Enterprise AI Strategy

Fix your value forecasts and investment decisions, then judge AI spending against labor cost and delivery. A cheaper model won’t rescue the wrong roadmap.

Elon Musk started cutting Twitter before ChatGPT launched. Most companies will spread the same AI-assisted operating change across a decade because that is what executive incentives reward.

Your AI cost memo sounds sensible until you send it to San Francisco taxi drivers and ask them to avoid hills, skip breaks, and push the cab.

One company cut its $200-per-engineer AI bill to zero and kept delivery flat. Its playbook: 98-hour weeks, story-point accounting, employee-funded training, and attrition.

If a senior engineer's AI inference budget is less than one Starbucks drink every other working day, turn AI off for 90 days and measure what the shutdown exposes.

AI value is not an inference metric. Start with product outcome, cycle time, total cost, and cost of delay, then overlay inference investment.

Per-engineer token caps optimize the most visible cost while hiding labor, delay, and business value. Budget AI production by accepted outcome, not individual contributor.

Price the outcome before its tokens. A rational token budget is a function of total delivery economics, and even 150% of labor cost can make sense when it buys enough time.

Your CFO should not be asking what you spent tokens on as if the model invoice is a minibar tab. Your CFO should be asking why the highest-ROI production input in the software portfolio is underfunded.

Your token burn is not the problem. It is the first invoice honest enough to show that your engineering work has no value discipline.