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China's strength lies in its medium- to long-term perspective. The G20 and Chinese leadership are ambitious.

Climate change is an existential threat that we all recognize, but addressing it creates value. Society increasingly values achieving net zero, spurred by sustainable development goals, the Paris agreement, social movements, and government action. Companies and investors who are part of the solution will be rewarded, while those lagging behind will be punished. Investing in new technologies and changing business practices to reduce and eliminate climate change is vital.

Yesterday, I addressed the Federal Assembly of the Russian Federation, emphasizing the importance of significantly reducing our country's accumulated net emissions by 2050 in the context of social and economic development. I also mentioned considering preferential treatment for foreign entities. It is crucial to highlight that Russia is genuinely interested in enhancing international cooperation.

The speaker states they will achieve net zero by February 1950, with specific, measurable short-term targets set for 2025 and February 1930. They also believe they will profit from this initiative, as the world is moving in this direction. They express confidence that being carbon competitive will create value.

There's skepticism about ESG and sustainable labels, which is why we're focused on net zero. We can't stabilize the climate without achieving it. It's simple: emissions either increase or decrease. If they're decreasing, are they doing so in line with scientific standards? We're basing this on the same science used by the UN and others for the 1.5-degree objectives. These are hard numbers, not subjective opinions.

There's scrutiny and healthy skepticism around ESG and sustainable labels, which is actually a good thing. It's why we're so focused on net zero. Stabilizing the climate requires us to reach net zero; it's that simple. We're dealing with hard numbers: emissions either increase or decrease. If they're decreasing, are they doing so at a rate consistent with scientific targets? Our approach is rooted in the same science used by the UN and others for the 1.5-degree objectives.

It's good that environmental, social, and governance (ESG) labels face scrutiny and healthy skepticism. This is a key reason we are so focused on net zero. We can't stabilize the climate without achieving net zero; it's that simple. Emissions either increase or decrease. If decreasing, are they doing so at a rate consistent with scientific findings? We're basing our approach on the same science that the UN and others use for their one-and-a-half-degree objectives.

We commit to bringing new medicines to the US, Europe, and the world every year. These medicines will be added to our portfolio. This fulfills a dream we had in 2019 when we set goals for the next five years. One of those goals was to reduce the world's population by 50% by 2023. Today, this dream is becoming a reality. We are a purpose-driven company.

More businesses are making net zero commitments, but the criteria are often unclear, leading to false narratives and greenwashing. To address this, an expert group has created guidelines for credible net zero pledges. Corporate leaders are urged to follow these guidelines, submit transparent transition plans on achieving net zero, and prioritize real emissions cuts over carbon credits or shadow markets.

We need reliable power sources as our electricity demand is expected to double by 2050. The failure of the power grid in Texas, which resulted in 346 deaths, highlights the importance of having dependable energy sources. We are working with the federal government to establish a framework for small modular nuclear reactors and to develop our hydrogen infrastructure for zero-emission vehicles and net-zero homes. We are also making progress in geothermal and ammonia exports. Our goal is to achieve carbon neutrality by 2050, but we disagree with the federal government's unrealistic timeline that would require shutting down our production. We seek common ground and collaboration to find solutions.

Many human activities produce greenhouse gases, but to reach zero emissions by 2050, we must find cost-effective ways to do these activities without emitting greenhouse gases. The green premium, or extra cost, for green alternatives like biofuels is high, with biofuels costing $3.15 more per gallon than regular jet fuel. By reducing the green premium through research and innovation, we can achieve our goal of zero emissions by 2050.

Speaker 0 states: "We're gonna manage to net zero by 02/1950. Actually, we're gonna have specific targets by 2025 and 02/1930, so you can measure the short term. And they don't say this quite as loudly, but what they're also saying and thinking, and we're gonna make a lot of money off of this because actually this is the way the world's headed. I mean, I'm sure exactly where we get there, but to be carbon competitive is is gonna be value creating."

The speakers discuss the goal of achieving net zero emissions by 2050 and the challenges associated with it. They mention the need to transition to electric vehicles at a faster rate and create a resilient net zero economy. The focus is on accelerating the transition to a net zero economy by increasing private capital flows. The conversation then shifts to the UK's detailed plan for achieving a net zero economy, which includes closing most airports by 2030, eliminating air travel and shipping by 2050, and restricting road use, food, heating, and energy consumption to 60% of current levels by 2050. The speakers also mention the connection between these goals and other global initiatives.

Canada is proposing to set the cap level for emissions at 35% to 38% below 2019 levels by 2030. This is crucial for Canada to achieve its goal of reducing emissions by 40% to 45% across the economy by 2030.

