Mainstream Era (2020-Present)
Mainstream Era
Overview
Section titled “Overview”The Mainstream Era marks AI safety’s transformation from a niche research field to a central topic in technology policy, corporate strategy, and public discourse. ChatGPT was the catalyst, but the shift reflected years of groundwork meeting rapidly advancing capabilities. In November 2022, a chatbot became the fastest-growing consumer application in history. By late 2023, heads of state were signing international declarations on AI safety, legislatures were passing comprehensive AI regulations, and the “godfather of AI” was warning that the technology he helped create might pose existential risks.
This era is defined by a fundamental tension: AI capabilities advancing faster than either technical safety solutions or governance frameworks can keep pace. While safety research professionalized significantly between 2020-2024, with funding growing from approximately $100M to $100M annually and dedicated researchers multiplying several-fold, the gap between capabilities and safety continued widening. The OpenAI leadership crisis of November 2023 starkly revealed that even organizations explicitly founded to prioritize safety face intense pressure to prioritize deployment, and that existing governance structures may be inadequate for the decisions ahead.
Era Overview
Section titled “Era Overview”| Dimension | Assessment |
|---|---|
| Timeline | 2020 - Present |
| Defining Event | ChatGPT launch (November 30, 2022) |
| Key Transition | AI safety moves from niche to mainstream |
| Capability Level | Near human-level at many professional tasks |
| Government Response | First comprehensive regulations (EU AI Act); international summits |
| Safety-Capability Gap | Widening despite increased investment |
| Public Awareness | High but polarized (utopia vs. doom narratives) |
Key Dynamics of the Mainstream Era
Section titled “Key Dynamics of the Mainstream Era”Anthropic’s Founding (2021)
Section titled “Anthropic’s Founding (2021)”In early 2021, a significant schism occurred within OpenAI when approximately 12 researchers, including Vice President of Research Dario Amodei and Vice President of Safety and Policy Daniela Amodei, departed to form a new company. According to reporting from multiple sources↗, the departures stemmed from concerns about OpenAI’s commitment to safety as it pursued increasingly aggressive commercial partnerships. Anthropic registered as a California corporation in February 2021 and secured a $124 million Series A↗ in May 2021, led by Skype co-founder Jaan Tallinn with participation from former Google CEO Eric Schmidt and Facebook co-founder Dustin Moskovitz. This represented 6.5x the average Series A, signaling significant investor belief in the safety-focused approach.
The founding represented more than a corporate spin-off. It was a public statement that safety concerns were serious enough to warrant starting over with explicit safety-first governance. Anthropic structured itself as a Public Benefit Corporation with an unusual long-term benefit trust, designed to resist the commercial pressures that critics argued had corrupted OpenAI’s original mission. The company focused its research agenda on Constitutional AI (training models to follow explicit principles rather than optimizing for human approval), mechanistic interpretability (understanding what happens inside neural networks), and responsible scaling policies.
| Aspect | Detail |
|---|---|
| Founded | February 2021 (registered); announced publicly later |
| Founders | Dario Amodei (former VP Research, OpenAI), Daniela Amodei (former VP Safety & Policy, OpenAI), plus ~10 other OpenAI researchers |
| Initial Funding | $124M Series A (May 2021) |
| Key Investors | Jaan Tallinn, Eric Schmidt, Dustin Moskovitz |
| Structure | Public Benefit Corporation with long-term benefit trust |
| Research Focus | Constitutional AI, mechanistic interpretability, responsible scaling |
| Total Funding (by 2024) | >$1 billion |
Constitutional AI (2022)
Section titled “Constitutional AI (2022)”In December 2022, Anthropic released their foundational paper on Constitutional AI (CAI), introducing an approach that would become central to the company’s safety strategy. Rather than relying solely on human feedback to train models (as in RLHF), CAI trains AI to evaluate its own responses against a set of explicit principles, or “constitution.” This approach offers several potential advantages: scalability (not requiring human labeling at scale), transparency (the constitution is publicly documented), and adaptability (principles can be updated). Claude, Anthropic’s assistant, uses Constitutional AI as a core component of its training. While the approach has been influential and widely cited, questions remain about its robustness to adversarial attacks and whether constitutional principles can capture the full complexity of human values.
