How AI Is Building Smarter Versions of Itself—and What That Means for the Future

Meta's Ambitious AI Goals

Mark Zuckerberg recently announced Meta's objective to develop AI systems smarter than humans. His approach starts with attracting top talent, reportedly offering nine-figure packages to researchers at Meta Superintelligence Labs. The second key element is leveraging AI to improve itself.

Meta’s focus is on creating self-improving AI capable of enhancing its performance autonomously. Unlike other technologies such as CRISPR or fusion reactors, AI systems—especially large language models (LLMs)—can optimize their own hardware, train new models efficiently, and potentially generate original research ideas.

Zuckerberg envisions this self-improvement freeing humans from routine tasks, enabling them to pursue higher goals with AI companions. However, this feedback loop also carries risks. AI could accelerate development of harmful capabilities like hacking or weapon design, raising concerns about an intelligence explosion far beyond human control.

Major AI organizations like OpenAI, Anthropic, and Google acknowledge these risks in their safety frameworks. Experts like Jeff Clune from Google DeepMind consider automated AI research the fastest path to powerful AI and stress its importance. While humans still drive AI progress, AI is increasingly contributing to its own advancement.

Five Ways AI is Making Itself Better

1. Enhancing Productivity

One of the simplest but most impactful contributions of LLMs is coding assistance. Tools like Claude Code and Cursor help engineers write software faster. Google CEO Sundar Pichai noted that AI generated 25% of the company’s new code in late 2024. These assistants could speed up AI system development by increasing researcher productivity.

However, evidence is mixed. A METR study found that developers might take 20% longer using AI coding tools due to error correction. The impact on AI researchers, who often write short scripts, remains unclear. More targeted studies could clarify whether these tools truly accelerate frontier AI work.

2. Optimizing Infrastructure

Training LLMs is slow and costly, making infrastructure optimization critical. Azalia Mirhoseini of Stanford and Google DeepMind has used AI to design efficient chip layouts, with Google applying these designs in their custom AI chips.

Google’s AlphaEvolve system uses its Gemini LLM to iteratively improve algorithms for data center operations and chip kernel functions, achieving small but significant savings in computational resources and training speed. Though improvements like a 1% speedup might seem minor, they translate into substantial savings at scale.

3. Automating Training

LLMs require vast and diverse data for training, but real-world examples aren’t always available. AI can generate synthetic data to fill gaps, especially in niche areas. Techniques like “LLM as a judge” replace costly human feedback with AI evaluations to guide training, as seen in Anthropic’s Constitutional AI framework.

For AI agents that need multi-step problem-solving skills, synthetic generation and evaluation of task steps by LLMs allow creation of unlimited training experiences, addressing data scarcity and accelerating learning.

4. Perfecting Agent Design

While the core transformer architecture of LLMs remains human-designed, LLM agents that interact with tools and environments open new design possibilities. Jeff Clune and colleagues developed a “Darwin Gödel Machine” that iteratively modifies its own prompts and code to improve tasks, discovering novel enhancements beyond its initial design.

This demonstrates a true self-improvement loop where AI agents can evolve themselves without human intervention, exploring design spaces humans have yet to fully map.

5. Advancing Research

Human creativity and judgment still play a major role in AI research, particularly in selecting promising directions. However, AI systems like Clune’s “AI Scientist” are beginning to autonomously identify research questions, run experiments, and write papers. One such paper was accepted at a respected conference workshop, showcasing AI’s potential in scientific discovery.

The AI Scientist has even proposed ideas independently echoed by human researchers, signaling progress toward AI systems that can contribute meaningfully to research literature.

Is Superintelligence on Its Way?

Self-improving AI contributions will likely grow in the near future. Zuckerberg suggests superintelligent models are approaching, but actual impacts remain uncertain. Early gains like AlphaEvolve’s 1% training speedup show potential yet reflect a slow feedback loop.

Incremental improvements can compound, with each generation faster and more capable. Still, innovation often becomes harder as fields mature, possibly limiting rapid breakthroughs once initial easy gains are exhausted.

Measuring AI progress is challenging since frontier models are often proprietary. METR tracks AI capabilities by timing human task completion versus AI autonomy, finding that task complexity handled by AI has doubled every seven months since 2019, accelerating to every four months since 2024. This suggests AI development pace is increasing.

While increased investment partly explains this acceleration, AI's role in boosting research productivity could be significant. If AI takes on more research tasks, progress may speed up further. The big question is how long this acceleration will last and how it will reshape the research landscape.

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