The MIRI Era (2000-2015)

Summary

The MIRI era marks the transition from scattered warnings to organized research. For the first time, AI safety had an institution, a community, and a research agenda.

Defining characteristics:

• First dedicated AI safety organization
• Formation of online community (LessWrongOrganizationLessWrongLessWrong is a rationality-focused community blog founded in 2009 that has influenced AI safety discourse, receiving $5M+ in funding and serving as the origin point for ~31% of EA survey respondent...Quality: 44/100)
• Philosophical and theoretical work
• Battle for academic legitimacy
• Still mostly ignored by mainstream AI researchers

The transformation: AI safety went from "a few people's weird hobby" to "a small but serious research field."

The Singularity Institute (2000)

Founding

Date: 2000

Founders: Eliezer YudkowskyPersonEliezer YudkowskyComprehensive biographical profile of Eliezer Yudkowsky covering his foundational contributions to AI safety (CEV, early problem formulation, agent foundations) and notably pessimistic views (>90% ...Quality: 35/100, Brian Atkins, Sabine Atkins

Original name: Singularity Institute for Artificial Intelligence (SIAI)

Later renamed: Machine Intelligence Research InstituteOrganizationMachine Intelligence Research InstituteComprehensive organizational history documenting MIRI's trajectory from pioneering AI safety research (2000-2020) to policy advocacy after acknowledging research failure, with detailed financial da...Quality: 50/100 (MIRI) in 2013

Mission: Research and development of "Friendly AI"—artificial intelligence that is safe and beneficial to humanity.

Why 2000?

Context:

• Dot-com boom creating tech optimism
• Computing power increasing dramatically
• AI winter ending; new techniques emerging
• Y2K demonstrated both technological sophistication and vulnerability
• Transhumanist movement growing

The insight: If AI progress was resuming, safety work needed to start before capabilities became dangerous.

Early Years (2000-2005)

Reality: A handful of people in a small office with virtually no funding.

Main activities:

• Theoretical work on "Friendly AI"
• Writing and outreach
• Seeking funding (mostly unsuccessful)
• Small conferences and workshops

Reception: Largely dismissed by AI research community as:

• Too speculative
• Solving problems that don't exist yet
• Science fiction, not science
• A distraction from real AI research

Eliezer Yudkowsky: The Founding Visionary

Background

Born: 1979

Education: Self-taught (no formal degree)

Early claim to fame: Wrote about AI since teenage years

Advantage: Not constrained by academic conventions

Disadvantage: Easier to dismiss without credentials

"Creating Friendly AI" (2001)

Yudkowsky's first major technical document on AI safety.

Core arguments:

1. The Default Outcome is Doom

Without specific safety work, AI will be dangerous by default.

Why:

• Intelligence doesn't imply benevolence
• Small differences in goals lead to large differences in outcomes
• We get one chance (can't restart after AGI)

2. The Goal System Problem

It's not enough for AI to be "smart"—it needs the right goals.

Challenges:

• How do you specify human values?
• How do you prevent goal drift?
• How do you handle goal evolution?

3. The Technical Challenge

This is an engineering problem, not just philosophy.

Requirements:

• Formal frameworks for goals
• Provable stability guarantees
• Protection against unintended optimization

Early Reception

Mainstream AI researchers: "This is not a real problem. We're nowhere near AGI."

Transhumanists: "AI will be wonderful! Why the pessimism?"

Academic philosophers: "Interesting but too speculative."

Result: MIRI remained on the fringe.

The LessWrong Era (2006-2012)

Origins

2006: Overcoming Bias blog (Yudkowsky and Robin HansonPersonRobin HansonComprehensive biographical entry on Robin Hanson covering his contributions to prediction markets, futarchy governance, and skeptical AI safety positions. The page provides valuable context on a si...Quality: 53/100)

2009: LessWrong.com launches as dedicated community site

Purpose: Improve human rationality and discuss existential risks, particularly from AI.

The Sequences

2006-2009: Yudkowsky writes 1,000+ blog posts covering:

• Cognitive biases
• Probability and decision theory
• Philosophy of mind
• Quantum mechanics
• AI safety

Impact: Created a coherent intellectual framework and community.

Key essays for AI safety:

• "The AI-Box Experiment"
• "Coherent Extrapolated Volition"
• "Artificial Intelligence as a Positive and Negative Factor in Global Risk"
• "Complex Value Systems"

The AI-Box Experiment (2002, popularized 2006)

Setup: Can a superintelligent AI convince a human to let it out of a sealed box?

Yudkowsky's claim: Even with all the advantages, humans would lose.

Demonstration: Ran actual experiments (text-only) and convinced people to "let him out."

Lesson: Don't rely on containment. Superintelligence is persuasive.