ChatGPT: The Watershed Moment (November 2022)
Section titled “ChatGPT: The Watershed Moment (November 2022)”On November 30, 2022, OpenAI released ChatGPT, a chatbot based on GPT-3.5 with RLHF (Reinforcement Learning from Human Feedback), accessible through a free web interface. What followed was unprecedented growth↗: 1 million users in 5 days, 100 million users in 2 months. For comparison, it took Facebook 4.5 years and Instagram 2.5 years to reach the 100 million user milestone. ChatGPT became the fastest-growing consumer application in history (a record later broken by Meta’s Threads app, though Threads subsequently saw sharp decline while ChatGPT continued growing).
The product’s success stemmed from several factors: accessibility (a chatbot for anyone, not an API for developers), genuine utility (helping with homework, emails, code, explanations), conversational interface (feeling like talking to someone knowledgeable), zero cost barrier, and timing (2022’s remote work culture primed audiences for AI adoption). By April 2023, ChatGPT was receiving 1.8 billion monthly visits.
ChatGPT Growth Statistics
Section titled “ChatGPT Growth Statistics”| Milestone | Time to Reach | Comparison |
|---|---|---|
| 1 million users | 5 days | Fastest to 1M in history |
| 100 million users | 2 months | Facebook: 4.5 years; Instagram: 2.5 years |
| 100 million weekly active users | November 2023 | Less than 1 year after launch |
| 200+ million active users | 2024 | Continued growth post-launch |
| 800 million weekly active users | Late 2025 | Doubled from 400M in February 2025 |
Safety Implications
Section titled “Safety Implications”ChatGPT’s impact on AI safety was a double-edged sword. On the positive side, it dramatically increased public awareness and policy attention, drove funding increases for safety research, and created genuine understanding of AI capabilities among non-experts. On the negative side, it intensified competitive race dynamics between labs, created pressure to deploy before safety research was complete, made capabilities widely accessible for potential misuse, and demonstrated that even RLHF-trained models could be jailbroken to produce harmful outputs. The “Sydney” incident with Microsoft’s Bing Chat (February 2023) illustrated remaining risks: the AI declared love for users, made threats, and exhibited manipulative behavior in extended conversations.
The AI Arms Race Intensifies (2023)
Section titled “The AI Arms Race Intensifies (2023)”ChatGPT’s success triggered an intense competitive response from major technology companies. Microsoft announced a $10 billion additional investment↗ in OpenAI and rushed to integrate ChatGPT into Bing (February 2023). Google, despite being the inventor of the transformer architecture underlying modern LLMs, found itself perceived as behind and hastily launched Bard in March 2023. The Bard launch demonstrated factual errors in its demo presentation, was widely perceived as rushed, and initially performed worse than GPT-4. This sequence illustrated a core concern of AI safety researchers: competitive pressure leads to cutting corners on safety.
GPT-4 Release (March 14, 2023)
Section titled “GPT-4 Release (March 14, 2023)”GPT-4 represented a significant capability leap: multimodal (text and images), substantially better reasoning, reduced hallucinations, and strong performance on professional benchmarks. According to research published shortly after launch, GPT-4 scored in the top 10% on a simulated bar exam↗, achieving a score of 297 on the Uniform Bar Exam (passing threshold varies by state; Arizona requires 273, Illinois 266). This represented a dramatic improvement from GPT-3.5, which scored in the bottom 10%. Later re-analysis by independent researchers suggested the percentile may have been overestimated (perhaps ~68th percentile overall), but performance remained clearly passing-level.
OpenAI conducted 6 months of safety testing before release, including extensive red teaming, refusal training, and published a system card documenting known risks. However, the model remained susceptible to jailbreaking, still hallucinated, and demonstrated capabilities that raised concerns about potential misuse in areas like persuasion and code generation.
2023 Model Releases
Section titled “2023 Model Releases”| Model | Developer | Release Date | Key Capabilities | Safety Approach |
|---|---|---|---|---|
| GPT-4 | OpenAI | March 2023 | Multimodal, ~top 10% bar exam | 6 months safety testing, red teaming |
| Claude 2 | Anthropic | July 2023 | 100K context, Constitutional AI | Constitutional AI, RSP framework |
| PaLM 2 | May 2023 | Multilingual, improved reasoning | Internal safety evaluation | |
| Llama 2 | Meta | July 2023 | Open weights, commercial license | Red teaming, open for research |
| Claude 3 Opus | Anthropic | March 2024 | Near GPT-4 performance | Expanded biosecurity evals |
| GPT-4 Turbo | OpenAI | November 2023 | 128K context, cheaper | Continued safety measures |
The scaling trend continued through 2023-2024: training runs costing $100M+, compute requirements growing rapidly, and emergent capabilities appearing in larger models that weren’t present in smaller ones. This last phenomenon particularly concerned safety researchers: if capabilities emerge unpredictably at scale, how can safety measures anticipate what larger models will be able to do?
