Can AI do AI Research?

AI research can be carried out entirely within digital spaces, making it ripe for automation. Recent efforts have demonstrated that AI systems are capable of carrying out the whole process of research from ideation to publishing. Startup Sakana.ai has created an 'AI Scientist' that independently chooses research topics, conducts experiments, and publishes complete papers showing its results. While the quality of this work is still only comparable to an early-stage researcher, things will only improve from here.

Judging Social Situations

AI chatbots, including Claude and Microsoft Copilot, can outperform humans in evaluating social situations. In an established 'Situational Judgment Test', these AI systems consistently selected more effective responses than human participants.

SOURCE

Analyzing Scientific Literature

While language models are known to hallucinate information, this tendency can be reduced. PaperQA2, an LLM optimized to reliably provide factual information, was able to match or exceed human subject matter experts across a range of realistic literature review tasks. The summary articles it produced were found to be more accurate than those written by human authors.

SOURCE

Writing Emotive Poetry

A study has shown that non-expert readers can no longer tell AI-authored poems from those written by acclaimed human poets. The AI poems were also rated higher in rhythm and beauty.

SOURCE

Writing Post-surgical Operative Reports

Surgeons take painstaking notes of the actions they carry out during surgeries, collecting them into narrative form as an 'operative report'. A machine vision system was trained to watch surgery footage and produce such reports. It did so with higher accuracy (and much higher speed) than human authors.

SOURCE

Developing New Algorithms

AIs can find innovative solutions to difficult coding problems when given an appropriate framing. For example, a dedicated system called AlphaDev was trained to play a game about creating sorting algorithms. The algorithms it discovered were novel and outperformed existing human-authored benchmarks.

SOURCE

Who is Building AGI?

The following companies have explicitly stated they intend to develop AGI, either through public statements or in response to FLI’s 2024 AI Safety Index survey:

Anthropic

OpenAI

Google DeepMind

Meta

x.AI

Zhipu AI

Alibaba

DeepSeek

How can we avoid AGI?

There are policies we can implement to avoid some of the dangers of rapid power seeking through AI. They include:

Compute accounting
Standardized tracking and verification of AI computational power usage

Compute caps
Hard limits on computational power for AI systems, enforced through law and hardware

Enhanced liability
Strict legal responsibility for developers of highly autonomous, general, and capable AI

Tiered safety standards
Comprehensive safety requirements that scale with system capability and risk

TOMORROW’S AI

ALL SCENARIOS

Power Struggle

BACK TO SCENARIOS

Intended Use: Science/Engineering

Technology Type: Wearable/Personal Devices

Runaway Type: Environmental Tipping Points

Primary Setting: Global

The Future is Fusion

By the early 2030s, an AI-driven breakthrough in nuclear fusion stokes global optimism. For a moment, it feels like humanity has conquered scarcity and turned a corner on climate change. Pilot fusion grids hum to life, promising to absorb the soaring power needs of a globally booming AI economy. Unfortunately, this success only deepens fears of falling behind, as nations scramble to be first to develop the next AI breakthrough.

The AI Economy

With China centralizing its AI infrastructure under state control, the U.S. and its allies push to accelerate their own AI development. Governments subsidize new AI data centers, easing regulations to boost private sector growth. Tech giants like OpenAI, DeepMind, and Microsoft pour billions into infrastructure, turning rural areas into AI boomtowns. Despite the massive investment, advances toward AGI plateau. Massive models begin to deliver only marginal improvements, and the cost of further breakthroughs becomes prohibitive. Companies pivot toward deploying hyper-capable tool AI to capture consumer and enterprise markets at scale.

More, More, More

AI copilots become universal, embedded in 80% of professional workflows. Government sectors from healthcare to defense are rebuilt around predictive analytics and autonomous decision-making. Energy demand explodes beyond forecasts, and the promise of fusion is slowed by technical, regulatory, and material bottlenecks. Fossil fuel plants roar back online. Environmentalists sound alarms, but policymakers gamble: another breakthrough in fusion will come. It’s only a matter of time.

Deus Ex Machina?

AI-driven efficiencies initially boost economic output, with GDP growth in AI-heavy sectors surging by 7% annually. Automated supply chains reduce costs, AI-powered medical research accelerates drug discovery, and real-time weather models improve disaster preparedness. But fusion’s slow, uneven rollout leaves most grids reliant on traditional energy sources. Global CO₂ emissions rise by 8% in a decade - wiping out prior climate progress. Efforts to cap AI energy use shatter major climate alliances, as Western powers reject quotas they fear would hand China an edge.

Meltdown

In a desperate bid to outrun collapse, fusion, fossil, hydro, solar, and other energy sources are extracted at full tilt to help fuel the AI furnace. But droughts cripple hydroelectric dams. Emergency desalination efforts accelerate ecosystem collapse. Thawing Arctic permafrost unleashes methane plumes into the atmosphere. Fisheries crash. Coastal economies buckle. By the time carbon caps and AI energy quotas are imposed, runaway climate tipping points are irreversible. Nations that gambled on AI-fueled salvation are left scrambling for survival in a hotter, hungrier, fractured world.

BACK TO SCENARIOS