... explaining problems, the better the results became. 6. Every feature has a cost beyond development. Third-party APIs, hosting, maintenance, testing, support, and even the time it takes to explain a feature to users. AI can make something cheap to create without making it cheap

... user told me my product was too cheap. Not a complaint about quality. They said the price made them doubt whether it was serious. I had spent years worrying that I was too expensive. It never occurred to me that some people read a low price as a warning sign. Cheap

... country's premier engineering institutes are an export sector for global tech companies contributing to the GDP of first world countries, all while said companies setup shop in Bengaluru, Pune, Hyderabad, Gurgaon and outsource majority of their work to cheap Indian labor. But by all means, lets purchase AI tokens and further add to the problem ...

... What tools are you using for competitor research across conferences (event marketing), paid, influencers, AI SEO, social and content? I want to do a deep dive into how our competitors

... labelled, and whether it can be operated cleanly, starts to matter to software as well as to people. I haven't tested this on our own app yet, so this is a hypothesis, not a finding. The cheap first check is the one you should be doing anyway: do your buttons and fields have real accessible labels? If you're choosing a model this week, what are you measuring besides price per token? Comment from Mona Truong(@monatruong_murror): @matheus ... ... between language pairs. On the cheaper tier the overall error rate looked fine, but pairs where neither language is English came out consistently worse on translation. So those requests go to the pricier model automatically and everything else stays cheap

... used to offer super cheap monthly plans thinking it would make closing deals way easier. Turns out low prices just made people think the software was cheap

... something clean, confident and wrong, and that sails straight past any eval checking format instead of truth. Nobody catches it until a user does. So the real cost of routing isn't the token delta. It's that the cheap path now needs a verification step, and verification is another call, which eats the margin you routed for in the first place. What's worked for us: route on how checkable the task is, not how hard it looks. Anything ... ... with a deterministic check, parseable output, a schema, a test that passes or fails, goes cheap

... same commit, including the ones nobody would ever file under copy. Curious whether your 38 modals audit turned into anything automated, or stayed a one-off pass? Comment from Siarhei Hamanovich(@hamanovich): For me deleting the code was the cheap part The expensive part was everything that still described it: FAQ, docs, pricing page, whatever an AI assistant indexed last month... I had a FAQ telling people to install a third-party add-on for audio after the product ...

... Apple's own support pages. Apple In-App Purchase: 26%, or 15% if you're in the Small Business Program, the Mini Apps or Video Partner Programs, or on an auto-renewable subscription after the subscriber's first year. Alternative payment processing inside your app: 20%, or 10% for those same groups. Out-of-app offers with an actionable link: a 15% store services commission, or 10% for those groups, on anything bought within 7 days of the tap. Apps ... ... distributed outside the App Store entirely - an alternative

... Most routing I've seen picks the cheapest model that clears some benchmark, and that optimises the one cost that barely matters. Tokens are cheap. The expensive thing is an answer that looks right, ships, and gets caught by a person twenty minutes later, or never. So the number I care about isn't cost per call, it's cost per accepted output. A model that's 4x cheaper but needs two regenerations and a careful human read ... ... cheaper, it just moved the bill somewhere you weren't measuring. Once you count it that way the routing table changes, and a few tasks that felt like obvious cheap

... One thing I underestimated with AI coding: The problem isn't always that the agent makes a bad change. It ...

... incredible at the time. Then I pulled the usage. 9 of them cover roughly 80 percent of runs, and around 40 have never been opened by anyone outside the team. The build time wasn't the cost. That was cheap, that's the whole point of working this way. The cost is that every one of those 111 is a surface that can break, and I now have a settings page nobody can scan and a support inbox where ... ... often can't tell which app someone means. Cheap

... been thinking about AI product economics, and I think "cost per API call" is only part of the problem. An AI feature can look cheap from the infrastructure side and still become expensive to operate. I like thinking about four different costs: 1. Inference cost How much are the model calls actually costing? That's the obvious one. 2. Retry cost How often does the system call the model again because the first result failed validation, timed out, or produced ... ... something unusable? A cheap

