I want to talk about something that doesn’t get enough airtime in the AI conversation: the cost.
Not the philosophical cost, not the job displacement debate. I mean the actual dollar figure on your cloud invoice at the end of the month.
AI has been genuinely exciting. The use cases have been real and measurable, not just demos. But the cost curve has a shape that most organizations haven’t fully felt yet, and I think that’s worth being honest about.
The Use Cases Are Real
Let me be clear: the things AI is actually doing well right now are impressive.
Software development is the obvious one. Developers using AI-assisted coding tools are producing first drafts faster, catching common patterns, and spending more time on the interesting architectural decisions instead of boilerplate. The ceiling on individual developer output has quietly risen.
Summarization and knowledge access is where a lot of enterprises are seeing quiet wins. RAG-based systems that let employees search internal documents, policies, and runbooks in plain language instead of clicking through wikis are genuinely saving time. Not glamorous, but measurably useful.
Data analysis and reporting has changed. The ability to describe what you want to see and get a workable SQL query or a chart scaffold in seconds has made data more accessible to non-technical stakeholders. That’s a real democratization.
Customer support tooling is maturing. AI-assisted triage, suggested responses, and escalation routing have cut handling times at a lot of companies. When it’s well-tuned and monitored, it works.
Code review and security scanning is getting more interesting. AI tools that flag logic issues, insecure patterns, or test coverage gaps are starting to feel genuinely useful rather than just noisy.
So yes, real use cases, real value. I’m not here to dismiss any of that.
The Bill Is Coming
Here’s the thing nobody talks about over pizza at the AI meetup.
Running large language models at production scale costs money in ways that are easy to underestimate until you’re deep in it.
API usage fees add up fast. A development team playing around with a tool might generate thousands of tokens per session. Multiply that by an enterprise workforce, and the numbers start climbing. Then multiply it again by feature expansion, because once you ship one AI feature, stakeholders want five more.
Managed AI services are billed on usage. Unlike a server you size once and leave alone, AI inference cost scales directly with engagement. If your product is successful, your bill scales too. That feedback loop can be uncomfortable.
Most cost estimates I’ve seen from teams early in their AI journey are off by a factor of two to five by the time they hit real production load. Not because they were careless, but because actual usage patterns are hard to predict before you have data.
The normal consumer is going to feel this in a different way. Subscription AI features are landing in software people already use: productivity suites, creative tools, customer-facing applications. Right now, a lot of those costs are subsidized by VC money or absorbed as a competitive play. That’s not a permanent state.
When the pricing normalizes, there’s a real question about how much users are actually going to pay for AI features as a line item versus as a free expectation. The answer is probably: some will, many won’t, and the reckoning will be noisy.
What Teams Should Be Thinking About
I’m not saying don’t use AI. I’m saying go in with eyes open.
Model selection matters. Not every use case needs the most capable and most expensive model. Many tasks that feel like they need GPT-class capability actually work fine with a smaller, faster, cheaper model. Building a routing layer that matches task complexity to model tier is worth the engineering investment.
Token efficiency is an engineering discipline. Prompt design, context window management, and caching repeated context can make a meaningful difference on cost at scale. This is not premature optimization. It’s just good product engineering.
Build cost observability early. You should be able to answer, per feature and per user cohort, what AI is actually costing you to serve. Most teams can’t answer this until it’s a problem.
Have a value threshold. What level of cost is acceptable per outcome? If your AI feature saves a support agent 3 minutes per ticket, how many tokens is that worth? Doing this math early prevents uncomfortable surprises later.
The teams that will use AI well over the long run are the ones treating it like any other engineering resource: valuable, but finite, and subject to basic operational discipline.
The growth is real. The use cases are legitimate. The costs are also real, and the curve bends sharply. Planning for all three at once is what separates durable AI adoption from a wave of expensive experiments that didn’t quite work out.