🚨 The AI 2040 Plan: A Concrete Roadmap to Slow Down AGI

The AI doomer movement is no longer just about sounding alarms — it's about concrete policy proposals. As Congress begins to wake up to AI doomsday threats (literally covered in The Wall Street Journal this month), protests are emerging across the country, and high-profile figures are being pressed on AI existential risk. The question is no longer whether regulation is coming, but what form it will take.

📋 The AI 2040 Framework: A Detailed Slowdown Strategy

The AI 2040 proposal represents the most detailed regulatory roadmap yet from the AI safety community. Contrary to popular belief, this is not a call to stop AI development entirely. Instead, the plan seeks to:

• Keep current models running and allow inference to continue
• Maintain ongoing capabilities research
• Delay the arrival of superintelligence from 2028 to 2040
• Reach top human expert capability around 2035, then wait five years before unlocking superintelligence in 2040

The goal is simple: buy time to ensure proper safety measures, governance structures, and international coordination are in place before humanity crosses the superintelligence threshold.

🔒 Enforcement Mechanisms: How to Pause Training Runs

The AI 2040 plan includes highly tactical enforcement mechanisms designed to control the pace of AI development:

1. The AI Pause
The plan calls for a pause on all new frontier training runs and R&D experiments. To enforce this, the proposal suggests applying inference-only verification to all major AI data centers — specifically, any facility with more than 10,000 H100 equivalents (roughly $100 million worth of equipment). Enforcement would be straightforward: identify large buildings, send inspectors inside, count the GPUs, and require permits for all workloads.

2. International Compute Inventories
Major countries would be required to declare their AI compute inventories to the international community — similar to nuclear warhead declarations. This includes disclosing how many H100 equivalents they possess and where they are located. While this mirrors nuclear non-proliferation frameworks, international agreements of this scale remain notoriously difficult to implement.

3. Infrastructure Restrictions
For existing data centers, the plan proposes removing high-bandwidth east-west networking to prevent large distributed training runs while still allowing inference workloads. For new R&D facilities, the requirements become far more stringent:

• Entirely new facilities built from scratch with nation-state level physical security
• Buildings constructed inside Faraday cages to prevent external communication
• Air-gapped systems with strictly controlled access
• Bandwidth-capped external connections at one megabit per second — enough to send instructions, but slow enough that exfiltrating model weights would take approximately five years

This bandwidth cap represents a particularly clever hardware-level control: any attempt to steal model weights would be immediately obvious, as the one-meg pipe would run at full capacity for months.

4. Physical Transfer Protocols
When frontier model weights need to move from R&D facilities to inference facilities, the plan calls for extraordinary security measures. Weights would be placed on physical storage devices encrypted independently by both the US and China, with both countries required to sign off. Representatives from both nations would physically escort the export to its destination.

Additionally, frontier models would be made deliberately larger than compute-optimal — potentially 100 terabytes instead of one terabyte — making them significantly harder to move or steal.

⚙️ The Core Control Valve: Compute Caps

The most effective mechanism for controlling capability improvements? Compute caps and data center buildout slowdowns. Rather than allowing algorithmic breakthroughs that can leak to secret projects, the AI 2040 framework proposes a system where models improve primarily by adding hardware — a resource that's visible, trackable, and controllable.

Under this system, organizations would need to demonstrate successful alignment work before being allocated additional compute. The goal is to shift away from "one weird trick" algorithmic leaps and toward measured, supervised scaling.

🏛️ Bernie Sanders Enters the Arena

Senator Bernie Sanders has proposed his own version of AI regulation, which includes:

• Banning artificial superintelligence — defined as AI systems that match or exceed human cognitive performance across a broad range of domains
• Banning AI systems capable of planning and executing the disempowerment of humanity
• Banning AI systems that could overthrow or undermine the US government
• Pausing advanced AI development until a new federal AI regulatory body is operational
• Creating a cabinet-level federal agency to monitor frontier AI systems throughout their lifecycle
• Implementing the "corporate death penalty" for violations — entities could face dissolution, and individuals could face up to 20 years in prison

The "corporate death penalty" language represents a notably aggressive regulatory stance, going beyond typical bankruptcy or wind-down procedures.

