True — short-termism is deeply baked into the current system, from equity markets to corporate incentives.
But that’s exactly why we need governance frameworks: markets alone won’t correct for long-term stability. Well-designed institutions can act as the counterweight — whether in finance or in AI policy.
Interesting thought — long-term contracts could indeed align incentives for growth and stability. The challenge, as you note, is trust: few employees or companies are willing to bind themselves for 5–10 years in today’s fluid market.
That’s why governance frameworks (whether in labor or in AI) matter: they provide external guarantees of trust where bilateral promises may not hold.
I hear you — salary compression and inversion, along with short tenure, are very real structural problems. It’s understandable that managers and even directors end up focused only on the next quarter.
My broader point is that when these short-term incentives dominate, organizations (and societies) lose the capacity to build for the long term. That’s exactly why governance frameworks matter: they help create safeguards against purely short-term dynamics — whether in HR policy or in AI policy.
Thank you for pointing this out. The phrase “AI living among us” was not meant as a literal ontological claim, but rather as a social metaphor: how people might perceive and integrate AI into everyday life.
I agree that governance must avoid anthropomorphizing tools. At the same time, in policy discussions metaphors often serve to highlight social risks and expectations.
Your “AAA” framing (Autonomy, Ambition, Access) is an interesting lens — I see value in exploring how licensing frameworks like AIBL could act as safeguards around exactly those dimensions.
This piece argues for an AI Social Contract as a safeguard, with staged licensing and human oversight in “gray zones.”
It suggests that imperfection itself should be a design principle.
Do we need governance frameworks like this — or is existing liability law enough?
I agree that high turnover is a real constraint. That’s why the answer isn’t “10 years of apprenticeship” but designing scaffolds that combine learning with contribution in a shorter timeframe. Things like short rotations, micro-credentials, or mentorship stipends let juniors add value while they’re still on the job. Even if they leave after a few years, the investment isn’t wasted — both sides still capture meaningful returns.
You’re absolutely right that mid-level hires buy immediate productivity. But “growth potential” isn’t just romanticism — it’s an investable trajectory. With the right project design, feedback loops, and domain exposure, juniors can grow into “multipliers” — people who combine technical skills with adaptability or domain expertise. That’s a kind of return you rarely get from simply adding another mid-level hire. In practice, resilient organizations balance both: mid-levels for immediate throughput, and juniors for long-term strength.
It’s true that much of the debate around AI swings between extremes — utopian promises on one side, dystopian collapse on the other. But institutions don’t operate well in extremes.
What matters is how we design governance that acknowledges uncertainty while still enabling progress. In practice, that means imperfect but adaptive frameworks — guardrails that evolve as technology and society evolve.
Instead of asking “which fallacy is right,” we might ask: how do we build systems that remain trustworthy even when our assumptions about AI turn out to be wrong?
I think the real challenge is not whether AI will “replace” people, but how we preserve the spaces where skills are actually practiced and refined.
Entry-level jobs, internships, and junior projects have always been more about learning curves than efficiency. If AI shortcuts those too aggressively, we risk cutting off the very ladder that produces the next generation of capable engineers and creators.
Maybe the question isn’t “Will AI take jobs?” but “How do we redesign pathways so humans still get the training ground they need—while AI handles the repetitive load?”
Free-for-All was a natural assumption in the early internet, but in the age of AI, alignment with contracts and governance becomes essential. Technical capability alone is not enough — without mechanisms like licensing or audits to ensure legitimacy, such practices may prove socially unsustainable.
People don’t really use ChatGPT as a search engine replacement. It’s more about decision support, writing, and formatting tasks. That matches what I see at work: younger colleagues often use it for drafting text or templates, but not for “just looking things up.”
Generative AI may automate some entry-level tasks, but young professionals are not just “replaceable labor.” They bring growth potential, adaptation, and social learning. Without frameworks to manage AI’s role, we risk undermining the very training grounds that prepare the next generation of experts.
We are exploring the idea of AI Behavior Licensing (AIBL) — a framework to license embodied AI behaviors, similar to how we regulate human drivers or medical devices.
The goal is to create an institutional safeguard before embodied AI becomes mainstream.
Question to the community: Should AI be licensed like human professionals, or is existing liability law enough?
But that’s exactly why we need governance frameworks: markets alone won’t correct for long-term stability. Well-designed institutions can act as the counterweight — whether in finance or in AI policy.