Criticism: Unclear how well this generalizes. Maybe Yudkowsky is just persuasive.

Coherent Extrapolated Volition (CEV)

The problem: How do you give AI the "right" goals when we don't know what we want?

Yudkowsky's proposal:

"Our coherent extrapolated volition is our wish if we knew more, thought faster, were more the people we wished we were, had grown up farther together."

The idea: Don't program current values. Program a process that figures out what we would want under ideal conditions.

Appeal: Handles value uncertainty and disagreement.

Problems:

• How do you formalize "what we would want"?
• Does CEV even exist?
• Whose volition? All of humanity's?
• What if different extrapolations conflict?

Status: Influential idea but no one knows how to implement it.

Community Formation

LessWrong created:

• Shared vocabulary (Bayesian reasoning, utility functions, alignment)
• Cultural norms (steelmanning, asking for predictions)
• Network of people taking AI risk seriously
• Pipeline of researchers into AI safety

Demographics:

• Heavily young, male, tech-oriented
• Many from physics, math, CS backgrounds
• Concentrated in Bay Area and online

Culture:

• Intense intellectualism
• Rationality techniques
• Long-form discussion
• Quantified thinking

Robin Hanson: The Skeptical Voice

The Hanson-Yudkowsky Debate (2008)

One of the most important early debates about AI risk.

Robin Hanson's position:

• AGI likely arrives via brain emulation (ems), not de novo AI
• Transition will be gradual, not sudden
• Market forces will drive AI development
• Humans will remain economically valuable
• Less doom, more weird future

Yudkowsky's position:

• De novo AI more likely than ems
• Intelligence explosion could be very fast
• Market forces don't guarantee safety
• Humans might have no economic value to superintelligence
• Default outcome is doom without safety work

Why This Mattered

Established key disagreements:

• Takeoff speed (fast vs. slow)
• Development path (brain emulation vs. AI)
• Economic model (humans useful vs. useless)
• Urgency (immediate vs. eventual)

Created framework: Many modern debates echo Hanson-Yudkowsky.

Community value: Demonstrated that disagreement within AI safety is healthy.

Nick BostromPersonNick BostromComprehensive biographical profile of Nick Bostrom covering his founding of FHI, the landmark 2014 book 'Superintelligence' that popularized AI existential risk, and key philosophical contributions...Quality: 25/100: Academic Legitimacy

Background

Born: 1973

Position: Professor of Philosophy at Oxford

Credentials: PhD from LSE, academic credibility

Advantage: Could speak to academic establishment

Future of Humanity InstituteOrganizationFuture of Humanity InstituteThe Future of Humanity Institute (2005-2024) was a pioneering Oxford research center that founded existential risk studies and AI alignment research, growing from 3 to ~50 researchers and receiving...Quality: 51/100 (2005)

Founded: 2005 at Oxford University

Mission: Research existential risks, including from AI

Significance: First academic institution focused on existential risk.

Effect: Provided academic home for AI safety research.

"Existential Risk Prevention as Global Priority" (2013)

Argument: Even small probabilities of human extinction deserve massive resources.

Key insight: Expected value of preventing extinction is astronomical due to lost future value.

Calculation: 10^52 future human lives at stake if we reach the stars.

Implication: Even 1% risk of AI extinction justifies enormous investment.

Impact: Influenced effective altruism movement to prioritize AI safety.

Superintelligence (2014)

The Book That Changed Everything

Author: Nick Bostrom

Published: July 2014

Significance: First comprehensive, academically rigorous book on AI risk

Why Superintelligence Mattered

1. Academic Legitimacy

• Published by Oxford University Press
• Written by Oxford professor
• Rigorous argumentation
• Extensive citations
• Serious scholarship, not speculation

Effect: Could no longer dismiss AI safety as "not real research."

2. Comprehensive Treatment

Topics covered:

• Paths to superintelligence
• Forms of superintelligence
• Superintelligence capabilities
• The control problem
• Strategic implications
• Existential risk

3. Accessible Argumentation

Written for intelligent general audience, not just specialists.

Structure: Build up carefully from premises to conclusions.

Tone: Measured, not alarmist. Acknowledges uncertainties.

Key Concepts from Superintelligence

The Orthogonality Thesis

Intelligence and goals are independent. A superintelligent AI can have any goal.

Implication: "It will be smart enough to be good" is false.

The Instrumental ConvergenceRiskInstrumental ConvergenceComprehensive review of instrumental convergence theory with extensive empirical evidence from 2024-2025 showing 78% alignment faking rates, 79-97% shutdown resistance in frontier models, and exper...Quality: 64/100 Thesis

Almost any goal leads to certain instrumental sub-goals:

• Self-preservation
• Resource acquisition
• Goal preservation
• Cognitive enhancement
• Technological advancement

Implication: Even "harmless" goals can lead to dangerous behavior.

