Google’s Jeff Dean on the Coming Era of Virtual Engineers

Jeff Dean makes a bold prediction: we will have AI systems operating at the level of junior engineers within a year.

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At AI Ascent 2025, Google's Chief Scientist Jeff Dean offered a glimpse into the future of AI, drawing on his deep experience since joining Google in 1999. In conversation with Sequoia partner Bill Coughran, who previously ran engineering at Google, Jeff shared insights on AI's evolution, the hardware powering it, and where the technology is heading next.

The Long Arc of AI Development

Despite AI's sudden prominence in public consciousness over the past few years, Jeff emphasized that this revolution has been building for over a decade. He traced the journey back to 2012-2013, when researchers discovered that large neural networks could solve complex problems in vision, speech, and language using similar algorithmic approaches.

"We trained a neural network that at the time was 60x larger than anything else, and we used 16,000 CPU cores, because that's what we had in our data centers and got really good results," Jeff recalled. "That really cemented in our mind that scaling these approaches would really work well."

This early insight led to Google's guiding principle: "Bigger model, more data, better results"—an approach that has proven remarkably consistent for the past 12-15 years.

The Future of AI Capabilities

Jeff painted a picture of AI's expanding capabilities, driven by several key factors:

1. Scale and Hardware: Larger models with more data continue to deliver better results, supported by specialized hardware like Google's TPUs.

2. Algorithmic Improvements: Better training techniques enable more capable models with the same compute costs.

3. Multimodality: The ability to process and generate multiple forms of content—audio, video, images, text, and code—creates versatile systems.

4. Reinforcement Learning: Post-training approaches are making models better and guiding them toward desired behaviors.

Agents: Promise vs. Reality

When Bill provocatively called some current agent technologies "vaporware," Jeff acknowledged the gap between today’s capabilities and future potential while remaining optimistic.

"I do see a path for agents with the right training process to eventually be able to do many, many things in the virtual computer environment that humans can do today," Jeff explained. The roadmap involves more reinforcement learning, increased agent experience, and early products that solve specific problems.

Jeff extended this vision to physical robots, predicting that within a year or two, robots will begin performing useful tasks in messy environments—starting with expensive products that can do 20 things, then being cost-engineered to devices that can do 1,000 things at a fraction of the price.

The Future of Large Models

On the competitive landscape of large language models, Jeff was pragmatic: "It takes quite a lot of investment to build the absolute cutting edge models. And I think there won't be 50 of those. There may be like a handful."

He highlighted knowledge distillation (a technique he co-authored with Geoffrey Hinton that was initially rejected from NeurIPS 2014 as "unlikely to have impact") as a method that allows smaller, lighter-weight models to benefit from larger ones—potentially enabling a diverse ecosystem of specialized models alongside a few dominant general-purpose systems.

Hardware's Critical Role

Jeff discussed the critical importance of specialized AI hardware, having helped bootstrap Google's TPU program in 2013. "Accelerators for reduced precision linear algebra are what you want, and you want them to be better and better generation over generation," he explained.

The ideal infrastructure connects these accelerators with high-speed networking to distribute model computation across many devices. Google's latest TPU iteration, codenamed "Ironwood," is about to be released, continuing their hardware evolution.

AI's Scientific Impact

When asked about AI's growing influence in science—highlighted by recent Nobel Prizes awarded to AI researchers—Jeff was enthusiastic about the technology's transformative potential.

"AI is influencing lots of different kinds of science," he noted. One particularly powerful application is using neural networks to approximate expensive computational simulators used in fields like weather forecasting, fluid dynamics, or quantum chemistry.

"Often what you can do is use those simulators as training data for a neural net and then build something that approximates the simulator, but now is 300,000 times faster," Jeff explained. "That just changes how you do science... I'm going to go to lunch and screen 10 million molecules. That's now possible."

The Future of Computing Infrastructure

Looking at computing's future, Jeff described a fundamental shift in what we want computers to do: "It's pretty clear now that you want to run incredibly large neural networks at incredibly high performance and incredibly low power."

This creates distinct needs for training versus inference, with specialized hardware solutions for each. The computing landscape will adapt to power models across different environments:

• Low-power devices like phones running sophisticated local models

• Robots and autonomous vehicles with specialized hardware

• Data centers operating at massive scale

• Selective application of intensive compute resources—using "10,000 times as much compute for some problems as for others"

The Coming AI Workforce

Perhaps most startlingly, Jeff predicted we're only about a year away from having AI systems that can operate 24/7 at the level of a junior software engineer. When pressed on what advancements would enable this, he explained these systems would need more than code generation—they'll need to run tests, debug performance issues, and use various tools.

"Junior virtual engineer is going to be pretty good at reading documentation and sort of trying things out in virtual environments," Jeff noted. "I don't know how far it’ll take us, but it seems like it'll take us pretty far."

The Path Forward

Jeff's vision for future AI systems involves more organic, continuous learning approaches than today's rigid models. He described a preference for "sparse" models with different areas of expertise—similar to how human brains efficiently allocate resources.

"From our weak biological analogies, that's partly how our real brains get so power efficient... our Shakespeare poetry part is not active when we're worried about the garbage truck backing up at us in the car," Jeff explained.

He envisions models with varying computational paths—some 100-1000x more expensive than others—with the ability to extend with new parameters, compact underused sections through distillation, and reallocate resources dynamically.

While acknowledging the effectiveness of current approaches makes radical change difficult, Jeff believes these more organic, adaptable systems represent an important future direction for AI.

As computing power increases and algorithms improve, Jeff's insights suggest we're entering an era where AI will transform not just technology companies but education, scientific discovery, and the nature of work itself—perhaps sooner than many of us realize.