A 48-hour run of meetings in San Francisco with billionaire investors, data labs and AI founders surfaced a set of sharp industry signals: a booming market of data brokers selling company data to labs, startups chasing hyper-fast revenue growth that may mask terrible retention, and a broad sense that computing is being reinvented with large models as the CPU, agent harnesses as the operating system, and skills/plugins as apps. Practical timelines feel short - agents that can observe a week of work and perform tasks autonomously look possible within a year - and teams are treating models as swappable components routed to different jobs and evaluated by real user behavior rather than model-only metrics.
That shift produces concrete opportunities and risks. Vertical agent harnesses require connectors, domain-specific evals, safety rules and human review, and companies that build primitives (APIs, data layers, agent tools) will win. Easy cloning and reverse-engineering make software replaceable, so durable value migrates to customer data, agent failure logs and long-term memory; messy internal data creates a big cleanup/consulting market. Physical deployment, cybersecurity, agent insurance, monetizing an “agent web,” and the scarcity of distribution and attention are immediate battlegrounds. Hiring people who can judge model outputs (write evals) and thinking about data valuation will determine winners.
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Geoffrey Hinton warns that AI is creating alien-like beings and we have only a few years to adapt to this reality. He criticizes Donald Trump's understanding of superintelligence and emphasizes the need to prioritize human interests. (theguardian.com)
Anthropic suggests its AI may possess consciousness, raising ethical questions about AI feelings and suffering. The development has sparked debate among tech leaders about the moral implications and safety risks of advanced AI systems. (bbc.com)
Self-Evolving Agent Harness revises an AI agent's software components through a feedback-driven process, while keeping model weights fixed. It formalizes continuous validation and updates to improve reliability, safety, and organizational workflows. (arxiv.org)
Reinforcement learning in large language models can cause 'thinking leakage,' where models partially reactivate their original reasoning abilities rather than learning new behaviors. This leakage explains much of the observed performance improvements in models trained with RL in non-thinking modes. (arxiv.org)
Sergey Brin states that Google's AI models are entering a phase of recursive self-improvement, where AI helps develop better AI systems. This process could accelerate AI development significantly within the next year, according to industry experts. (businessinsider.com)
Linus Torvalds expressed a cautious stance on using AI for coding, emphasizing that understanding and effort are more important than the tools used. He believes people should do their best and understand their work regardless of the technology involved. (lore.kernel.org)
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