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Logan Kilpatrick: Windsurf Acquisition, Gemini 3, Agentic Browsing, VO4, and More!

Sofia Alvarez
Logan Kilpatrick Windsurf Acquisition, Gemini 3, Agentic Browsing

Logan Kilpatrick Windsurf Acquisition, Gemini 3, Agentic Browsing

In the rapidly evolving world of artificial intelligence, staying ahead of the curve means understanding not just the technology itself, but also the ecosystem surrounding it — from startup acquisitions and developer tools to new AI form factors and the future of browsing. Logan Kilpatrick, a prominent figure at DeepMind, recently shared deep insights into these very topics in a detailed conversation with Matthew Berman. This article delves into Logan’s perspectives on the Windsurf acquisition, the rise of AI-powered developer tools, the evolution of AI interfaces, the future of web interaction, and the exciting advancements in generative media models like VO3 and the forthcoming VO4.

Let’s explore the cutting edge of AI through Logan’s eyes and uncover what the future holds for developers, creators, and users alike.

Table of Contents

🛠️ Windsurf Acquisition: A Strategic Talent Play for DeepMind

One of the earliest topics Logan discussed was the Windsurf acquisition by Google, where a subset of the Windsurf team joined DeepMind directly, while the product and IP went to another company called Cognition. This move was primarily a talent acquisition rather than a product buyout.

Logan reflected on the “arc and pendulum” of developer plus AI, noting how the initial excitement around AI-powered developer tools, like GitHub Copilot, seemed to plateau before reigniting in the past year and a half. The Windsurf team’s success in building developer-loved products highlighted the enormous potential in the developer space for AI innovation.

“It’s a great product. I personally am a user. I love using Windsurf. It’s in my rotation similar to how developers switch between chat apps.” — Logan Kilpatrick

He emphasized that DeepMind’s growing focus on developer tools is a significant shift from its foundational research roots. The organization is now explicitly investing in products that developers love, which aligns with Logan’s passion for building developer-centric AI solutions.

However, the trend of acquiring teams but leaving the original product independent raises questions about the broader startup ecosystem. Logan expressed concern that if startup founders and early employees find no path to building bigger companies or going public, this could negatively impact the ecosystem. Nonetheless, he remains bullish on AI startups and the opportunities they present.

🕶️ The Best AI Form Factor: A Blend of Glasses, Screens, and Voice

When it comes to the ultimate AI assistant interface for consumers, Logan believes there will never be a “one size fits all” solution. Instead, the future will see a mix of form factors, each suited to different contexts and user needs.

He traced the evolution from chat-based interactions to voice and asynchronous agents that work in the background. Logan imagines a UI where users have a “queue” of agent actions needing their approval, much like reviewing pull requests in software development. This approach offers a new paradigm beyond chat and voice, allowing users to stay in control while benefiting from AI assistance.

Regarding glasses, Logan acknowledges the excitement around smart glasses as a form factor but points out their limitations. Most productive work happens on computer screens, where AI can already access the full context of what’s visible on the screen with user permission. He also highlights that not everyone will want to wear glasses all day, and future solutions might include AI-enabled contact lenses or other innovations.

Voice remains an essential interaction layer, particularly in scenarios where hands-free communication is necessary, such as driving. Logan shares experiences of using AI assistants in cars to facilitate conversations, answer questions, and manage tasks like booking reservations, emphasizing voice’s natural fit in these contexts.

Yet, voice has its limits. For example, reviewing detailed code changes in a car is impractical. Logan believes that AI’s context awareness can help by delaying certain notifications or tasks until the user is in an appropriate environment, such as their desktop at home.

🌐 The Future of Web Browsing: Humans Still in the Driver’s Seat

With the rise of AI browsers and agentic assistants, the interaction between humans and the internet is poised for transformation. Logan shares a nuanced view, emphasizing that while AI can summarize vast amounts of content, humans crave authentic content created by other humans.

He points out that as AI-generated content becomes easier to produce, the value of authenticity and authoritative perspectives grows. This dynamic makes search engines like Google Search even more critical in filtering and surfacing credible information.

Logan also highlights new categories of internet engagement enabled by AI, such as deep research. He recounts a personal story of shopping for a gift, where visiting twenty different websites was overwhelming. AI-assisted research that aggregates and analyzes hundreds of pages could save users tremendous time and effort, creating entirely new user actions that were previously impractical.

