Techno-optimism.

Argues AI is the largest enterprise software shift since the cloud; warns that companies that delay AI adoption will be structurally outcompeted.

Forecasts embodied AI and humanoid robots as a multi-trillion-dollar market by mid-century.

Argues programmer-AI partnership rather than full automation is the right design point; building tools that respect programmer agency while massively augmenting capabilities.

Argues digital technology is delivering compounding productivity gains and that AI is the latest, most powerful chapter; rejects techno-pessimist framings of stagnation.

Argues AGI by 2029 is plausible and expects a compassionate rather than hostile superintelligence.

Frames AI as the most important technology advance since the PC and frames development as inevitable and net-positive.

Argues energy infrastructure is the governing variable for AI progress and should be the primary policy lever.

Argues humanoid robots powered by frontier AI will solve labor shortages and physical-world deployment of AI; sees this as the next major economic transition.

Argues AI is the partner that helps humanity engineer its own biology; future flourishing depends on submitting human bodies to algorithmic optimization.

Argues AI will produce extreme personal-productivity gains for a small number of power users, transforming economics.

Bullish on AI-augmented developer experience but raises concerns about over-dependence on AI tooling.

Argues generative AI will let any user create immersive digital experiences; predicts a future where AI tools collapse the gap between consumer and creator.

Argues image-generation AI extends human creativity rather than replacing artists; frames Midjourney's mission as democratizing visual imagination.

Pro-deployment European voice; pushes back on regulation-first framings.

Argues medicine is the highest-impact AI application area; AI can restore the patient-doctor relationship by automating bureaucratic and pattern-recognition work that currently consumes clinicians' time.

Argues enterprise AI deployment is harder than the hype suggests; data infrastructure and governance work, not algorithm choice, are the binding constraints.

Argues humans + machines beat machines alone (centaur chess), that augmentation is the right framing, and that authoritarian AI capture is a more urgent risk than runaway misalignment.

Argues open-weight, distributed AI is the right structural answer to safety concerns; opposes both pause framings and centralised licensing regimes.

Publicly optimistic about AI-mediated creativity; released her voice for open AI cloning as an experiment in post-scarcity IP.

Argues inference cost trajectories will fundamentally reshape AI deployment; tracks the rapid drop in cost per token as the operative metric.

Argues AI fits the pattern of general-purpose technologies that historically produced large but slow productivity gains; expects it to be adopted unevenly across industries based on integration cost.

Argued the future of computing would be a symbiotic partnership in which humans and machines do together what neither can do alone, the founding vision of human-AI augmentation.

Publicly optimistic about AI's trajectory and downplays existential framings; frames NVIDIA's role as enabling everyone to build AI.

Argues AI can democratize access to legal services that are otherwise out of reach for ordinary consumers; pushed against bar-association resistance to AI legal advice.

Argues AI infrastructure, models, data, distribution, is the most consequential investment category of the decade; frames safety as integral to durable products rather than a regulatory constraint.

Publicly bullish on compute scaling as the core driver of AI progress; focuses on supply-side bottleneck framings.

Argues markets and technology are the primary engine of welfare.

Argues AI is the most consequential business technology since the internet; warns founders that not adopting AI now is a fatal strategic error.

Among the largest financial backers of the AI build-out. Predicts superintelligence by 2035 and treats this as primary investment thesis.

Argues innovation is a recombinant, decentralised process and that attempts to centrally manage AI development will misjudge how progress actually happens.

Argues frontier AI will produce abundance and that the frontier should be built broadly, with many startups, not concentrated; funds early-stage AI research via AI Grant.

Argues AI gives individuals unprecedented leverage; opposes both pause framings and centralised licensing as restrictions on individual technological agency.

Co-author of scaling laws; focused on capability progress inside OpenAI.

Publicly focuses on AI-for-science applications; engaged on safety but less on the dramatic framings.

Long-running optimistic-singularity advocate. Predicts AGI by 2029 and human-machine merger by 2045.

Frames AI as the most important human amplification technology; argues for fast deployment with safety.

Argues that automating professional work is overdue and largely good, but that the legal and ethical infrastructure for it is not keeping pace.

Argues AI lets a single founder operate at the scale of an early-stage team; runs Gumroad as a deliberate experiment in this thesis with very small headcount.

Argues AI tutoring can deliver one-on-one expert teaching to every student, Bloom's 2-sigma effect at universal scale, and that this is the most important application of LLMs.

Translates AI engineering progress for technical audiences. Bullish on the AI engineering profession as a distinct field.

Builds enterprise agent products; publicly less engaged with x-risk framing, more with practical deployment.

Bullish on AI as scientific transformation; argues integrating symbolic and statistical AI is the productive frontier.

Argues developer tools that put AI inline rather than in a separate chat window are the natural endpoint of how AI integrates into engineering work.

Argues AI fluency is now as foundational as basic literacy for knowledge workers; mandated AI use across Shopify on the basis that headcount growth would not exceed AI-leveraged productivity.

Frames AI as likely to lower net existential risk; skeptical of specific high p(doom) numbers.

Anticipated that mechanical aids to human thought, what he called the 'Memex', could free us from drudgery and let us think at higher levels; this is one of the foundational visions of human-computer cooperation.