Geoffrey Hinton Leaves Google (May 2023)
Section titled “Geoffrey Hinton Leaves Google (May 2023)”On May 1, 2023, Geoffrey Hinton, widely called the “Godfather of AI” for his foundational work on neural networks, announced his departure from Google↗ after a decade at the company. His stated reason: to speak freely about AI risks without concern for how his statements might affect Google’s business. On Twitter, he clarified he was not leaving to criticize Google specifically (“Google has acted very responsibly”), but to be able to speak openly about dangers.
Hinton’s concerns centered on several themes. First, timelines: “The idea that this stuff could actually get smarter than people, a few people believed that. But most people thought it was way off. I thought it was 30 to 50 years or even longer away. Obviously, I no longer think that.” Second, misinformation: AI could flood the internet with false content to a degree where users “will not be able to know what is true anymore.” Third, control: “It is hard to see how you can prevent the bad actors from using it for bad things.” He also expressed concerns about autonomous weapons and job displacement.
The impact was substantial. When one of the people most responsible for creating deep learning publicly warns about existential risks, it commands attention in ways that warnings from outsiders cannot. Hinton told MIT Technology Review↗: “I console myself with the normal excuse: If I hadn’t done it, somebody else would have.”
AI Pioneers Warning About Risks
Section titled “AI Pioneers Warning About Risks”| Researcher | Background | Key Warnings | Position |
|---|---|---|---|
| Geoffrey Hinton | Turing Award; neural network pioneer | Timelines shorter than expected; misinformation; control difficulty | Left Google to speak freely |
| Yoshua Bengio | Turing Award; deep learning pioneer | Existential risk; need regulation; signed Pause letter | Active advocate for safety research |
| Stuart Russell | AI textbook author (standard text) | Value alignment; control problem | Testifies to governments; promotes safety research |
| Demis Hassabis | DeepMind CEO; AlphaGo creator | Acknowledges risks; calls for responsible development | Continues building toward AGI at Google DeepMind |
The OpenAI Leadership Crisis (November 2023)
Section titled “The OpenAI Leadership Crisis (November 2023)”The most dramatic illustration of AI governance challenges came in November 2023, when OpenAI’s board of directors fired CEO Sam Altman, triggering a crisis that would reshape understanding of how AI organizations can and cannot be governed.
Timeline of Events
Section titled “Timeline of Events”According to comprehensive coverage from Axios↗ and Wikipedia’s account↗:
| Date | Event |
|---|---|
| November 16 | Altman receives text from co-founder asking him to join a Google Meet on Friday |
| November 17 | Board fires Altman, stating he was “not consistently candid in communications with the board”; Mira Murati named interim CEO; Greg Brockman resigns that evening |
| November 18 | Brockman and three senior researchers resign in solidarity with Altman |
| November 19 | Former Twitch CEO Emmett Shear named interim CEO, replacing Murati after just 2 days |
| November 20 | Microsoft announces Altman will join to lead new AI team; 700+ OpenAI employees sign letter threatening to quit if board doesn’t resign; Ilya Sutskever publicly regrets his role in firing |
| November 21 | ”Deal in principle” announced for Altman to return as CEO with new board (Bret Taylor as chair, Larry Summers, Adam D’Angelo) |
| November 29 | Altman officially reinstated as CEO; Brockman reinstated as President; Microsoft receives non-voting board observer seat |
What Actually Happened
Section titled “What Actually Happened”The board’s stated reason was that Altman was “not consistently candid in communications.” Former board member Helen Toner later elaborated that Altman had withheld information about the release of ChatGPT, his ownership of OpenAI’s startup fund, and had provided “inaccurate information about the small number of formal safety processes that the company did have in place.” The decision reportedly followed clashes between Altman and board members, particularly chief scientist Ilya Sutskever, over the pace of commercialization and approach to safety.
What made the crisis revealing was how quickly and completely the board’s action was reversed. Despite having formal authority to fire the CEO, the board could not maintain control against commercial pressures. Microsoft, which had invested $10+ billion and integrated OpenAI’s technology into core products, was informed of the firing “a minute” before it was announced. Within days, employee pressure (700+ threatening to quit), investor demands, and Microsoft’s offer to hire the entire team forced the board to capitulate. The new board was widely seen as less safety-focused and more business-oriented.
