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Britain's most capable coding model can't be exported, and that ban is the whole reason Cosine set out to build one from scratch. Alistair Pullen, CEO and co-founder of Cosine, sits down with Tim Scarfe to explain how a frontier system he calls Fable, locked behind US export controls, became the founding case for a UK sovereign model trained on the Isambard supercomputer in Bristol.
The bet underneath it is economic. Pullen argues that an inference company, rather than a training-first lab, doesn't need billions to compete: millions, a national compute allocation, and a consortium feedback loop can be enough. From there it gets into the machinery, why open-weight models still trail the frontier on size, active parameters and data, the mixture-of-experts versus dense trade-off and why active params dominate how a model actually feels, and the edge that real coding trajectories confer.
The back half is about making agents trustworthy. Pullen makes the case for beating "slop" by rewarding the process instead of the final answer, reframes code review as runtime proof (spin the bug up in a VM and force the agent to actually exploit it), and walks through Swarm, Cosine's system running hundreds of sub-agents in one shot. It ends on why memory is still an unsolved hack, how synthetic graders let you run RL on tasks with no built-in test, and why Pullen reads US export controls as an accidental gift, with a supply-chain sting in the tail.
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TIMESTAMPS:
00:00:00 The sovereign mandate and the Fable ban
00:04:02 Millions vs billions: the inference-company model
00:07:19 The consortium feedback loop
00:07:40 Why open models lag the frontier
00:14:59 MoE vs dense, and why active params matter
00:16:29 Trajectories: the process-data advantage
00:19:48 Beating slop: reward the process, not the answer
00:26:06 Reusable abstractions and the epistemic wall
00:29:56 Code review becomes runtime proof
00:37:32 Do agentic harnesses still matter?
00:40:35 Swarm: orchestrating hundreds of sub-agents
00:45:14 Why memory is still unsolved
00:48:25 Synthetic data and graders for RL
00:53:09 The US export gift and supply-chain risk
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REFERENCES:
organization:
[00:01:15] Cosine
https://cosine.sh
[00:04:14] Mistral AI
https://mistral.ai
[00:05:50] Anthropic
https://www.anthropic.com
[00:07:42] Cohere
https://cohere.com
[00:08:36] DeepSeek
https://www.deepseek.com
tool:
[00:02:52] Isambard-AI
https://isambard.ac.uk
[00:05:56] Colossus (xAI)
https://en.wikipedia.org/wiki/Colossus_(supercomputer)
[00:07:52] GLM (Z.ai)
https://z.ai
[00:11:52] NVIDIA B300
https://www.nvidia.com/en-us/data-center/dgx-b300/
[00:15:37] gpt-oss-120b
https://huggingface.co/openai/gpt-oss-120b
[00:15:52] Devstral 2
https://mistral.ai/news/devstral
[00:16:01] Llama 70b
https://www.llama.com
[00:17:05] Claude Code
https://www.anthropic.com/claude-code
[00:26:23] ARC-AGI (Francois Chollet)
https://arcprize.org
[00:40:38] Swarm (Cosine)
https://cosine.sh
[00:40:50] OpenAI Codex
https://github.com/openai/codex
[00:41:16] Lumen Outpost (Cosine)
https://cosine.sh
[00:41:18] Kimi K2 (Moonshot)
https://huggingface.co/moonshotai/Kimi-K2-Instruct
[00:49:55] SWE-bench
https://www.swebench.com
[00:52:40] SystemVerilog
https://en.wikipedia.org/wiki/SystemVerilog
person:
[00:23:40] Andrej Karpathy
https://karpathy.ai
paper:
[00:27:10] GRPO (DeepSeekMath)
https://arxiv.org/abs/2402.03300
[00:27:13] GSPO
https://arxiv.org/abs/2507.18071
Incompressible Knowledge Probes, Bojie Li
https://arxiv.org/pdf/2604.24827
Estimating the Size of Claude Opus 4.5/4.6
https://unexcitedneurons.substack.com/p/estimating-the-size-of-claude-opus
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ReScript:
https://app.rescript.info/session/5852d2b884c4ce4b?share=10b9799160845bb11779f8ac6cd3124f