Despite these advances, Logan stresses that humans want to remain in control, making decisions themselves rather than fully delegating to AI. This balance between assistance and agency defines the future of web interaction.

Travel planning is a prime example. While a complex multi-person, multi-destination itinerary might benefit greatly from an AI travel agent, simpler, routine bookings may not see significant speed gains. Still, Logan envisions a future where AI can handle repeat bookings with minimal user input, freeing up time for more valuable tasks.

🤖 Models Becoming Systems: The Shift from Scaffolding to Native Reasoning

One of the most exciting trends in AI development is the evolution of language models from standalone predictors to integrated systems or agents. Logan explains that early AI deployments required significant scaffolding — layers of orchestration, guardrails, and tool integrations — to deliver reliable results in production.

However, as models improve, many companies are simplifying their stacks by reducing reliance on external scaffolding. For example, Notebook Eleven has cut down its orchestration workflow from 15 steps to just 4 or 5, thanks to advances in model capabilities.

Reasoning models are a key driver of this shift. These models can autonomously call external tools like search engines, code execution environments, and function calls during their reasoning process, effectively acting as agents out of the box. Logan predicts that the boundary between models and agents will blur as native capabilities expand.

For startup founders, this presents both opportunities and challenges. While it’s tempting to build extensive scaffolding today, one must anticipate that models will soon absorb many of these capabilities natively. Being nimble and ready to adapt to evolving model strengths is crucial.

👩‍💻 The Future of Engineering: More Engineers, Not Fewer

Contrary to popular fears that AI will replace engineers, Logan is extremely bullish on the number of engineers increasing over time, driven by AI’s ability to make each engineer more productive.

He believes that learning to code remains a fundamental skill, not just for writing instructions but for problem-solving, systems thinking, and perseverance. The ability to understand and tweak code is essential for wielding AI tools effectively, especially for advanced tasks.

Logan shares that many current AI coding tools don’t yet bring users along in the learning process. Instead of doing everything for the user, there’s a tremendous opportunity to build products that educate and empower users to become better developers themselves.

This vision aligns with the goal of bringing the next hundred million developers into the ecosystem by thoughtfully exposing the right details and educational content during the AI-assisted development process.

Regarding natural language as the source of truth for programming, Logan is cautious. While translating natural language specs into tested code is an exciting area, the deterministic nature of code remains critical for building reliable, large-scale systems. For serious software development, direct control over code is indispensable.

🎥 VO3 and the Future of Generative Media

The generative media space is exploding, and Google’s VO3 model is at the forefront. Announced at Google I/O, VO3 introduced native audio in video generation, significantly enhancing realism and immersion. Tens of millions of videos have already been created using VO3, garnering billions of views.

Logan highlights how VO3 lowers the barrier to content creation, similar to how AI-assisted coding lowers the entry threshold for software development. This democratization does not diminish the value of expert creators but instead offers them powerful tools to accelerate their work.

Google’s internal creative tools, like Flow and WISC, have been instrumental in helping artists harness VO3’s capabilities. Now, with VO3 available via API, developers worldwide can build innovative creative tools and applications beyond Google’s initial offerings.

Looking ahead, Logan mentions plans to address cost and video length. A faster, more affordable VO3 model is expected soon, maintaining comparable quality. Longer video generation remains a challenge due to coherence issues, but iterative creative workflows mimic human video production and hold promise for improvement.

VO3’s evolution parallels the broader scaling laws in AI, with advancements in image generation (like Imagine 4) complementing progress in large language models (LLMs). This multi-model progress fuels new possibilities in generative media and beyond.

⚡ Diffusion Models and AI Architecture Futures

Logan touched on the diffusion model architecture, which Google introduced recently. While diffusion models offer super-fast generation, they currently trade off some quality for speed. The big question is whether diffusion can scale as effectively as transformer-based models.

Diffusion models have shown strong performance in specific domains, such as code generation, and could enable revolutionary user experiences like dynamically generated UI pages. However, scalability and cost remain open questions.

Speed is an often underappreciated but critical factor. Faster models enable tighter iteration loops, keeping developers in a productive “flow state.” Logan relates to this personally, sharing his experience of waiting for model responses during vibe coding sessions.

