about post
(1) AI public discourse
The arrival of AI is often compared to the invention of fire, yet it is discussed as if it were the launch of a new iPhone. Public AI discourse is dominated by model efficiencies, benchmarks, and performance gains, while in reality human civilization is undergoing a historic transformation across many aspects of society.
(2) Anxiety because of uncertainty
This mismatch in scale helps explain why people feel uneasy. The anxiety is not necessarily about a jobless future, but about uncertainty. We sense that many things will change, yet we have little clarity on how. History shows that transitions of this magnitude can be destabilizing or catastrophic when they are not understood or managed well.
(4) What’s missing: an overarching framework and access
What’s missing from public conversation is not expertise, but an accessible way to connect the dots. Questions about institutional impact, organizational incentives, and human judgment do exist, but they remain fragmented, technical, or buried in academic and policy discussions. Without an overarching framework, it becomes difficult to debate or guide the transition while decisions are actively being made.
(8) What this blog does
This blog focuses on making sense of this transition phase of AI. It looks at how AI is being implemented across socio-economic structures, organizations, and products, bringing together research from multiple fields and putting forward an opinion grounded in common sense. The underlying idea is that whatever systems we build—no matter how technical, autonomous, or agentic they appear—are shaped by human values, incentives, and constraints.
(9) Who it’s for
The aim is to help readers become more active participants in this transformation by giving them a clearer foundation. Whether you are an employee, entrepreneur, policymaker, or citizen, this blog is meant to make the transition more legible and the conversation more accessible. If that’s something you’re interested in, you can sign up to follow along.
The underlying model
(6) Limitations of AI before AGI
While AGI is often presented as a future horizon where everything changes, the current reality is more constrained. For the foreseeable future, AI helps ideate, analyze, and create, but it does not meaningfully make judgment calls, navigate trade-offs, or take political responsibility. There is also limited social appetite for delegating those decisions fully to machines.
(5) Why we need inspiration from Bezos
Any framework meant to guide this transition must be resilient to rapid technological change. This echoes Jeff Bezos’s approach to long-term decision-making: focusing less on what will change and more on what will not. In a fast-moving environment, durability comes from anchoring on stable constraints rather than speculative futures.
(7) Differences across verticals
Some domains are closer to greater autonomy than others. In areas like software development, where outcomes are objective and verifiable, AI can act more agentically and learn from its mistakes. In domains where outcomes are subjective, context-dependent, or value-laden—such as writing, design, leadership, or governance—human judgment remains a defining factor.