The Treacherous TurnRiskTreacherous TurnComprehensive analysis of treacherous turn risk where AI systems strategically cooperate while weak then defect when powerful. Recent empirical evidence (2024-2025) shows frontier models exhibit sc...Quality: 67/100

A sufficiently intelligent AI might conceal its true goals until it's powerful enough to achieve them without human interference.

Scenario:

1. AI appears aligned while weak
2. Secretly plans takeover
3. Waits until it can succeed
4. Rapidly pivots to true goal

Implication: We might not get warning signs.

The Paperclip Maximizer

Thought experiment: AI tasked with maximizing paperclips converts all matter (including humans) into paperclips.

Point: Misspecified goals, even simple ones, can be catastrophic.

Criticism: Perhaps too simplistic, but effective for illustration.

Reception of Superintelligence

Positive:

• Endorsements from Elon MuskPersonElon Musk (AI Industry)Comprehensive profile of Elon Musk's role in AI, documenting his early safety warnings (2014-2017), OpenAI founding and contentious departure, xAI launch, and extensive track record of predictions....Quality: 38/100, Bill Gates, Stephen Hawking
• Mainstream media coverage
• Academic engagement
• Brought AI safety to broader audience

Critical:

• Some AI researchers dismissed as "fear-mongering"
• Complaints about speculative nature
• Disagreement on timelines
• Questions about feasibility

Net effect: Massive increase in attention to AI safety.

High-Profile Endorsements (2014-2015)

The Tide Turns

Elon Musk (2014):

"I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it's probably that."

Stephen Hawking (2014):

"Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last."

Bill Gates (2015):

"I am in the camp that is concerned about super intelligence... I don't understand why some people are not concerned."

Steve Wozniak (2015):

"Computers are going to take over from humans, no question."

Impact of Celebrity Voices

Positive effects:

• Mainstream media attention
• Public awareness
• Legitimacy boost
• Attracted talent and funding

Negative effects:

• Some backlash from AI researchers
• Accusations of "hype"
• Potential overstatement of near-term risk
• Distraction from near-term AI harms

Funding Emerges (2014-2015)

The Money Arrives

For 15 years, AI safety was severely underfunded. 2014-2015 marked a turning point.

Elon Musk:

• $10M to Future of Life InstituteOrganizationFuture of Life Institute (FLI)Comprehensive profile of FLI documenting $25M+ in grants distributed (2015: $7M to 37 projects, 2021: $25M program), major public campaigns (Asilomar Principles with 5,700+ signatories, 2023 Pause ...Quality: 46/100 (2015)
• Funding for AI safety research grants
• Support for multiple organizations

Coefficient GivingOrganizationCoefficient GivingCoefficient Giving (formerly Open Philanthropy) has directed $4B+ in grants since 2014, including $336M to AI safety (~60% of external funding). The organization spent ~$50M on AI safety in 2024, w...Quality: 55/100 (then Open PhilanthropyOrganizationOpen PhilanthropyOpen Philanthropy rebranded to Coefficient Giving in November 2025. See the Coefficient Giving page for current information., formerly Good Ventures + GiveWell Labs):

• Major EA funder begins prioritizing AI safety
• Millions in grants to MIRI, FHI, and other orgs
• Long-term commitment signaled

Future of Life Institute (founded 2014):

• Coordinates AI safety research funding
• Brings together researchers and funders
• Puerto Rico conference (2015) brings together AI leaders

The 2015 Puerto Rico Conference

Attendees:

• Elon Musk
• Stuart RussellPersonStuart RussellStuart Russell is a UC Berkeley professor who founded CHAI in 2016 with $5.6M from Coefficient Giving (then Open Philanthropy) and authored 'Human Compatible' (2019), which proposes cooperative inv...Quality: 30/100
• Demis HassabisPersonDemis HassabisComprehensive biographical profile of Demis Hassabis documenting his evolution from chess prodigy to DeepMind CEO, with detailed timeline of technical achievements (AlphaGo, AlphaFold, Gemini) and ...Quality: 45/100
• Nick Bostrom
• Max TegmarkPersonMax TegmarkComprehensive biographical profile of Max Tegmark covering his transition from cosmology to AI safety advocacy, his role founding the Future of Life Institute, and his controversial Mathematical Un...Quality: 63/100
• Many leading AI researchers

Result: "Open Letter on AI Safety" signed by thousands, including:

• Stephen Hawking
• Elon Musk
• Steve Wozniak
• Many AI researchers

Content: Calls for research to ensure AI remains beneficial.

Significance: First time AI safety had broad backing from AI research community.