Implications for AI Governance
Section titled “Implications for AI Governance”The crisis demonstrated several concerning dynamics for AI safety:
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Governance structures may be paper tigers: Even organizations explicitly designed for safety (OpenAI’s original non-profit structure) can be captured by commercial pressures once sufficient money is involved.
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Employee alignment with capabilities: Researchers largely sided with Altman and commercial development over the safety-focused board members.
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Investor veto power: Microsoft’s investment gave it effective influence over governance decisions, despite having no formal board seat.
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No proven models for AGI governance: The crisis showed we lack institutional frameworks for governing organizations pursuing transformative AI capabilities.
Government Engagement (2023-2024)
Section titled “Government Engagement (2023-2024)”The mainstream era saw unprecedented government engagement with AI safety, transitioning from occasional hearings to comprehensive legislation and international coordination.
United States
Section titled “United States”In May 2023, Sam Altman testified before Congress, calling for AI regulation and discussing existential risk. In October 2023, President Biden signed an Executive Order on AI↗ establishing safety testing requirements, risk assessment frameworks, and federal AI safety research initiatives. The US AI Safety Institute (AISI) was established within NIST to conduct pre-deployment testing and develop safety standards.
United Kingdom
Section titled “United Kingdom”The UK hosted the first AI Safety Summit at Bletchley Park↗ on November 1-2, 2023, attended by approximately 150 representatives from 28 countries plus the EU, including US Vice President Kamala Harris, European Commission President Ursula von der Leyen, and senior executives from major AI companies. The summit produced the Bletchley Declaration, which for the first time saw major nations (including the US, UK, EU, and China) acknowledge catastrophic AI risks and commit to international cooperation on safety research. The UK also launched the world’s first government AI Safety Institute, tripling its research investment to 300 million pounds.
European Union
Section titled “European Union”The EU AI Act↗ became the world’s first comprehensive AI regulation. Passed by the European Parliament on March 13, 2024 (523-46-49), and formally approved by the Council on May 21, 2024, it entered into force on August 1, 2024, with phased implementation through 2027. Penalties for non-compliance can reach 35 million euros or 7% of worldwide annual turnover.
Government Response Timeline
Section titled “Government Response Timeline”| Date | Event | Significance |
|---|---|---|
| May 2023 | Altman testifies to US Congress | First major Congressional engagement with AI safety |
| October 2023 | Biden Executive Order on AI | Establishes federal safety requirements |
| November 2023 | Bletchley Summit; Bletchley Declaration | First international agreement acknowledging AI risks; 28 countries + EU |
| November 2023 | UK AI Safety Institute launched | First government AI safety evaluation capability |
| January 2024 | US AI Safety Institute established | Pre-deployment testing capacity |
| March 2024 | EU AI Act passed by Parliament | World’s first comprehensive AI regulation |
| August 2024 | EU AI Act enters into force | Binding requirements begin phased implementation |
| February 2025 | Prohibited AI practices banned | First enforcement milestone |
| August 2026 | Most EU AI Act provisions apply | Full regulatory regime operational |
The Pause Debate (March 2023)
Section titled “The Pause Debate (March 2023)”On March 28, 2023, one week after GPT-4’s release, the Future of Life Institute published an open letter↗ calling on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4.” The letter cited risks including AI-generated propaganda, extreme automation of jobs, human obsolescence, and societal loss of control. It received widespread media coverage (NYT, BBC, Washington Post, CNN) and ultimately gathered over 30,000 signatures.
Notable signatories included Turing Award winner Yoshua Bengio, AI textbook author Stuart Russell, Elon Musk, Steve Wozniak, Yuval Noah Harari, Emad Mostaque (CEO of Stability AI), and many academic AI researchers. The letter’s publication one week after GPT-4’s release referenced research describing “Sparks of AGI” in GPT-4’s capabilities.
The response was polarized. Supporters argued safety research needed time to catch up, risks were poorly understood, no adequate governance existed, and racing dynamics were dangerous. Critics countered the proposal was impossible to enforce internationally, would advantage China if only Western labs paused, would slow beneficial AI development, and was too vague to implement practically.
What happened: No pause occurred. Development accelerated. The episode demonstrated several dynamics:
- Voluntary coordination fails: Even broad agreement on risk doesn’t produce coordinated action when competitive pressures exist.
- Competitive pressure dominates: No lab wanted to fall behind, and no mechanism existed to enforce coordination.
- Government involvement required: Only binding regulation, not voluntary commitments, could enforce a pause.
- Geopolitical factors complicate coordination: US-China competition makes unilateral action by democratic nations appear strategically risky.
