Don't Wait for the Next Model Generation; Do the Math First

A friend recommended the "AI Waiting Equation," so I'm giving it a shot to see if it's actually useful. The name sounds intimidating, but it boils down to one question: use AI now, or wait for the next-gen model?

The waiting equation originated with sci-fi author Robert L. Forward and was later expanded by Andrew Kennedy. The idea is that if technology will continue to improve, waiting might be more cost-effective. Applied to projects, it's about doing the math: waiting has costs, and adopting early carries risks.

When technological progress is rapid, waiting for compute can easily become a lazy tyranny.

Ethan Mollick's warning hits hard. I agree from an engineering perspective too. What many people really struggle with is facing the reality of "getting a minimal workflow running right now." A minimal workflow means taking a small batch of tasks, running them through, and seeing if the model works, where it fails, and whether humans need to step in as a safety net.

This tutorial creates an "AI Waiting Decision Table." Suppose you're deciding whether to handle customer feedback summaries, resume classification, and test report generation now, or wait for a model upgrade.

First, compare two options. Option A: Do it now using current models. Run 10 samples first, recording time taken, errors, and manual review time. Option B: Wait for the next generation. Assume accuracy improves after the upgrade, but you have to pause the project and bear the opportunity cost of the waiting period. Opportunity cost is what you lose by not working during this time—how much less profit, how many more errors, and how much extra manpower consumed.

Beginners should use a spreadsheet; those more experienced can use the terminal. Here's the most intuitive approach. Open your spreadsheet software, type Task in A1, Quantity in B1, Minutes Saved Per Task Now in C1, Days to Wait in D1, Daily Opportunity Cost in E1, and Next-Gen Improvement Ratio in F1. Fill in a sample row: Customer Feedback Summary, 20, 20, 30, 10, 10%. In G1, calculate total savings now (B1 * C1); in H1, calculate waiting cost (D1 * E1); in I1, calculate additional benefit from next-gen (G1 * F1); in J1, calculate the net difference (G1 - H1 + I1).

With these numbers, G1 shows 400, H1 shows 300, I1 shows 40, and J1 shows 140. A positive number means doing it now is more worthwhile.

I ran a similar small-sample test on my end, and the conclusion isn't mysterious. For most daily tasks, the benefits of waiting rarely cover the costs of stalling.

What's truly worth waiting for is usually when there's a hard gap in model capabilities, such as inability to read long texts fully, poor image/video processing, or frequent deviations in structured output. Structured output means generating results according to fixed fields, like dates, amounts, and party names (Party A/B).

Here are a few pitfalls.

Only calculating model scores without accounting for manual review. Improved benchmark scores don't mean your dirty data won't cause failures. In customer feedback, a phrase like "this plan is okay but don't push it" might be judged as positive by the model.

Calculating waiting days as calendar days without considering team stagnation. If a project stops for 30 days, you lose 30 days of potential labor savings, plus pilot windows, client trust, and internal project approval opportunities.

No acceptance criteria. If resume classification errs once, you might have to rerun a whole batch. Acceptance criteria clearly define "what counts as correct, what is wrong, and who fixes it if it goes wrong." Previously, in my reply to the post about WorkBuddy misclassifying archived job positions, the core issue was a lack of typical samples.

Using "waiting for the next gen" as an excuse to avoid work. Many projects fail because they aren't broken down small enough. If you ask it to "automatically generate high-quality reports," stability will naturally suffer. If you ask it to "extract amounts, dates, and parties from contracts," the success rate will be much higher.

My judgment is: run 10 samples first, then decide whether to wait. If 8 out of 10 are usable, set up the workflow, logging, and rollback mechanisms immediately. Rollback mechanism means being able to revert to the previous version if errors occur. If all 10 are wrong, then waiting is acceptable.

Over the next year, most teams will get their minimal workflows running first, organizing failure samples into acceptance criteria. The waiting equation will shift from a strategic slogan to a line item in project budgets.

Throw real tasks into the table and run 3 cases. Pick the messiest task and manually annotate 10 items, clearly defining error types.

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📌 This article is compiled from Hacker News, original source: https://xendo.bearblog.dev/the-wait-equation/

All rights reserved. This is a compilation and independent analysis based on public reporting.