ChatGPT PowerPoint’s Free Ride Is Over • The Weighted Average

ChatGPT for PowerPoint moved from a free experiment to metered production use after August 6, with a typical task consuming 10–50 credits. At 100 tasks, that published range becomes 1,000–5,000 credits, a 5× spread that makes per-deck telemetry—not seats—the first procurement control.

A deck is now a variable-cost workload

The OpenAI ChatGPT rate card states that PowerPoint charging begins after August 6 and places a typical task between 10 and 50 credits. The arithmetic is deliberately simple: 100 × 10 equals 1,000 credits, while 100 × 50 equals 5,000 credits. Dividing the high end by the low end produces the 5× headline spread. It is a usage range, not proof that any particular team will land at either endpoint.

That range meets a weak measurement baseline. KPMG found only 7% of 2,145 surveyed leaders reported established AI ROI. A cross-source rollout rule follows: if only 7 of every 100 pilot teams can initially demonstrate an accepted-deck outcome worth funding, OpenAI’s 10–50-credit task range puts the first measured success across those 100 trials at at least 143 credits and as much as 714 credits—1,000 ÷ 7 to 5,000 ÷ 7. This is an illustrative planning denominator, not a forecast or an OpenAI customer conversion rate; it shows why budget belongs to accepted outcomes, not attempts.

Do not convert those credits to dollars. OpenAI’s public material does not establish one universal customer dollars-per-credit rate, and enterprise contracts may differ. Multiplying by a guessed conversion would turn sound unit arithmetic into invented economics. Finance should instead apply its own contracted rate privately and retain credits as the comparable workload measure in cross-team reporting.

The rate card also exposes why two apparently similar decks can diverge. GPT-5.5 is listed at 125 credits per million input tokens, 12.5 per million cached input tokens, and 750 per million output tokens. OpenAI’s worked Workspace Agent example combines 20,000 input, 80,000 cached input and 5,000 output tokens for 7.25 credits. That example is not a PowerPoint quote, but it shows how input reuse and generated output shape the meter.

OpenAI’s Business release notes document the product’s enterprise rollout, while its Enterprise and Edu release notes provide the corresponding managed-workspace chronology. OpenAI’s separate voice documentation illustrates the same workspace pattern of feature enablement under administrative control. Together with the rate card, those pages describe a familiar transition: a workflow graduates from adoption incentive to measurable consumption. The economically important event is not that AI can make slides. It is that the marginal task is no longer priced at zero.

That transition belongs beside the archive’s tokenmaxxing analysis, which argued that spend per outcome matters more than nominal unit price. It also echoes Actualyze’s gateway break-even test: count retries, overhead and successful jobs rather than admiring a cheap-looking input rate. A finished deck that needs heavy human repair is not a low-cost outcome merely because generation used 10 credits.

Meter revisions before buying volume

The right rollout starts with a four-week pilot across distinct deck types: template refreshes, data-heavy reviews, narrative strategy decks and first drafts. Record credits, elapsed time, number of regeneration cycles, human editing minutes, approval outcome and whether the deck shipped. The unit of value is an accepted presentation, not an invocation. Teams should compare credits per accepted deck and minutes saved against their contract-specific credit cost without publishing an imaginary universal dollar figure.

A useful control is to preserve templates and stable reference material so caching can matter, then test whether reuse actually lowers consumption. Another is to cap regeneration loops. If a user asks for five cosmetic variations before selecting one, the system may save composition time while consuming far more credits than a single-pass task. The 10–50 range is wide enough that workflow design can dominate seat-count forecasting.

Who should adopt now? Presentation-heavy analyst, sales and operations groups with repeatable templates and measurable editing baselines have the clearest case. Who should delay? Teams that cannot attribute consumption to a department, cannot measure revisions, or make only occasional high-stakes decks should first establish telemetry. Central procurement should avoid an enterprise-wide assumption based on the low endpoint.

The counterpoint is strong. Even 50 credits may be inexpensive relative to a few minutes of skilled employee time once a buyer applies its actual contract rate. Cached input is priced at one-tenth of uncached input for GPT-5.5, so disciplined templates may pull production toward the low end. If observed decks cluster near 10 credits and reduce total revision time without hurting quality, broad adoption is justified.

The verdict breaks the other way if decks repeatedly approach 50 credits, require extensive repair, or shift labor from creation into fact-checking and formatting. Evidence that changes the decision is concrete: median and p90 credits per accepted deck, human minutes saved, regeneration count, defect rate and the customer’s own contracted credit price. Seat adoption alone cannot answer the question.

Procurement should also separate exploration from production. Give a pilot group a bounded credit pool, publish weekly usage by deck type, and require an owner for templates and evaluation. After four weeks, forecast from the observed median and p90 rather than OpenAI’s full published range. A central team can then negotiate or allocate credits using actual workload shape, while departments decide whether their accepted-deck economics justify expansion.

The shutdown rule matters as much as the expansion rule. Pause a workflow when credit consumption rises for two review periods without a corresponding increase in accepted decks or saved editing time. That prevents novelty, repeated regeneration and abandoned drafts from masquerading as adoption. Resume only after the team changes templates, task scope or review practice and demonstrates a better outcome rate.

Today’s Astra governance lead recommends attaching approval to model versions and evidence rather than brand names. The same discipline applies economically: attach budget to accepted deck outcomes rather than a PowerPoint feature toggle. OpenAI has made the meter visible. Operators now have to make the denominator honest.