AI ROI Reality Check

Tokens burned is not a result. Translate your AI-tool spend and the productivity gain everyone's claiming into net realized value — after rework, throwaway, and the maintenance tax on generated code.

Your AI program

Estimates are fine — this is a model, not a meter.

People using AI tools

Fully-loaded hourly ($)

Seat cost / month ($)

Token / API spend / month ($)

The claim

Claimed productivity gain (%)

Share of work AI-assisted (%)

The reality

Review / rework overhead (%)

AI output actually shipped (%)

Maintenance tax (%)

Incidents / quarter

Avg incident cost ($)

Verdict

Oversold — real value, but a fraction of the claim

Claimed value / yr

$1.03M

the headline

Net realized / yr

$236K

23% of the claim

Tokenmaxxing gap

$790K

claimed − realized

ROI multiple

5.6x

net ÷ AI spend

Where the value erodes

From the headline claim down to what actually lands

Stop measuring → start measuring

Retire the vanity metric; report the outcome instead

Vanity metric (tokenmaxxing)	Outcome metric to report
Tokens consumed / API spend	Value shipped per $ of AI spend (ROI multiple)
Lines of AI code generated	Change-failure rate / defect escape rate
Suggestions accepted / acceptance rate	Cycle time: idea → production
Seats deployed / % adoption	Share of shipped value traceable to AI
“We're an AI-first eng org”	Maintenance load of AI-authored code

Recommendations

What to do with this

Report value shipped and net ROI to leadership — never tokens, lines generated, or acceptance rate.

Most of the claimed gain is lost to review, rework, and maintenance — not tooling. Invest there before buying more seats.

Escaped-defect cost already exceeds your AI spend. Track change-failure rate on AI-assisted changes specifically.