🔄 The Unintended Consequences Debate

Critics of these proposals raise several concerns:

Regulatory Capture Risk
Heavy-handed regulation could cement the position of a handful of major players while preventing new entrants from competing. Startups and smaller labs worry about being locked out of frontier AI development entirely.

Economic Opportunity Cost
In scenarios where alignment is solved and X-risk diminishes significantly, these regulations could delay enormous economic gains and improvements to human welfare. The "good ending" would make these restrictions look overly cautious in hindsight.

Secret Development Incentives
International coordination creates incentives for millions of individuals or groups globally to pursue breakthrough research in secret. While large-scale training runs remain visible due to their energy signatures and infrastructure requirements, the framework assumes that scale will remain a prerequisite for AGI development.

Libertarian Concerns
Even when dealing with $100 million computers, restrictions on computational freedom raise philosophical questions about the appropriate limits of government control over technology.

💼 Industry Pushback: The Pro-Development Coalition

Not everyone in the AI ecosystem supports slowdown proposals. Several groups are pushing back:

Semiconductor Manufacturers and Hardware Providers
Companies like Nvidia and data center providers have strong economic incentives to maintain rapid AI development. Jensen Huang has expressed skepticism about doom scenarios.

Application Layer Companies
Interestingly, companies building on top of frontier models are less concerned about capabilities risk. Many are still struggling to effectively deploy current models and see massive capability overhang — plenty of untapped potential in existing systems that could drive value for years.

Pro-Freedom Advocates
Jamie Cox, co-founder of Fluid Stack, outlined a set of convictions supporting American AI development: pro-freedom, pro-democracy, believing AI will bolster human flourishing, and supporting simple, clear regulatory frameworks that set requirements proportional to capabilities and risk — without unnecessary barriers to competition.

Anthropic's Dario Amodei endorsed the statement "we believe AI will make everyone rich, healthy, and free" as "novel and interesting comms from the frontier."

📊 Tyler Cowen's Challenge: Name Your Market Prices

Economist Tyler Cowen has challenged AI doomers to make falsifiable predictions: "If you have very pessimistic fears or predictions about AI, name the market prices that will support or confirm them. This is what taking this seriously means."

Critics respond that short-term existential risk may not meaningfully affect market prices — contracts that pay out if everyone dies aren't worth anything. However, some prominent safety researchers are putting capital to work:

Paul Christiano's Portfolio
Paul Christiano, who recently joined OpenAI's board, is 2x levered long with the following allocation:

• 5% of net worth in Tesla
• 90% in AI-related bets
• 100% in normal investments
• Short position in US 30-year debt

Interestingly, this portfolio performs well in both doom scenarios and optimistic AI outcomes, making it less of a pure "doom bet" and more of a general AI acceleration trade.

🪰 The Fruit Fly Simulation: A Preview of Moral Questions Ahead

Google recently mapped the entire neural structure of a fruit fly in 3D, and developers have now recreated it in software simulation. This has sparked unexpected ethical debates:

• Researchers can now observe the fly's "brain" react in real-time — when trapped in a virtual environment (like inside a Rabbit R1 device), its escape circuits light up
• Developers have even taught the simulated fly to parallel park
• Some have made it play the video game Doom

The discourse quickly moved to deeper questions: If torturing a simulated fly feels wrong, what about simulated humans? As AI systems become more sophisticated and potentially develop agency, society will need to grapple with questions of digital consciousness and moral weight.

🎯 The Bottom Line

The AI safety movement has moved from abstract warnings to concrete policy proposals. Whether the AI 2040 framework represents prudent caution or excessive restriction remains hotly debated. What's clear is that the conversation is shifting from if regulation will happen to what form it will take.

For investors, builders, and policymakers, the key variables to watch are:

• Congressional momentum around AI safety legislation
• International coordination efforts (particularly between the US and China)
• The trajectory of capabilities vs. alignment research
• Whether X-risk estimates rise or fall as models continue to scale

Current models remain highly capable, and significant value can be extracted from existing systems. But the race to superintelligence — and the regulatory frameworks that will govern it — is accelerating faster than most anticipated.

"The goal here is not to go back in time. It's definitely not to stop everything in its tracks. It's a slowdown with the goal of scaling gradually."

The debate over AI's future is no longer academic — it's entering the halls of Congress, corporate boardrooms, and international treaty negotiations. The next few years will determine whether humanity takes a measured approach to superintelligence or races toward it at full speed.