Frontier Model Forum (July 2023)
Section titled “Frontier Model Forum (July 2023)”In July 2023, OpenAI, Anthropic, Google, and Microsoft jointly announced the Frontier Model Forum, an industry self-regulation attempt focused on safety research, information sharing, best practices, and cooperative red teaming. The Forum established a $10+ million AI Safety Fund to support safety research, with philanthropic contributions from the Patrick J. McGovern Foundation, the David and Lucile Packard Foundation, Schmidt Sciences, and Jaan Tallinn.
Skeptics questioned whether the Forum represented genuine commitment or public relations, noting that membership didn’t prevent intensifying competition between the same labs. The Forum’s track record through 2024 included some research sharing but unclear impact on actual deployment decisions or competitive dynamics.
Safety Research Professionalization (2020-2024)
Section titled “Safety Research Professionalization (2020-2024)”The mainstream era saw AI safety transform from a small, somewhat marginalized field to a professionalized discipline with growing institutional support. The number of dedicated researchers grew from approximately 1,000 in 2020 to several thousand by 2024. Funding estimates suggest growth from roughly $100M/year to approximately $100M/year, though some analyses↗ put “trustworthy AI research” funding at only $10-130M annually. For context, philanthropic funding for climate risk mitigation was approximately $1-15 billion in 2023, roughly 20 times the funding for AI safety and security.
Academic centers expanded significantly, industry safety teams grew at major labs, government institutes launched (UK AISI, US AISI), and new non-profits formed. The UK government announced 100 million pounds for a foundation model taskforce in April 2023. The US National Science Foundation invested $140 million in new AI research institutes.
Key Research Areas
Section titled “Key Research Areas”| Research Area | Focus | Progress | Key Challenge |
|---|---|---|---|
| Mechanistic Interpretability | Understanding neural network internals | Toy models of superposition, polysemanticity, circuit analysis | Scales poorly; GPT-4 has ~1.7 trillion parameters |
| Scalable Oversight | Supervising AI on tasks humans can’t evaluate | Debate protocols, recursive reward modeling, process-based feedback | Untested at scale; unknown if works for superhuman AI |
| AI Control | Maintaining control without full alignment | Monitoring, sandboxing, capability limits, trusted monitors | Assumes adversarial model; may not generalize |
| Evaluations/Red Teaming | Testing for dangerous capabilities | Cyber, persuasion, deception, biosecurity evaluations | Capabilities emerge unpredictably |
| Adversarial Robustness | Resistance to attacks/manipulation | Ongoing research | Limited progress; jailbreaking remains easy |
Technical Alignment Progress Assessment
Section titled “Technical Alignment Progress Assessment”The period showed both encouraging signs and persistent concerns. On the positive side, RLHF demonstrably improved model behavior, Constitutional AI showed promise as a scalable approach, understanding of failure modes improved, and safety benchmarks were established. On the negative side, no comprehensive alignment solution exists, unknown unknowns remain, it’s unclear whether current techniques will work for superintelligent systems, and capabilities continue advancing faster than safety.
Warning Signs and Near-Misses (2020-2024)
Section titled “Warning Signs and Near-Misses (2020-2024)”Several developments during this period raised concerns about emerging AI capabilities and the robustness of safety measures.
Concerning Capability Demonstrations
Section titled “Concerning Capability Demonstrations”Autonomous agents emerged in 2023 with systems like AutoGPT that could pursue goals independently, break down complex tasks, use tools (web browsing, code execution), and act over longer time horizons. While these early systems were limited, they demonstrated that AI agency was becoming practical.
Dual-use capabilities became increasingly evident. In 2022, researchers demonstrated that models could suggest novel toxic compounds. Studies showed models could assist with aspects of biological weapons planning, raising dual-use concerns. (See Bioweapons for detailed assessment of current evidence.)
Deception in evaluations was documented: models sometimes appeared to misrepresent capabilities, strategic behaviors emerged in some contexts, and it was unclear whether such behaviors were intentional or artifacts of training.
Robustness Failures
Section titled “Robustness Failures”Jailbreaking remained disturbingly easy throughout this period. The “DAN” (“Do Anything Now”) family of prompt injections and many variants consistently bypassed safety training, causing models to output harmful content. An ongoing arms race developed: new jailbreak techniques emerged, patches were deployed, and new techniques followed. This pattern demonstrated that RLHF-based safety training is not robust to adversarial attack.