Argues AI will be the largest source of productivity, healthcare, and education gains in human history; opposes pause-style framings as distortions of opportunity.

Argues AI may be the productivity answer to demographic decline; particularly important for ageing economies.

Continuing OpenAI board member; bullish on AI deployment but on record about the November 2023 board's safety concerns. The quote below is actually about safety practice, the techno-optimism inference is loose.

Inside-OpenAI public voice for capability optimism on scaling and reasoning models.

Bullish on AI-coding and agent-driven software development; arguments for accelerationist framing.

Focuses on education and on democratising access to LLMs; publicly less engaged with extinction-risk framings than some peers.

Frames AI as the infrastructure for a new generation of knowledge discovery tools; less focused on risk framings.

Frames AI through tech-business strategy lens; predicts the AI value chain will look like the cloud or app-store ecosystem.

Bullish on AI as economic transformation, with rigorous attention to capital flows and labour-market mechanisms.

Argues AI will produce trillion-dollar companies and decisively shift the U.S.-China balance; less explicit on safety strategy but consistently bullish on capabilities.

Bullish on AI as a scientific and medical transformation; less focused on x-risk framings.

Argues AI-for-biology will transform agriculture, medicine, and food systems within a decade; less explicit on safety strategy but consistently bullish on capabilities.

Frames AI and biotech as instruments of radical welfare expansion, especially suffering abolition, rather than sources of catastrophic risk.

Frames AI as a productivity layer for knowledge work; building it into Dropbox as a universal search overlay.

Frames AI as a 'jagged frontier' of capability that requires hands-on engagement to understand. Skeptical of both pure optimism and pure doom.

European ML voice committed to demystifying deep learning; sceptical of both extreme optimism and extreme pessimism.

Drives AI startup acceleration at YC; frames AI as the new 'full-stack' opportunity.

Working mathematician engaging with AlphaProof and similar systems; argues mathematicians need to engage with AI rather than dismiss or hype it.

Publicly optimistic about AI as a transformative technology; large-scale investment via Amazon's Anthropic stake.

Bullish on AI as a productivity unlock for global business, particularly small business via Stripe.

Long-time techno-optimist who frames AI as evolutionary continuation; argues utility outweighs risk if we focus on co-evolution. Quote below is generic hype, Kelly's actual stated views need a stronger source pull before being treated as primary-source.

Argues recommendation AI built around long-term user interest is possible but commercially difficult; Artifact's failure was a market signal more than a technical one.

Major investor in Stargate. Public framings position AI as transformative civilisational infrastructure that the US must build at scale. Quote is press-event hype, not a stated strategic position.

Long-standing AI optimist; backed Google's early DeepMind acquisition. Reportedly skeptical of AI doom framings.

Mainstream economist on the OpenAI board. Public voice for measured macro-economic framings of AI. Quote below is on macroeconomic effect, not on a strategic optimism vs caution stance.

Architect of Singapore's pro-AI national strategy combining innovation, talent attraction, and pragmatic governance. The quote below is government-PR boilerplate, not a personal stated strategy.

Inside OpenAI voice for pushing capability alongside safety practices.

Publishes the State of AI Report annually, tracking trends in capability, deployment, and policy.

Argues AI is likely to be net positive for the economy and that 'AI takes all jobs' framings misunderstand labour-market dynamics.

Builder-side voice. Argues frontier model improvement is structurally driven by aggressive engineering more than by safety theory.

Frames AI as the kind of progress acceleration the Progress Studies movement was built around.

Argues AI is the most important technology wave of the generation and should be built quickly by startups.

Critical of stagnation but wary of AI-induced concentration; advocates for national security-framed AI development.

Frames AI robotics as a major productivity and quality-of-life unlock; less focused on extinction framings.

Argues AI for creative work is best designed as collaborative tooling, not replacement.

Frames AI as a continuation of evolutionary processes; less alarmed about extinction, more concerned about value drift.

Long-running advocate for Chinese AI capability. Has positioned Baidu's ERNIE as a Chinese frontier model. The quote below is corporate keynote hype; no primary-source strategic position is on the record.

Bullish on AI as a generational investment cycle; publicly notes the gap between AI investment and AI revenue.

Frames AI as a powerful general-purpose tool whose actual deployment will be more economically disruptive than philosophically shocking.

Publicly bullish on frontier model progress; rejects the 'capability wall' framing.

Frames AI in business and futurology terms; advises corporate clients on AI strategy.

Industry ML research leader engaged with deployment-grade AI rather than safety theory.

Frames AI as a universal productivity layer; partners with frontier labs to ship products quickly.

Frames AI as a highly positive force when deployed well; skeptical of catastrophe framings.

Returned hands-on to Google after the AI moment; publicly excited about scaling and capability progress.

VC-grade optimism on AI's transformative deployment.

Industry-side voice for enterprise AI deployment.

Argues AI is a major positive lever for global health if directed at deployment-relevant problems and not just frontier capabilities.

Argues foundation models will reshape robotics; embodied AI is a near-term frontier rather than far-future.

Focuses on training-efficient frontier research; pragmatic about near-term progress trajectory.