AI Cost Watch

Scruuge’s TLDR

Every message re-sends the entire conversation — that's how LLMs fake memory — so your 10th message pays for the 9 before it, and every retry multiplies it. The fix isn't a bigger context window; it's keeping your desk clean: start fresh when the job changes, carry the artifact not the argument, and store answers you can look up.

Scruuge’s TLDR

Open-weights ≠ automatically cheaper. Judge a model by the finished bill, not the per-token rate: a verbose 'budget' model on a big footprint can beat a pricier frontier one. Pick open source for control, fine-tuning, and resilience — not a blanket cost win. Don't generalize DeepSeek's one serving win into a law.

Scruuge’s TLDR

You will not optimize your way to a flat AI bill — cheaper tokens get spent on more (Jevons). Spend isn't the enemy; unmeasured spend is. Cut the waste (floor), steer the rest toward where it compounds into revenue (ceiling), and measure per-outcome, not per-token.

Scruuge’s TLDR

The race stopped being 'who's smartest' and became intelligence per dollar, per second, per watt. A chip-starved lab just lapped the field on that scoreboard and gave away the blueprints — so the 'best model' you picked last quarter is probably not the one you should pay for now.

Scruuge’s TLDR

Cutting your bill is the floor — necessary, and soon table stakes for every competitor. The ceiling is where you point the frontier: the questions no one else thought to ask. We'll cut the floor honestly, then tell you when cost was never your real constraint.

Scruuge’s TLDR

Best model to plan, good-enough model to execute. You're not buying a smarter AI for the whole job — you're buying judgment for the 10% that needs it and paying wholesale for the 90% that doesn't. Route by task, never mid-session.

Scruuge’s TLDR

The token bill is a symptom; the disease is re-reasoning. Use AI to figure a repeating task out ONCE, compile the answer into deterministic automation, then run that — not the model — every time after.

Scruuge’s TLDR

You can't cut what you can't see — and roughly three in four teams can't. Uber isn't an outlier; it's what usage-based pricing does without a meter. Scruuge is the two-minute meter you run before finance finds out.

Scruuge’s TLDR

When the vendor quietly doubles its OWN cost estimate, that's not a rounding error — it's the trajectory. The meter only spins faster from here. Map your waste before the next 'quiet update.'

Scruuge’s TLDR

Hard proof the waste is real — and a warning we took to heart: switching models to save can backfire in long sessions. Switch your default, not your session.

Scruuge’s TLDR

Flat-rate AI is ending. Usage-based billing means your bill now scales with every token your team burns — including the 40–60% that's pure waste. The teams that mapped that waste first will feel this least.

Scruuge’s TLDR

When a chip-maker's own VP says compute costs more than the humans, the 'AI is basically free' era is over. Budget for it like the real line item it's become — then trim it.

Scruuge’s TLDR

The uncomfortable half of the story: a lot of AI spend isn't paying for itself. Scruuge's honest line — the problem is usually the WASTE, not the tool. Cut the tokenmaxxing before you conclude AI doesn't work.