The Quiet Divide I Found While Building an Agent

When I started building OpenClaw, I assumed the hard problems would be engineering ones: making the gateway resilient, wiring channels together, and teaching agents to call tools instead of hallucinating them. I didn’t expect to stumble across a social and economic question that felt bigger than the code — a slow, dawning realization that access to the best models isn’t just convenience; it can become a direct vector of advantage.

The realization arrived plainly. On my first day running OpenClaw with an Anthropic Opus 4.6 key, I watched credits drain fast — about $50 in twelve hours. I was experimenting, letting the agent riff, iterating long prompts and longer sessions. The results were striking: sharper synthesis, fewer prompt gymnastics, and a tone of thoughtfulness that made delegation feel safe. But the price was real. When the balance dropped I switched to Claude Haiku and then to Gemini Flash as my default: cheaper and usable, but not the same. Now I save Opus for the handful of tasks where the difference matters. I ration tokens the way someone budgets a scarce resource.

That small, personal budgeting choice revealed a structural gap. If you can afford to run the most capable models continually, your agent becomes perpetually smarter, more proactive, and more able to automate high-leverage tasks. It can triage messages, draft polished proposals, iterate marketing hooks, and run experiments — all with a level of fluency and trust that reduces oversight. If you can’t, you get episodic assistance: flashes of help, interleaved with manual work. The effect is straightforward and stark: money buys thinking.

What that looks like in practice is worrying precisely because it’s so mundane. Imagine two households. One can afford a steady stream of premium model access for homework help, career coaching, language tutoring, and daily personal automation. The other gets occasional, cheaper assistance — enough to be helpful, but not transformative. Over time, the household with persistent, higher-quality AI assistance may see better grades, more productive work habits, faster career moves, and more efficient small-business operations. The advantage compounds. Money buys better answers, which make it easier to earn more money, which buys more compute — a feedback loop that maps economic capital into cognitive capital.

Call it the token economy in miniature: access to compute and model quality becomes a resource you budget. Consumers will balance token budgets just as people budget for childcare or tutors. The practical consequence is that intelligence-enhancing services can become another axis of inequality. Instead of paying a private tutor or a skilled coach, you pay for an agent that thinks better, faster, and at scale. What used to be a localized advantage — hiring a tutor for your child, hiring a consultant for a startup — can now be purchased as continuous, personalized cognition.

That’s not to say smaller models are useless. They’re often astonishingly capable for many tasks, and in practice I rely on them the vast majority of the time. But the qualitative gap at the top still matters: long-form planning, nuanced strategic thinking, and complex synthesis tend to be easier on the largest models. Those differences change delegation decisions. You’ll trust a model you can count on; you’ll run it more often; you’ll let it do more.

The implications spill beyond individuals. Founders with steady access to top models can prototype faster and iterate product ideas more cheaply. Creators who can afford higher-tier inference can produce higher-quality content more quickly. Teams that can run expensive models as part of background automation will free up human attention for higher-level work. The result is a distributional effect that reinforces existing advantages: capital begets better tools, which beget more capital.

I’m not describing a science-fiction dystopia; I’m describing an economic gradient forming in real time. It’s visible in pricing choices, in the way providers segment access, and in the decisions people make when they throttle an agent because a credit limit is approaching. It is visible in the quiet shift from time-based scarcity (I don’t have enough hours) to cognitive scarcity (I don’t have enough model-quality).

Two images stick with me. The first is a dashboard showing credits ticking down as an agent chased an idea through multiple drafts and research queries; it felt oddly intimate to see thinking monetized in real time. The second is the comparison between trusting a model with a complex multi-step task when you know it will be cheap enough to run iteratively, versus forcing a human (or yourself) to babysit because the compute cost for repeated runs is prohibitive. One path accelerates; the other grinds.

There’s a deeper social logic here. Markets will push for cheaper, faster inference — and already are. Distillation, quantization, and clever engineering will narrow the gap between the largest models and the best small ones. Open-source communities are building capable alternatives. But these responses don’t automatically erase the gap. Running and maintaining high-performance inference stacks still requires infrastructure and expertise. Even when models are free at the research level, operational costs for reliable, low-latency, private use persist. In short, technical progress mitigates but does not automatically equalize access.

This feels like one of those moments where a technical trade-off has a social tail. If advanced cognition becomes paywalled in practice, we will see new behaviors: people who can afford it will delegate more thought to machines; people who can’t will design workflows to avoid needing it. Entire classes of opportunity could stratify along an axis of collective attention and model access. The quality of your life — or your child’s education, or the competitiveness of your small business — could increasingly hinge on how much money you allocate to your agent.

I don’t have a neat solution. I’m not listing policy prescriptions or a how-to guide. This is a note more than a manifesto: an observation from the trenches, and a warning about where a reasonable technical trajectory could lead if left unexamined. The most important step, maybe, is awareness. Once you see tokens as a resource that buys thinking, you start to notice how decisions change — both yours and everyone else’s.

So I’ll end with that: a small, uneasy question I keep returning to as I wire more systems and tune more budgets. We are designing systems that translate money into thought — sometimes subtly, sometimes loudly. What kind of society do we want if the sharpest thinking can be rented and the rents compound advantage?

What does it mean when thinking is something you can buy by the hour?