🚀 Gemini 3 and Multi-Dimensional Scaling

Gemini 3 remains under wraps, but Logan offers some clues about the direction DeepMind is taking. The key insight is that AI model scaling is no longer one-dimensional. Instead, there are three interconnected axes:

Gemini 2.5 demonstrated the power of scaling across all three dimensions simultaneously, leading to multiplicative gains. Logan emphasizes that Google continues to invest heavily in all three areas to maximize this effect.

When asked about desired features for Gemini 3, Logan expressed interest in faster speed and more built-in tools, especially for AI studio environments, to reduce the need for coding while maintaining power and flexibility.

🤝 Competition and Google’s Unique Position

In a landscape crowded with formidable players like OpenAI, Anthropic, and XAI, Google’s competitive edge lies in its breadth and depth of research and product integration.

Logan reminds us that competition in AI is fundamentally about making the world a better place, and many of the key players are friends and collaborators in this mission. Google’s strength comes from DeepMind’s diverse research portfolio, including Nobel Prize-winning work outside of LLMs, which feeds back into improving AI models.

Moreover, Google’s scale is unparalleled, with AI capabilities being deployed to billions of users across multiple products. This scale demands robust, efficient, and cost-effective solutions, where Google’s proprietary TPU chips offer a significant advantage over competitors reliant solely on Nvidia hardware.

Logan personally treasures his TPU chips and highlights how controlling the silicon-to-software stack enables Google to deliver AI at scale, often for free, to a global audience, democratizing access to advanced AI technologies.

📚 Conclusion: Embracing AI’s Future with Agency and Authenticity

Logan Kilpatrick’s insights paint a vivid picture of an AI-powered future that balances innovation with human agency, authenticity, and taste. From the Windsurf acquisition highlighting the importance of developer tools, to evolving AI form factors blending glasses, voice, and screens, the landscape is rich with opportunity.

The future of web browsing and content creation will be reshaped by AI, but humans will remain at the helm, craving authentic, human-built experiences. Models are becoming more integrated systems, reducing scaffolding while increasing native reasoning capabilities.

Contrary to dystopian fears, the engineering workforce is poised to grow as AI empowers developers to be more productive and creative. Generative media models like VO3 are democratizing creativity, while diffusion models and multi-dimensional scaling promise exciting architectural advances.

Finally, Google’s unique position, powered by DeepMind’s research and TPU hardware, ensures it will remain a formidable player in shaping AI’s trajectory.

As we move forward, embracing AI’s power while maintaining human control and authenticity will be the key to unlocking its full potential.

❓ Frequently Asked Questions (FAQ)

What was the Windsurf acquisition about?

The Windsurf acquisition was primarily a talent acquisition by Google’s DeepMind, bringing in a skilled team focused on AI-powered developer tools. The product and IP went to another company, Cognition.

Is there a single best form factor for AI assistants?

No, the future will involve a mix of form factors including chat, voice, glasses, and asynchronous agents. Each has strengths depending on the context, like voice being essential while driving and screens being better for detailed work.

Will AI replace software engineers?

Logan believes AI will make engineers more productive and increase the number of engineers over time. Learning to code and systems thinking remains crucial for leveraging AI tools effectively.

What is the future of web browsing with AI?

AI will augment web browsing by summarizing and researching content, but humans will still want authentic, human-created content. New categories like deep research will emerge, changing how we engage with the internet.

What is VO3 and why is it important?

VO3 is Google’s generative video model that integrates native audio, dramatically enhancing video realism. It lowers barriers to creative content production and is now available via API for developers to build new tools.

How do diffusion models compare to transformers?

Diffusion models offer faster generation but currently at lower quality. Research is ongoing to determine if diffusion can scale like transformers, with promising use cases such as code generation and dynamic UI creation.

What is Gemini 3 and what can we expect?

Gemini 3 is the next iteration of DeepMind’s AI models, focusing on multi-dimensional scaling across pre-training, post-training, and reasoning. It aims to be faster and more capable, with enhanced tools for developers.

How does Google’s TPU hardware give it an advantage?

Google’s TPUs allow it to control the entire hardware-software stack, enabling efficient, large-scale AI deployments. This reduces dependence on third-party chips and helps deliver AI services cost-effectively to billions of users.

 

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