AI Agents: Getting More Done Isn’t the Same as Creating Value
Article 7 in a series on building with and using AI tools as a non-native technologist
Intro
There’s been a lot of noise lately about AI agents — most recently OpenClaw, before that OpenAI’s agents, Anthropic’s, Google’s, and the growing ecosystem of tools that let you automate workflows, string tasks together, and run complex operations with minimal intervention. The pitch is compelling: more getting done, less effort spent doing it.
I’ve found most of that pitch to be true in a narrow but important sense. These tools genuinely do help you get more done. I use them regularly for exactly that purpose.
But I keep noticing a gap between the claim and what’s actually happening — between getting more done and creating value. They’re not the same thing. The confusion between them, I think, is one of the more consequential misunderstandings in how these tools are being talked about right now.
Accompanying audio note:
The Productivity Claim
When people talk about AI productivity gains, they’re usually describing something real: tasks that previously took an hour now take ten minutes. Analyses that required manual effort can be automated. Research that involved dozens of searches can be compressed into a few prompts.
This is genuinely useful. I don’t want to dismiss it. But productivity, strictly speaking, just means output per unit of input. Getting more things done in less time. And productivity is only valuable to the degree that the things being done are worth doing.
That sounds obvious…but watch what happens in practice. Someone deploys an agent that automates a multi-step process. They count the hours saved. They extrapolate to the team, multiply by average hourly rate, and arrive at a dollar figure. The number sounds large. The announcement goes out: $X million in value created.
In most cases, this conflates two very different things: the cost of the activity and the value of the outcome.
Getting more done faster is only as valuable as what’s getting done. If an AI agent helps you accelerate a process that was already misaligned with what your customers needed, you’ve become more efficient at something that shouldn’t have existed in the first place. That’s not value creation — it’s waste, just at a higher throughput.
The More Interesting Question
None of this means productivity gains are worthless. It means the question worth asking is: what do you do with the time you get back?
There are two very different answers. One is that the freed capacity gets absorbed by more tasks of the same kind — a never-ending queue of lower-priority work that expands to fill available bandwidth. More things get done. The ratio of important things to unimportant things stays roughly the same. The volume increases.
The other answer is that the freed capacity gets redirected toward genuinely higher-leverage work — the things that actually move outcomes, that create something worth having, that compound over time. If that happens, then the productivity gain is real in the deeper sense: it bought you time to work on the things that matter.
Which answer you get depends almost entirely on whether you have the judgment and the agency to make that call. Judgment to identify what the high-leverage work actually is. Agency to actually redirect toward it — which, in an organizational setting, is far from guaranteed.
In a previous article I wrote about the difference between where you build in the stack and where your time actually compounds. The same logic applies here. Productivity tools don’t answer the allocation question. They just widen the gap between people who are asking it well and people who aren’t.
A Third Category
There’s one more thing I’ve been sitting with, and it’s harder to articulate cleanly.
Most discussions of AI productivity focus on one of two categories: either you were doing the task before and now do it faster, or you weren’t doing it and now you can. What I’ve started to notice is a third category — things you technically could have done before, but that were just annoying and friction-laden enough that you didn’t, in practice, ever do them.
A specific example: I’ve had FSA accounts for years, with funds that expire if unused — the classic use-it-or-lose-it situation. Submitting claims, especially for borderline or unfamiliar expense types, has always involved navigating opaque requirements, decoding denial reasons, and figuring out exactly what documentation was needed. Annoying enough that I’d let some money expire rather than waste time fighting the process.
Earlier this year I used ChatGPT to work through a claim denial. Not just as a search engine — as a conversational partner. I described the situation, asked about common denial reasons for that category, worked through what documentation would address each one, and drafted a resubmission. The claim was approved. Several hundred dollars I would have left on the table.
The productivity framing undersells what happened there. I didn’t do that task faster — I did a task I wasn’t going to do at all. The LLM didn’t just reduce time cost. It reduced friction below the threshold where I’d actually act.
There’s probably a psychological dimension to this as well. Something about the conversational format — the ability to ask follow-ups, to get a response that acknowledges your specific situation rather than returning generic search results — lowers the activation energy in a way that a list of web links doesn’t. I’m not sure whether that’s because the medium maps to how people have evolved to interact with and process information, their mental model or something else. But the effect is real.
This matters because it suggests a category of value that doesn’t show up in time-savings calculations. Not hours recaptured. Not existing tasks accelerated. But things that now happen that otherwise wouldn’t — claims filed, questions answered, decisions made with better information. That’s not productivity. It’s enablement.
What This Means in Practice
The honest version of the AI agent story is more nuanced than the headlines suggest. These tools genuinely do help you get more done. In the right conditions — when there’s judgment about what’s worth doing and agency to act on it — that translates to real value. When those conditions are absent, you get faster hamster wheels.
For anyone building with or using these tools, I think the most important questions aren’t about the tools themselves. They’re about the human side of the equation: What are you actually trying to accomplish? Where does the allocation of your time currently mismatch what you’d choose if you thought clearly about it? And what category of things — the ones you’re not doing because the friction cost is too high — might be worth reconsidering?
Getting more done is the easy part. These tools are genuinely good at it. The harder part — which the tools don’t touch — is knowing what to do with the time.
This is Article 7 in a series on building with and using AI tools as a non-native technologist. Previous articles covered financial planning with AI tools, a framework for city selection, evaluating LLM tools, building the Seattle Nature Access Map, why judgment was always the bottleneck, and where in the stack to build.