... pairing an expensive AI model with a cheap one to save money, Anthropic's cost guide has a sentence worth reading twice: in their measurements, "a second model paid off in two shapes". Shape one, the advisor: the low-cost model runs the whole loop and phones a frontier model only when stuck. Advice comes back short, roughly 400 700 tokens, so you pay top rates only for judgment, never for the bulk generation. Their data says the advisor closed ... ... capability gap to the stronger model with a catch I'll get to. Shape two, the orchestrator: the frontier model plans and delegates, cheap

... telling them. In Arteza a quick edit and a full multi shot render are about 12x apart in what they cost us to run. Both are priced in the same unit. So someone learns an exchange rate from the cheap action, builds a mental model, then spends a third of their balance in one click and feels robbed. That isn't a pricing bug. We asked them to hold a cost model in their head that we never showed them ... ... That slot machine feeling is so real when users burn credits on bad outputs. I've seen a few teams use a free retry token for failed jobs instead of touching credit balances. How are you detecting system failures vs

... Every multi model product ends up building a router, and the first version always optimises for cost. Cheapest model that ...

... perceived as completely normal (at least for me). As equal human work. How do you view things generated by artificial intelligence? Maybe it's true that who is behind the output and prompting also influences the result (professional vs. amateur). But it clashes a bit with the fact that: if we do things with AI, why do we demand less money for the output? (We perceive it as cheap

... breakdown by price is the bit I'd tape to the wall. AI products priced above $250 a month: 70% gross revenue retention, essentially B2B SaaS territory. Priced $50 to $249: 45%. Priced under $50 a month: 23%. Being cheap and easy to buy is the same property as being cheap

... leadership." Copy, paste, post. Zero lived experience, zero original thought. It's just AI slop in a blazer. AI-written emails to people who matter. Your client can tell. Not because the grammar's off because the warmth is. VS GOOD AI JUDGEMENT Using AI to actually look at what a candidate did. Not keyword-matching a CV pulling apart their portfolio, cross-referencing their claims. You still decide. You just decide better. Using AI to draft, then making ... ... Comment from Ghost Kitty(@ghost-kitty-9927815): Comment Deleted Comment from Omri Ben-Shoham(@omri_ben_shoham1): One I'd add, from actually running scheduled agents unattended: bad AI judgement is letting it run autonomously somewhere you have no cheap

... noting, in the same team's 2025 report that figure was 9%. It's steepening, not flattening, as adoption matures. Code churn, lines deleted against lines added, up 861%. Some of that churn is healthy refactoring that was finally cheap enough to do. Some of it is code that got accepted fast and thrown away a week later. You cannot tell which from the outside, and honestly most of us can't tell from the inside either. One finding ... ... expensive. The acceleration is real. I'm not giving it back. But the thing to internalize from a dataset this size is that the work of making output safe and correct didn't shrink when the output got cheap

... save money. Cost per request dropped about 40% and the dashboard looked great. What the dashboard didn't show was people re-sending the same message, so once you count the retry and the follow up clarification, the cheap path burned more tokens than just sending it to the big model once. The mistake wasn't the routing. It was measuring cost per request instead of cost per finished task. A request that has to be asked twice ... ... cheap

... Deciding what's worth building. Getting anyone to care once it's out. AI will make anything I describe and never once ask if it should exist. So I'm curious where it landed for everyone else. Building got cheap what's the thing slowing you down now? Taste? Distribution? Just deciding what not to build? Comment from Harsh Patel(@im_harsh5): Building was never the hard part for me either, I shipped an invoicing app in a few weeks ...

... Everyone read today's pricing news as costs going down. On agent workloads it doesn't work like that. A cheap model that gets something wrong rarely fails loudly. It returns something plausible, the next step eats it, and you find out four steps later. By then you've paid for the cheap

... found a clean answer to: does anyone have a real methodology for tracking cost-per-solved-task rather than cost-per-token? Token counts alone feel misleading once you factor in retries, wrong turns, and the fact that a "cheap" run that needs 3 follow-up prompts to actually work isn't actually cheap

... would be building the AI. It wasn't. The hardest part was figuring out which questions every founder should answer before writing a single line of code. Some examples: Is this problem painful enough? Are people already paying for alternatives? What assumptions are completely untested? What would make someone switch? Instead of giving instant answers, FounderMind AI challenges ideas from multiple perspectives including market demand, competitors