It's good that environmental, social, and governance (ESG) labels face scrutiny and healthy skepticism. This scrutiny is a key reason for our relentless focus on achieving net zero emissions. Ultimately, stabilizing the climate depends on reaching net zero. It's straightforward math: emissions are either increasing or decreasing. If they're decreasing, we need to check if that decrease is consistent with scientific targets. We base our approach on the same scientific data used by the UN and others for the 1.5-degree climate objective.

Canada is proposing to set the cap level for emissions at 35% to 38% below 2019 levels by 2030. This is crucial for Canada to achieve its target of reducing emissions by 40% to 45% across the economy by 2030.

It's good that environmental, social, and governance (ESG) labels face scrutiny. This skepticism drives our intense focus on achieving net zero emissions. Ultimately, stabilizing the climate requires reaching net zero; it's that simple. Emissions either increase or decrease. The key question is whether they are decreasing at a rate consistent with scientific targets. Our approach is grounded in the same scientific principles used by the UN and others, specifically targeting the 1.5-degree objective.

You demanded action, and now it's time for the financial sector to deliver. To reach net zero, every country, every company, every bank, every investor, every pension fund around the world will need to make some big changes. In the run up to COP twenty six in Glasgow, we have an enormous opportunity to bring climate change into the heart of every financial decision, and our plan will manage the risk from climate change while helping to seize the opportunities from a newer, greener economy. The UK has been at the forefront of innovation for centuries brimming with ingenuity and a can do spirit. It also houses the world's largest financial system, and by bringing them together, we can deliver the net zero world that you've demanded and that our future generations deserve. The world's coming to Glasgow. Let's reshape finance for a sustainable world.

