AI debates are often framed as a choice between optimism and alarm. A recent personal essay, “I Feel about AI,” makes a more useful point for business leaders: both reactions can be true at once.
The writer describes surprise at the apparent capabilities that emerge from language models, enthusiasm for the ability to create code, images and music, and the sense that software development has been permanently changed. At the same time, the essay raises concerns about safety, the strain AI crawlers place on open online communities, pressure on artists, concentration of power and resource use.
It is not a market analysis or a policy blueprint. It is, however, a compact account of the trade-offs operators increasingly have to manage.
The capability question is no longer enough
For builders, the appeal is straightforward. Generative systems can help produce drafts, automate routine work and expand what a small team can attempt. The essay’s description of models as producing the next token without an explicit planning algorithm also reflects a core tension: systems can display useful behavior without being fully predictable or easily interpretable.
That matters in deployment. A workflow that appears impressive in a demo may still require controls around access, output review, failure handling and escalation. The practical question for an executive is not whether a model can occasionally reason through a task. It is whether the organization can define the task boundaries, measure performance and own the consequences when the system gets it wrong.
The web is part of the supply chain
The essay’s sharpest operational concern is its account of crawlers and agents overwhelming open wikis and forums. Whether or not one accepts its language, the underlying issue is concrete: AI products depend on an internet ecosystem that is not designed to absorb unlimited automated demand.
For companies building retrieval, search or agent products, this makes data acquisition and web access a governance issue rather than a background implementation detail. Respecting access controls, rate limits and community norms is not merely a legal or public-relations exercise. It can determine whether a product’s inputs remain available and whether the company becomes a trusted participant in the ecosystems it relies on.
Efficiency can redistribute pain
The author also points to artists facing cheaper, faster AI-generated competition. Businesses adopting these tools will naturally focus on cost, speed and volume. But replacing creative work with synthetic output can carry less visible costs: weaker differentiation, damaged relationships with contributors, and reputational risk when customers view a brand’s content as low-effort or exploitative.
The relevant decision is not simply human versus machine. It is where automation improves a process and where human judgment, craft and accountability remain part of the value customers are buying.
What to watch next
The essay concludes that AI appears technologically positive but socially bleak. Leaders do not need to share that conclusion to take it seriously. The gap between technical capability and institutional readiness is becoming a business constraint.
Watch for four signals: stronger restrictions on automated access to online communities; customer and creator expectations around disclosure and compensation; more scrutiny of compute and resource consumption; and growing demand for meaningful safeguards in high-impact AI deployments.
The companies best positioned for the next phase may not be those that automate the most. They may be the ones that can show how their AI use creates value without eroding the communities, labor and infrastructure that make that value possible.