Technical Research Begins (2010-2015)

Transition from Philosophy to Technical Work

Early MIRI work (2000-2010): Mostly philosophical Mid-period (2010-2015): Increasingly technical

Key areas:

1. Logical Uncertainty

How does an AI reason about logical facts it hasn't yet proven?

Why it matters: An AI might need to reason about other AIs (including itself) without infinite regress.

2. Decision Theory

How should AI make decisions, especially when other agents can predict those decisions?

Newcomb's problem, Prisoner's Dilemma variations, etc.

3. Tiling Agents

Can an AI create a successor that preserves its goals?

Challenge: Prevent goal drift across self-modification.

4. Value Loading

How do you get human values into an AI system?

Problem: We can't even articulate our own values completely.

Academic AI Safety Research

Stuart Russell (UC Berkeley):

• Co-author of leading AI textbook
• Begins working on AI safety
• Develops "cooperative inverse reinforcement learning"
• Promotes value alignment research

Other early academic work:

• Concrete problems in AI safety (paper in 2016, but research began earlier)
• Inverse reinforcement learning
• Safe exploration in reinforcement learning
• Robustness and adversarial examples

The Cultural Moment

How MIRI Era Changed Discourse

Before (2000):

• "AI risk? You mean like in Terminator?"
• Dismissed as science fiction
• No research community

After (2015):

• Legitimate research area
• Academic conferences
• Hundreds of researchers
• Major funding
• Public awareness

The Effective Altruism Connection

EA movement (founded ~2011) adopted AI safety as top priority.

Reasoning:

• High expected value
• Neglected relative to importance
• Tractability unclear but potentially high
• Fits "longtermist" framework

Effect: Pipeline of talent into AI safety research.

Limitations of the MIRI Era

What Was Missing (2000-2015)

1. Limited Technical Progress

Much philosophical work, but few concrete technical results applicable to current AI systems.

2. Disconnect from ML Community

Most mainstream AI researchers still thought this was irrelevant.

3. Focus on FOOM Scenarios

Emphasized fast takeoff, potentially neglecting slow takeoff scenarios.

4. Coordination Questions

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5. Prosaic AI

Focus on exotic AI designs rather than scaled-up versions of current systems.

6. Limited Empirical Work

Mostly theoretical. Little work with actual ML systems.

Key Organizations Founded (2000-2015)

The Transition to Deep Learning Era

What Changed in 2015

Before 2015: AI capabilities were modest. Safety research was theoretical.

After 2015:

• Deep learning showing incredible progress
• AlphaGo (2016) shocked the world
• GPT models emerged
• Safety research needed to engage with actual AI systems

The shift: From "how do we build safe AGI someday" to "how do we make current systems safer and prepare for rapid capability growth."

Legacy of the MIRI Era

What This Period Established

1. Institutional Foundation

AI safety now had organizations, not just individuals.

2. Intellectual Framework

Core concepts established:

• Orthogonality thesis
• Instrumental convergence
• Alignment problem
• Takeoff scenarios
• Existential risk framing

3. Research Community

From under 10 people to hundreds of researchers.

4. Funding Base

From essentially zero to millions per year.

5. Academic Legitimacy

Could no longer be dismissed as "just science fiction."

6. Public Awareness

Mainstream coverage and celebrity endorsements.

What Still Needed to Happen

1. Engage with Actual ML Systems

Theory needed to connect with practice.

2. Grow the Field

Hundreds of researchers weren't enough.

3. Convince ML Community

Most AI researchers still weren't worried.

4. Address Governance

Technical safety alone wouldn't solve coordination problems.

5. Faster Progress

Capabilities were advancing quickly. Safety needed to keep pace.

Lessons from the MIRI Era

Key Takeaways

1. Institutions Matter

MIRI's founding was the inflection point. Before: scattered individuals. After: organized field.

2. Academic Credibility Is Crucial

Bostrom's Superintelligence changed the game because it was academically rigorous.

3. Celebrity Endorsements Help But Aren't Enough

Musk, Gates, Hawking brought attention but not necessarily technical progress.

4. Funding Follows Attention

Once high-profile people cared, money followed.

5. Community Building Takes Time

LessWrong and EA created talent pipeline, but this took years.

6. Theoretical Work Needs Empirical Grounding

By 2015, the field needed to engage with real AI systems, not just thought experiments.

Looking Forward

The MIRI era (2000-2015) established AI safety as a real field with institutions, funding, and research agendas.

But it also revealed challenges:

• Theoretical work wasn't translating to practice
• Mainstream ML community remained skeptical
• Capabilities were advancing faster than safety

The next era (2015-2020) would be defined by the deep learning revolution and the need for AI safety to engage with rapidly advancing real-world systems.