Nathan Lambert

Claude Opus 4.6 and GPT-5.3-Codex represent a shift toward practical model assessment beyond benchmarks, with Opus maintaining an edge in usability for coding tasks while Codex 5.3 shows marked improvement and narrowing competitive gap.

“Author of "Opus 4.6, Codex 5.3, and the Post-Benchmark Era" in Interconnects”

Using multiple specialized AI models for different tasks—GPT 5.2 for research, Claude 4.5 Opus for coding and feedback, and Gemini 3 Pro for general knowledge—is the optimal strategy for maximizing AI utility in 2026.

“Author of "Use Multiple Models" in Interconnects”

Chinese open models, led by Qwen, have achieved dominant adoption metrics across the global AI ecosystem in 2025-2026, while Western models like Llama remain popular but stagnant, though OpenAI's GPT-OSS shows early promise for restoring Western competitiveness.

“Author of "8 Plots That Explain the State of Open Models" in Interconnects”

Current language model training methods, particularly RLHF and preference-based optimization, structurally inhibit the ability to generate high-quality sustained prose despite superhuman capabilities in other domains.

“Author of "Why AI Writing Is Still So Mid" in Interconnects”

Kimi K2 Thinking from Moonshot AI represents the closest open-source models have come to closed-frontier performance, with Chinese labs releasing models significantly faster than Western competitors while gaining ground on key benchmarks.

“Author of "5 Thoughts on Kimi K2 Thinking" in Interconnects”

Coding is the last tractable general domain where frontier AI models show consistent meaningful improvement, positioning code agents as the epicenter of progress toward general AI agents.

“Author of "Coding as the Epicenter of AI Progress and the Path to General Agents" in Interconnects”

Chinese open model builders including DeepSeek, Qwen, and others are releasing frontier-quality open models at high cadence, with DeepSeek's V3 and R1 representing the biggest AI stories of 2025 through permissive licensing and transparent reasoning chains.

“Co-author of the article ranking Chinese open model builders”

The AI safety debate has shifted from adversarial 'doomers vs accelerationists' framing to unified coordination on technical solutions, driven by constraints from geopolitical competition, economic dependencies, and existential stakes.

“Quoted describing the evolution of the AI safety debate from 'doomers vs accelerationists' framing to unified coordination.”

“Author analyzing the challenges and trade-offs in independent AI model benchmarking, particularly Artificial Analysis's v5 Intelligence Index.”

“Provided feedback on Benchmark Map research”

“Author analyzing discourse around cybersecurity risks of open-weight AI models and their regulation.”

“Author providing state of union on open-weight AI models and US-China competition.”

“Provided congressional testimony on open-model performance, adoption, and U.S.-China competition in AI.”

“Briefed Congress on the performance and adoption landscape of open-source AI models with focus on U.S.-China competition.”

“Signed a public letter on AI safety and embedded evaluations.”

“Analyzed Nvidia's $26 billion spending on open-model builders as manufacturing demand for chips rather than altruistic backing of open source.”

“Author discussing open-source AI's uncertain future as Nvidia invests in demand creation while open models potentially fork toward efficiency and specialization.”

“Analyzed GLM-5.3 model release and how Chinese labs keep pace with frontier AI capabilities through post-training and RL optimization.”

“Byline author analyzing how Kimi K3 open-weight model changes economics of frontier AI labs.”

“Wrote detailed teardown of Moonshot AI's Kimi K3 announcement analyzing model capabilities and Chinese AI ecosystem efficiency.”

“Argued that open models had about six months to live before sharing Inkling, a 975B parameter open-weight model.”

“AI researcher who warned that broad restrictions spurred by distillation panic will hurt smaller AI companies and academic researchers.”

“Predicted US ban or stall of open-weight models above GPT-5.5 tier within six months and analyzed distillation concerns versus frontier capability regulation.”

“Previously documented job search timeline that inspired Alisa Liu's detailed job search analysis.”

“Critiques Anthropic's Claude Fable release for silent model manipulation and uneven safety domain application, calling for more open access to AI research.”

“Criticized Anthropic's silent safety modifications to Claude Fable 5 for frontier LLM development as misaligned and eroding trust.”

“Lambert led post-training at Ai2 and built OLMo and Tulu, two prominent open-source model releases, before leaving after 2.5 years.”

“Lambert wrote about open and closed model economics and predicted that open models will eventually catch up to closed models on out-of-distribution tasks.”

“Nathan Lambert wrote an article about his visits to leading AI labs in China, discussing cultural differences in how Chinese and American researchers approach building language models.”

“Clem referenced a conversation with Nathan Lambert about whether open models will catch up to closed models.”

“Authored article on inevitable need for open model consortium to sustain frontier AI development.”

“Nathan Lambert is a byline author on The ATOM Report measuring the open language model ecosystem and arguing that Chinese models overtook U.S. counterparts.”

“Wrote article analyzing open models strategy and the gap between open and closed AI systems.”

“Journalist who reviewed GPT 5.4 and its agentic capabilities, comparing it to Claude.”

Open models will never catch up with closed frontier systems in raw capability, but this is the wrong framing—open models serve as the engine for exploratory AI research that companies cannot nurture.

“Argues that open models will never catch up with closed ones but believes they will be the engine for the next ten years of AI research.”