The primary threat from competitors like Google may not be a superior model, but a more cost-efficient one. Google's Gemini 3 Flash offers "frontier-level intelligence" at a fraction of the cost. This shifts the competitive battleground from pure performance to price-performance, potentially undermining business models built on expensive, large-scale compute.

Google AI leader Jeff Dean highlighted "continual learning"—a model's ability to learn from new inputs post-training—as a key step toward AGI. That leaders are discussing it publicly suggests a breakthrough is near, which could rapidly accelerate AI capabilities and lead to a "fast takeoff" scenario.

The concept that AIs can build better AIs, creating an accelerating feedback loop, is no longer theoretical. Leaders from Anthropic, OpenAI, and Google DeepMind have publicly confirmed they are actively using current AI models to develop the next generation, making RSI a practical engineering pursuit.

Silicon Valley insiders, including former Google CEO Eric Schmidt, believe AI capable of improving itself without human instruction is just 2-4 years away. This shift in focus from the abstract concept of superintelligence to a specific research goal signals an imminent acceleration in AI capabilities and associated risks.

Beyond just coding, improving AI models requires subtle skills like designing effective reinforcement learning environments or managing human expert feedback. Newman questions how close we are to recursive self-improvement by asking if AIs can automate these tasks, which rely on nuanced "taste and judgment" rather than just raw computational ability.

Companies like OpenAI and Anthropic are not just building better models; their strategic goal is an "automated AI researcher." The ability for an AI to accelerate its own development is viewed as the key to getting so far ahead that no competitor can catch up.

Google's new state-of-the-art Deep Research agents are still powered by the older Gemini 3.1 Pro model. Their significant performance improvements come entirely from 'harness upgrades' and additional inference techniques. This demonstrates that the systems, tools, and processes surrounding a model are now a primary driver of capability, not just the raw power of the base model itself.

The viral claim of "recursive self-improvement" is overstated. However, AI is drastically changing the work of AI engineers, shifting their role from coding to supervising AI agents. This automation of engineering is a critical precursor to true self-improvement.

Google's new AI coding "Strike Team," with personal involvement from Sergey Brin, is focused on improving its models for internal Google engineers first. The goal is to create a feedback loop where AI helps build better AI, a concept Brin calls "AI takeoff," treating any friction in this process as a top-priority blocker for achieving AGI.