Research Insights Made Simple 23 - The economics of AI in development (Category AI4SDLC)

The vote showed that the topic is interesting and therefore tomorrow 17:00 into live Let's break down the economics of AI in development. The bottom line is that tokens get cheaper, models get faster, but the company’s AI budget doesn’t necessarily decrease. The more work scenarios appear, the more tasks, agent chains, infrastructure, checks, and error costs become.

Let's talk about it. Why the reduction in the cost of a fixed level of quality increases demand and can increase the overall budget How an agent task turns into a long trace with dozens of calls, replays, and growing context – and why restrictions are needed on the entire workflow How do you count? cost per accepted taskincluding tools, infrastructure, human verification, alterations and the cost of error Why introduce a common quality gate and showback first, and then chargeback, routing and cost optimization? Where does the vendor lock-in appear and when is the local model really more profitable than the cloud after taking into account the quality and operation.

Separately I will show the working scenario before 2029 The price of today’s level of quality can decrease significantly, and the budget of a successful AI portfolio can grow. This is not the promise of the market, but a framework for talking about what exactly the company gets for this money.

The main question of the ether is what unit connects quality, cost and risk? The tokens are convenient for the supplier's account. For an engineering organization, the accepted work is more useful - a task that has been tested, did not require expensive alterations and gave the desired result.

Submissions: decade and longridYou can also post your questions in the comments to this post.

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