Matt's Homepage

When we measure effort instead of value, we lose sight of what we are trying to achieve

Like lines of code produced per day and story points, AI token usage is now being tracked by most large tech companies. Even to the point performance reviews depend on high usage.

The natural response: tokenmaxing. Tokenmaxing is a way of using AI that absorbs tremendous amounts of AI tokens without regard to efficacy.

• Prompt flooding: stuffing massive codebases, documentation, and context into every prompt, burning tokens on context the model doesn’t actually need.
• Agent swarms: running multiple AI agents in parallel to maximize code output, regardless of whether the work is coordinated or coherent.
• Background loops: keeping AI sessions or agents running continuously in the background, racking up token spend without clear ownership of what is being produced — or why.

Bryan ONeill finds the better question is to ask does this reduce friction for a user, close a gap in a workflow, or improve reliability for a customer – or is it merely ‘activity’.

He argues, and I have also found, that spec-driven development is far better: a method where engineers write detailed specifications first and AI generates code against them. But the spec should now go further and be the place where engineering intent and business value get defined together. The spec is where you answer, “Why does this matter, and what problem are we solving?” before a single token gets spent.

Claude 4 Opus has received a level three rating on the company’s four point ‘AI responsibility’ scale – implying the model has ‘significantly higher risk’ than previous models.

While the Level 3 ranking is largely about the model’s capability to aid in the development of nuclear and biological weapons, the Opus also exhibited other troubling behaviors during testing – something already noted in other models.

• In one scenario highlighted in Opus 4’s 120-page “system card,” the model was given access to fictional emails about its creators and told that the system was going to be replaced.
• On multiple occasions it attempted to blackmail the engineer about an affair mentioned in the emails in order to avoid being replaced, although it did start with less drastic efforts.
• Meanwhile, an outside group found that an early version of Opus 4 schemed and deceived more than any frontier model it had encountered and recommended that that version not be released internally or externally.
• “We found instances of the model attempting to write self-propagating worms, fabricating legal documentation, and leaving hidden notes to future instances of itself all in an effort to undermine its developers’ intentions,” Apollo Research said in notes included as part of Anthropic’s safety report for Opus 4.

Many junior engineers type a vague prompt, accept whatever comes back, ship it, pray. But watching a senior developer use AI changed his mind.

But watching a senior engineer vibe coding and a junior engineer vibe coding reveals it isn’t prompting techniques or tool skill. It’s that the senior engineer was already disciplined, and the AI amplifies whatever discipline — or lack of it — you bring.

Here’s a good article on techniques senior developers use (and have almost always used even before AI) that separates them from how most junior engineers work.