Due Diligence

“I feel like the [ AI ] problems are tractable, they’re surmountable, but they’re still difficult. If I just average it out, it just feels like a decade [ to AGI ] to me.” – Andrej Karpathy – Ex-OpenAI, Ex-Tesla AI

Andrej Karpathy’s reflection—“I feel like the [ AI ] problems are tractable, they’re surmountable, but they’re still difficult. If I just average it out, it just feels like a decade [ to AGI ] to me.”—encapsulates both a grounded optimism and a caution honed through years at the forefront of artificial intelligence research. Understanding this statement requires context about the speaker, the evolution of the field, and the intellectual landscape that shapes contemporary thinking on artificial general intelligence (AGI).

Andrej Karpathy: Technical Leadership and Shaping AI’s Trajectory

Karpathy is recognised as one of the most influential figures in modern AI. With a doctorate under Geoffrey Hinton, the so-called “godfather” of deep learning, Karpathy’s early career put him at the confluence of academic breakthroughs and industrial deployment. At Stanford, he helped launch the seminal CS231n course, which became a training ground for a generation of practitioners. He subsequently led critical efforts at OpenAI and Tesla, where he served as Director of AI, architecting large-scale deep learning systems for both language and autonomous driving.

From the earliest days of deep learning, Karpathy has witnessed—and helped drive—several “seismic shifts” that have periodically redefined the field. He recalls, for example, the transition from neural networks being considered a niche topic to their explosive relevance with the advent of AlexNet. At OpenAI, he observed the limitations of reinforcement learning when applied too soon to general agent-building and became an early proponent of focusing on practical, useful systems rather than chasing abstract analogies with biological evolution.

Karpathy’s approach is self-consciously pragmatic. He discounts analogies between AI and animal evolution, preferring to frame current efforts as “summoning ghosts,” i.e., building digital entities trained by imitation, not evolved intelligence. His career has taught him to discount industry hype cycles and focus on the “march of nines”—the painstaking work required to close the gap between impressive demos and robust, trustworthy products. This stance runs through his entire philosophy on AI progress.

Context for the Quote: Realism amidst Exponential Hype

The statement about AGI’s timeline emerges from Karpathy’s nuanced position between the extremes of utopian accelerationism and excessive scepticism. Against a backdrop of industry figures claiming near-term transformative breakthroughs, Karpathy advocates for a middle path: current models represent significant progress, but numerous “cognitive deficits” persist. Key limitations include the lack of robust continual learning, difficulties generalising out-of-distribution, and the absence of key memory and reasoning capabilities seen in human intelligence.

Karpathy classifies present-day AI systems as “competent, but not yet capable agents”—useful in narrow domains, such as code generation, but unable to function autonomously in open-ended, real-world contexts. He highlights how models exhibit an uncanny ability to memorise, yet often lack the generalisation skills required for truly adaptive behaviour; they’re powerful, but brittle. The hard problems left are not insurmountable, but solving them—including integrating richer memory, developing agency, and building reliable, context-sensitive learning—will take sustained, multi-year effort.

AGI and the Broader Field: Dialogue with Leading Theorists

Karpathy’s thinking exists in dialogue with several foundational theorists:

• Geoffrey Hinton: Pioneered deep learning and neural network approaches that underlie all current large-scale AI. His early conviction in neural networks, once seen as fringe, is now mainstream, but Hinton remains open to new architectural breakthroughs.

• Richard Sutton: A major proponent of reinforcement learning as a route to general intelligence. Sutton’s vision focuses on “building animals”—systems capable of learning from scratch via trial and error in complex environments—whereas Karpathy now sees this as less immediately relevant than imitation-based, practically grounded approaches.

• Yann LeCun: Another deep learning pioneer, LeCun has championed the continuous push toward self-supervised learning and innovations within model architecture.

• The Scaling Optimists: The school of thought, including some in the OpenAI and DeepMind circles, who argue that simply increasing model size and data, within current paradigms, will inexorably deliver AGI. Karpathy explicitly distances himself from this view, arguing for the necessity of algorithmic innovation and socio-technical integration.

Karpathy sees the arc of AI progress as analogous to general trends in automation and computing: exponential in aggregate, but marked by periods of over-prediction, gradual integration, and non-linear deployment. He draws lessons from the slow maturation of self-driving cars—a field he led at Tesla—where early demos quickly gave way to years of incremental improvement, ironing out “the last nines” to reach real-world reliability.

He also foregrounds the human side of the equation: as AI’s technical capability increases, the question becomes as much about organisational integration, legal and social adaptation, as it does about raw model performance.

In Summary: Surmountable Yet Difficult

Karpathy’s “decade to AGI” estimate is anchored in a sober appreciation of both technical tractability and practical difficulty. He is neither pessimistic nor a hype-driven optimist. Instead, he projects that AGI—defined as machines able to deliver the full spectrum of knowledge work at human levels—will require another decade of systematic progress spanning model architecture, algorithmic innovation, memory, continual learning, and above all, integration with the complex realities of the real world.

His perspective stands out for its blend of technical rigour, historical awareness, and humility in the face of both engineering constraints and the unpredictability of broader socio-technical systems. In this, Karpathy situates himself in conversation with a lineage of thinkers who have repeatedly recalibrated the AI field’s ambitions—and whose own varied predictions continue to shape the ongoing march toward general intelligence.