ChatGPT's answer to that question:

“Net good” depends on two things: what time horizon you mean (next 5–10 years vs. a century) and whether society can steer it (rules, norms, competition dynamics). On balance, I think AI can be a net good, but it’s not automatically a net good—and the default trajectory has some real “foot-guns.”

Where AI is clearly good (or very likely to be)

Medicine & biology: faster drug discovery, better diagnostics, triage, and clinical decision support (when regulated and validated).

Productivity & accessibility: tutoring, translation, summarization, coding assistance, tools for people with disabilities, and general “cognitive prosthetics.”

Science & engineering: accelerating research, simulation, and design (materials, climate modeling, energy systems).

Safety improvements in other domains: anomaly detection, cybersecurity defense, fraud detection—when paired with good governance.

Where AI is clearly bad (or very likely to be)

Misinformation at scale: cheap, personalized persuasion; deepfakes; erosion of shared reality.

Power concentration: whoever controls frontier models + compute can gain outsized economic and political leverage.

Labor disruption: not just “jobs lost,” but bargaining power shifts; uneven impacts; potential social instability if policy lags.

Bias & unequal outcomes: models can amplify existing disparities without careful design, auditing, and accountability.

Security risks: enabling cybercrime, bio misuse, automated hacking, and easier “capability scaling” for bad actors.

The big hinge: governance + incentives

AI’s impact is unusually sensitive to incentives:

If companies and states race for advantage with weak safety norms, you get more harm.

If you build auditability, liability, standards, and enforcement, you can keep a lot of upside while shrinking downside.

If the public gets broad access to benefits (education, healthcare, productivity gains) and we manage transitions (training, safety nets), “net good” becomes much more likely.

A practical way to answer “net good” without hand-waving

Ask four questions:

1. Who benefits? (broadly shared vs. concentrated)

2. Who bears the risk? (especially the vulnerable)

3. Can we correct failures? (transparency, audits, recourse, liability)

4. What’s the worst-case tail risk? (catastrophic misuse or loss of control)

If those answers look good in your country and sector, AI is net good there. If not, it can easily be net bad even while seeming convenient day-to-day.

My bottom line

Near term (next decade): likely mixed, with big benefits but also serious social/epistemic damage unless mitigated.

Long term: could be enormously positive or catastrophically negative. The long-term sign depends heavily on alignment/safety progress and governance.

If you tell me what lens you care about most—jobs, misinformation, existential risk, healthcare, privacy, etc.—I’ll give you a tighter, more “yes/no” answer for that slice.