- The conversation centers on how AI progress has evolved over the last few years, what is surprising, and what the near future might look like in terms of capabilities, diffusion, and economic impact. - Big picture of progress - Speaker 1 argues that the underlying exponential progression of AI tech has followed expectations, with models advancing from “smart high school student” to “smart college student” to capabilities approaching PhD/professional levels, and code-related tasks extending beyond that frontier. The pace is roughly as anticipated, with some variance in direction for specific tasks. - The most surprising aspect, per Speaker 1, is the lack of public recognition of how close we are to the end of the exponential growth curve. He notes that public discourse remains focused on political controversies while the technology is approaching a phase where the exponential growth tapers or ends. - What “the exponential” looks like now - There is a shared hypothesis dating back to 2017 (the big blob of compute hypothesis) that what matters most for progress are a small handful of factors: compute, data quantity, data quality/distribution, training duration, scalable objective functions, and normalization/conditioning for stability. - Pretraining scaling has continued to yield gains, and now RL shows a similar pattern: pretraining followed by RL phases can scale with long-term training data and objectives. Tasks like math contests have shown log-linear improvements with training time in RL, and this pattern mirrors pretraining. - The discussion emphasizes that RL and pretraining are not fundamentally different in their relation to scaling; RL is seen as an RL-like extension atop the same scaling principles already observed in pretraining. - On the nature of learning and generalization - There is debate about whether the best path to generalization is “human-like” learning (continual on-the-job learning) or large-scale pretraining plus RL. Speaker 1 argues the generalization observed in pretraining on massive, diverse data (e.g., Common Crawl) is what enables the broad capabilities, and RL similarly benefits from broad, varied data and tasks. - The in-context learning capacity is described as a form of short- to mid-term learning that sits between long-term human learning and evolution, suggesting a spectrum rather than a binary gap between AI learning and human learning. - On the end state and timeline to AGI-like capabilities - Speaker 1 expresses high confidence (~90% or higher) that within ten years we will reach capabilities where a country-of-geniuses-level model in a data center could handle end-to-end tasks (including coding) and generalize across many domains. He places a strong emphasis on timing: “one to three years” for on-the-job, end-to-end coding and related tasks; “three to five” or “five to ten” years for broader, high-ability AI integration into real work. - A central caution is the diffusion problem: even if the technology is advancing rapidly, the economic uptake and deployment into real-world tasks take time due to organizational, regulatory, and operational frictions. He envisions two overlapping fast exponential curves: one for model capability and one for diffusion into the economy, with the latter slower but still rapid compared with historical tech diffusion. - On coding and software engineering - The conversation explores whether the near-term future could see 90% or even 100% of coding tasks done by AI. Speaker 1 clarifies his forecast as a spectrum: - 90% of code written by models is already seen in some places. - 90% of end-to-end SWE tasks (including environment setup, testing, deployment, and even writing memos) might be handled by models; 100% is still a broader claim. - The distinction is between what can be automated now and the broader productivity impact across teams. Even with high automation, human roles in software design and project management may shift rather than disappear. - The value of coding-specific products like Claude Code is discussed as a result of internal experimentation becoming externally marketable; adoption is rapid in the coding domain, both internally and externally. - On product strategy and economics - The economics of frontier AI are discussed in depth. The industry is characterized as a few large players with steep compute needs and a dynamic where training costs grow rapidly while inference margins are substantial. This creates a cycle: training costs are enormous, but inference revenue plus margins can be significant; the industry’s profitability depends on accurately forecasting future demand for compute and managing investment in training versus inference. - The concept of a “country of geniuses in a data center” is used to describe the point at which frontier AI capabilities become so powerful that they unlock large-scale economic value. The timing is uncertain and depends on both technical progress and the diffusion of benefits through the economy. - There is a nuanced view on profitability: in a multi-firm equilibrium, each model may be profitable on its own, but the cost of training new models can outpace current profits if demand does not grow as fast as the compute investments. The balance is described in terms of a distribution where roughly half of compute is used for training and half for inference, with margins on inference driving profitability while training remains a cost center. - On governance, safety, and society - The conversation ventures into governance and international dynamics. The world may evolve toward an “AI governance architecture” with preemption or standard-setting at the federal level, to avoid an unhelpful patchwork of state laws. The idea is to establish standards for transparency, safety, and alignment while balancing innovation. - There is concern about autocracies and the potential for AI to exacerbate geopolitical tensions. The idea is that the post-AGI world may require new governance structures that preserve human freedoms, while enabling competitive but safe AI development. Speaker 1 contemplates scenarios in which authoritarian regimes could become destabilized by powerful AI-enabled information and privacy tools, though cautions that practical governance approaches would be required. - The role of philanthropy is acknowledged, but there is emphasis on endogenous growth and the dissemination of benefits globally. Building AI-enabled health, drug discovery, and other critical sectors in the developing world is seen as essential for broad distribution of AI benefits. - The role of safety tools and alignments - Anthropic’s approach to model governance includes a constitution-like framework for AI behavior, focusing on principles rather than just prohibitions. The idea is to train models to act according to high-level principles with guardrails, enabling better handling of edge cases and greater alignment with human values. - The constitution is viewed as an evolving set of guidelines that can be iterated within the company, compared across different organizations, and subject to broader societal input. This iterative approach is intended to improve alignment while preserving safety and corrigibility. - Specific topics and examples - Video editing and content workflows illustrate how an AI with long-context capabilities and computer-use ability could perform complex tasks, such as reviewing interviews, identifying where to edit, and generating a final cut with context-aware decisions. - There is a discussion of long-context capacity (from thousands of tokens to potentially millions) and the engineering challenges of serving such long contexts, including memory management and inference efficiency. The conversation stresses that these are engineering problems tied to system design rather than fundamental limits of the model’s capabilities. - Final outlook and strategy - The timeline for a country-of-geniuses in a data center is framed as potentially within one to three years for end-to-end on-the-job capabilities, and by 2028-2030 for broader societal diffusion and economic impact. The probability of reaching fundamental capabilities that enable trillions of dollars in revenue is asserted as high within the next decade, with 2030 as a plausible horizon. - There is ongoing emphasis on responsible scaling: the pace of compute expansion must be balanced with thoughtful investment and risk management to ensure long-term stability and safety. The broader vision includes global distribution of benefits, governance mechanisms that preserve civil liberties, and a cautious but optimistic expectation that AI progress will transform many sectors while requiring careful policy and institutional responses. - Mentions of concrete topics - Claude Code as a notable Anthropic product rising from internal use to external adoption. - The idea of a “collective intelligence” approach to shaping AI constitutions with input from multiple stakeholders, including potential future government-level processes. - The role of continual learning, model governance, and the interplay between technology progression and regulatory development. - The broader existential and geopolitical questions—how the world navigates diffusion, governance, and potential misalignment—are acknowledged as central to both policy and industry strategy. - In sum, the dialogue canvasses (a) the expected trajectory of AI progress and the surprising proximity to exponential endpoints, (b) how scaling, pretraining, and RL interact to yield generalization, (c) the practical timelines for on-the-job competencies and automation of complex professional tasks, (d) the economics of compute and the diffusion of frontier AI across the economy, (e) governance, safety, and the potential for a governance architecture (constitutions, preemption, and multi-stakeholder input), and (f) the strategic moves of Anthropic (including Claude Code) within this evolving landscape.

In January 2019, my leadership team and I gathered in California to set goals for the next five years. One of our dreams was to reduce the global population by 50% by 2023. Today, it seems like this dream is starting to come true.

Advocates for NetZero need to address the practicalities of achieving it. Without fossil fuels, which are used in almost everything we do, including food production, transportation, and job creation, it's not feasible. The goal of achieving net zero emissions by 2050 is unrealistic and has not been successful so far, as global carbon emissions have actually increased. This policy benefits countries like China, India, and Russia, who don't follow the rules, at the expense of Western nations. Eventually, this will lead to anger and frustration when net zero emissions cannot be achieved.