Insights
Notes on AI spend, engineering cost and where the money actually goes.
Buy tokens, rent GPUs, or own the rack?
Open-weight models are now good enough to make self-hosting a financial question rather than a capability one. I modelled four options, an office rack, colocation, rented GPUs and per-token APIs, to find where each one actually wins. The answer turns on utilisation, and it isn't the one the internet keeps giving.
Will Hackett Turn a $3m AI bill into $1.9m
Most teams overpay for AI twice without noticing: they run everything through the default flagship model, and they buy the tokens on a metered plan they can't see into. An intelligent proxy fixes both. Here is the maths, a calculator you can drag, and where Flowstate fits.
Will Hackett Observations on AI agent token consumption
A new paper from Stanford, Michigan, DeepMind and All Hands is the first open empirical study of how AI agents actually spend tokens at scale. The findings line up closely with what we are seeing at Flowstate, from a different angle.
Will Hackett What is Workforce Engineering, and how do you achieve flow state?
Workforce Engineering is the practice of deliberately designing, measuring and optimising how an organisation deploys its labour, human and AI, to produce outcomes. Here's the framework, the six practices, and how to get started.
Will Hackett Amortising a hallucination
We are currently adjusting our EBITDA to account for a sophisticated parrot that read the internet. Gartner says $2.52 trillion in global AI spend this year — yet only 14% of CFOs report clear ROI. Sequoia's 6:1 services-to-software ratio means the problem is about to get six times worse.
Will Hackett The best engineering teams can turn on a dime
Why fluid planning beats rigid quarters, and how the best CTOs build cultures that adapt without chaos.
Will Hackett Agentic coding is changing the engineering workforce—just not how you think
AI coding tools have crossed a critical adoption threshold. But the real shift isn't individual productivity—it's how engineering organisations allocate capacity across priorities.
Will Hackett Moore's Law for AI is officially dead
Exploring historic AI model prices, and Google's new Gemini 3 Flash at $0.50/$3.00 per million tokens—a 67% increase over 2.5 Flash.
Will Hackett Big teams, small team energy
It's IEEE Spectrum time again, so it's time to talk about why adding management layers early is the real problem behind $2 trillion in annual software failures. From Canada's Phoenix payroll system to the UK Post Office's Horizon fiasco.
Will Hackett How do you explain the £20M engineering spend?
So you're spending 20 million on engineering. What are you getting for it? Software engineering organisations struggle to answer basic questions about costs, and PE scrutiny is changing the game. It's more important now than ever to treat engineering like the investment it is.
Will Hackett A vision for the future of workforce planning
Engineering represents both an organisation's largest expense and most critical delivery driver, yet planning tools remain almost exclusively in the hands of finance.
Oliver Beach The hidden costs of static workforce planning
Modern organizations operate at a pace that defies annual planning cycles. Yet most workforce planning still relies on fixed organizational charts and yearly model refreshes.
Oliver Beach Taking the friction away from workforce data analysis
Most leaders understand that engineering represents their largest expense. Yet when asked straightforward questions about hiring delays, executives are forced into investigative work.
Oliver Beach