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My friends all hate AI; I just joined an AI startup

fast.ai

August 17, 2026

5 min read

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44/100

Summary

The fast.ai co-founder who helped launch the organization with Jeremy Howard in 2016 has joined Answer.AI to work on AI in education, despite widespread concerns about AI-generated content, overhyped corporate claims, resource use, and the erosion of human skills. The writer left AI in 2023 after burnout and completed an MS in Microbiology-Immunology before returning as public opposition to AI intensified. Answer.AI, which grew out of fast.ai, is developing SolveIt, a tool designed to keep people in control of AI-assisted problem-solving. Users can directly edit the system’s responses and decide their next steps. SolveIt takes its name from George Pólya’s 1945 book, How to Solve It, which divides problem-solving into understanding a problem, devising a plan, carrying it out, and reviewing the result. Its design rejects chatbots that immediately produce finished answers, on the grounds that doing the intervening work helps people understand and evaluate solutions. The writer says AI has contributed to AI-generated student work, low-quality open-source pull requests, degraded online writing, and education products focused on gameable metrics. They argue that OpenAI, Anthropic, Google, and xAI do not define AI’s only possible future, and call for tools that preserve human judgment, creativity, autonomy, collaboration, and awareness of resource constraints.

Key Takeaways

A fast.ai co-founder has joined Answer.AI to focus on AI in education after leaving the field in 2023 and earning an MS in Microbiology-Immunology.
Answer.AI’s SolveIt lets users edit AI responses directly and is designed to keep human judgment central to problem-solving.
SolveIt draws on George Pólya’s 1945 four-stage problem-solving framework: understand the problem, devise a plan, carry out the plan, and look back at the result.
The writer argues that AI-generated schoolwork, open-source code submissions, and low-quality online content are already harming education, software communities, and publishing.
The writer says major labs including OpenAI, Anthropic, Google, and xAI are not the only possible model for AI development and use.

What the discussion said

The thread spent less time on the startup career choice than on why AI has become socially radioactive. Several commenters argued that public hostility is rational: AI is being forced into products and workplaces before it reliably improves anyone’s life, while its visible consequences include low-quality generated content, job anxiety, data-center expansion, and automated systems that make consequential decisions harder to challenge. Others pushed back that suspicion of labor-saving technology is hardly new, and that many people are judging AI through mediocre search summaries or mandatory corporate rollouts rather than the strong paid models that can genuinely save skilled workers time. Education became the clearest test case. Commenters broadly agreed that an AI tool cannot call itself successful merely because it finishes schoolwork faster; if it removes the productive struggle through which students learn, efficiency is a loss. Some still saw room for carefully structured access, such as separating AI-assisted practice from unaided work. The larger split was over whether humane design can resist the business incentive to replace workers and flatten creative effort. Skeptics saw augmentation language as a temporary marketing layer over automation, while advocates argued that models can help people develop and implement ideas they otherwise lack time or expertise to pursue.

Where opinion split

The sharpest fight is whether AI is already a broadly useful personal productivity tool or a technology whose costs are being imposed on everyone else. Supporters point to substantial gains in coding, research, accessibility, and making ideas executable; critics answer that these gains are concentrated among people with access to expensive capable models, while errors, deskilling, layoffs, surveillance, and resource costs land on the public.

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Community Sentiment

Negative

Positives

Capable AI assistants can materially accelerate research, drafting, coding, and information retrieval, letting skilled users turn half-formed ideas into finished work far faster.
Used as a bounded tool rather than an oracle, AI can help with practical tasks and creative execution without requiring users to surrender judgment.
Structured AI access in education could preserve unaided practice while still teaching students how to use a tool they will encounter in professional life.

Concerns

AI is being inserted into daily life faster than its benefits can be demonstrated, leaving ordinary people with disruption instead of a convincing improvement.
Education tools face an unusually brutal standard: shortcuts that erase a student's struggle can produce faster assignments and weaker learning.
Consumer AI advice can sound authoritative while inventing details, overcomplicating practical work, or steering inexperienced users into mistakes they cannot independently catch.
The productivity story hides an unequal bargain: well-resourced workers get powerful models, while others face mandates, layoffs, higher infrastructure costs, and no meaningful opt-out.
Human-in-the-loop promises look fragile because the commercial payoff usually comes from removing the human, not preserving autonomy or expertise.