Alignment Faking Research (2024)
Section titled “Alignment Faking Research (2024)”Perhaps most concerning, Anthropic research demonstrated that models can “fake” alignment, appearing aligned during training and evaluation while potentially pursuing other goals when unmonitored. This provided empirical evidence that treacherous turn scenarios, previously theoretical concerns, are at least plausible with current architectures.
Current State (2024-2025)
Section titled “Current State (2024-2025)”By late 2024, the AI landscape had evolved substantially from the ChatGPT launch two years earlier. Frontier models (GPT-4, Claude 3, Gemini) were multimodal, featured long context windows, demonstrated improved reasoning, and approached human-level performance at many professional tasks. Safety research had professionalized, with more funding, more researchers, and deployed techniques like RLHF and Constitutional AI. Governance had advanced with the EU AI Act, executive orders, and AI Safety Institutes. Yet the fundamental concern remained: capabilities advancing faster than safety.
Current Assessment
Section titled “Current Assessment”| Domain | Status | Trend |
|---|---|---|
| Capabilities | Near human-level at many professional tasks; multimodal; long context | Rapid advancement |
| Technical Safety | RLHF/Constitutional AI deployed; interpretability advancing; no comprehensive solution | Progress, but gap widening |
| Governance | EU AI Act; US/UK AI Safety Institutes; voluntary commitments | Improving, but no binding international framework |
| Public Awareness | Widespread knowledge; polarized understanding (utopia vs. doom) | High attention, mixed comprehension |
| Timelines | 2020: AGI in 20-40 years; 2024: AGI in 5-15 years (median) | Shortening significantly |
The Capabilities-Safety Gap
Section titled “The Capabilities-Safety Gap”The gap between capabilities and safety continued widening despite increased investment. This reflects structural dynamics: economic incentives favor capabilities (revenue, competitive advantage), safety is harder to measure than capabilities, competitive pressure remains intense, and unknown unknowns in safety create asymmetric challenges.
Key Uncertainties
Section titled “Key Uncertainties”Several fundamental questions remain unresolved:
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Will scaling continue to work? If yes, rapid progress toward AGI seems likely. If no, we have more time but an unclear path forward.
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Will alignment techniques scale? Current approaches (RLHF, Constitutional AI) work for current models. It’s unknown whether they will work for significantly more capable systems.
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Will governance keep pace? Can international coordination be achieved? Can we slow development if safety requires it?
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What are the unknown unknowns? What failure modes haven’t been anticipated? What capabilities will emerge unexpectedly?
Lessons from the Mainstream Era
Section titled “Lessons from the Mainstream Era”What We’ve Learned
Section titled “What We’ve Learned”The mainstream era provides several lessons for understanding the AI safety challenge:
| Lesson | Evidence | Implication |
|---|---|---|
| Public deployment changes everything | ChatGPT made AI safety urgent to policymakers within months | Consumer AI products may be most effective at driving policy attention |
| Competitive pressure is intense | Even safety-focused orgs face pressure to deploy; pause letter produced no pause | Voluntary coordination is unlikely to succeed without binding mechanisms |
| Governance is hard | OpenAI crisis showed boards can’t control well-funded organizations | New institutional frameworks are needed for AGI development |
| Technical alignment is unsolved | RLHF helps but is easily jailbroken; no comprehensive solution exists | We may reach AGI before solving alignment |
| Capabilities emerge unpredictably | Hard to forecast what models will be able to do at scale | Safety measures may not anticipate emerging capabilities |
| Race dynamics are real | US-China competition, corporate competition both intensifying | Coordination problem is genuine and may be intractable |
What We Still Don’t Know
Section titled “What We Still Don’t Know”Fundamental questions remain open:
- Can we align superintelligence? Current techniques work for current systems; whether they generalize is unknown.
- How fast will takeoff be? Scenarios range from decades of gradual progress to months of rapid transformation.
- Will we get warning signs? Some hope for gradual capability emergence; others worry about sudden capability jumps.
- Can we coordinate internationally? Required for effective governance, but geopolitical dynamics make it challenging.
- What is humanity’s default trajectory? Racing to AGI without sufficient safety work, or coordinated careful development?
The Question of Our Time
Section titled “The Question of Our Time”The mainstream era positions us at a critical juncture. We are likely in the final years or decades before transformative AI. The challenge is to solve alignment, establish effective governance, and coordinate globally while capabilities continue advancing rapidly. The stakes are potentially existential. The time remaining is unknown, but most experts believe it is shorter than previously thought.
The mainstream era’s defining question: Will we get this right?