoning - a named person has signed it off YOU'RE still accountable
17
Build the search first and you can test whether AI actually adds anything, instead of guessing. Would a serious model on top help? Absolutely. The search works either way.
1
6
The best system for a company isn't the best system money could build. A huge firm with the GPUs already racked has different trade-offs. For this firm they were data, confidentiality, IP, budget Build the part that's valuable either way Then decide about AI
4