In the fast-paced world of startups, maintaining relevance requires constant innovation and adaptation. As Varun Mohan explains, startups must continually reinvent themselves to stay ahead.

"One of the things that I think is true for any startup is you have to keep proving yourself. Every single insight that we have is a depreciating insight."

Varun illustrates this principle with the example of Nvidia, noting that even industry leaders must constantly innovate to maintain their position. He emphasizes that he's comfortable with being wrong about insights because stagnation means slow death for a company.

The conversation introduces the evolving concept of developers transitioning to what Varun calls "builders," suggesting that software development is becoming increasingly democratized and accessible to everyone.

Timestamp: [0:00-0:30]

The conversation begins with an overview of Windsurf's current position in the market. Varun provides concrete metrics to illustrate the company's growth and adoption.

Windsurf has achieved impressive user adoption metrics: over a million developers have used the product, with hundreds of thousands of daily active users. The platform serves diverse use cases, from modifying large code bases to rapidly building applications from scratch.

Varun emphasizes the excitement around where their technology is heading, indicating confidence in Windsurf's trajectory and future potential.

Timestamp: [0:56-1:15]

Windsurf didn't begin as the coding assistant we know today. Varun reveals the company's origin story, tracing its evolution from a completely different business.

The company was originally founded four years ago as "Exofunction," focusing on GPU virtualization technology. Drawing from their backgrounds in autonomous vehicles and AR/VR, Varun and his co-founder believed deep learning would transform numerous industries including financial services, defense, and healthcare.

Their initial business model paralleled VMware's approach to CPU virtualization, but applied to GPUs instead. By mid-2022, they were managing approximately 10,000 GPUs for several companies and had reached a few million dollars in revenue.

Timestamp: [1:22-2:05]

As transformer models gained prominence in mid-2022, Varun and his team realized their original business model was vulnerable to disruption. This insight prompted them to reconsider their company's direction.

The rise of transformer models like OpenAI's Text-Davinci represented a fundamental shift in the AI landscape. Varun recognized that if everyone gravitated toward using the same type of model architecture, their GPU infrastructure business would quickly become commoditized.

"Because we felt that everyone was going to run these transformer type models, and in a world in which everyone was going to run one type of model architecture—transformers—we thought if we were a GPU infrastructure provider, we would get commoditized."

This realization led to a critical question: Could their existing technology be repurposed to pivot the company in a new direction?

Timestamp: [2:05-2:35]

Facing an existential threat to their business model, Varun and his co-founder made a dramatic decision to pivot their entire company—remarkably, over a single weekend.

"That was a bet-the-company moment. We did it within a weekend."

The pivot process began with a crucial conversation between Varun and his co-founder, acknowledging their business wasn't scalable in its current form. As early adopters of GitHub Copilot, they saw potential in that direction.

On Monday morning, they informed the rest of the company about the pivot, and everyone immediately began working on Kodium, their extension product. This rapid transition highlights the agility and decisiveness that enabled Windsurf's transformation.

Timestamp: [2:35-2:58]

When exploring the details of their pivot, Varun provides context about the company's size and financial position at the time of their dramatic shift.

Despite generating a few million dollars in revenue and being cash flow positive, the company maintained a lean structure with only eight team members. This approach aligned with Y Combinator principles of "ramen profitability" that they had embraced.

In a striking contrast to their operational scale, the company had raised an impressive $28 million—a feat Varun describes as "somehow magical"—during the peak of the zero interest rate environment. At just a year and a half old, they had already secured Series A funding.

"Even though we were making a couple million in revenue, we were kind of like free cash flow positive. It was the peak of zero interest rate at that time, so the company was a year and a half old, we had raised somehow magically $28 million of cash at the time."

Their fundamental realization was that current success wouldn't matter if they couldn't scale the business—necessitating rapid change.

Timestamp: [3:17-3:48]

The interviewer highlights the remarkable prescience of Varun's pivot decision, noting how they accurately foresaw the shift toward foundation models dominating the AI landscape.

Initially, Windsurf had bet on companies building custom deep learning pipelines for specific tasks. By 2022, they recognized a fundamental shift: rather than many specialized models, a single dominant model architecture would emerge that could handle multiple tasks.

This insight came at a critical juncture—the company was generating seven figures in revenue and could have easily raised a Series A round. Instead, they chose to abandon their progress and pivot completely, demonstrating extraordinary conviction in their new vision.

Timestamp: [3:56-4:27]

Varun explains how working with autonomous vehicle companies gave them unique insight into the changing AI landscape, allowing them to identify shifts that would render their original business model obsolete.

Their original thesis assumed that deep learning workloads would expand from autonomous vehicles to other industries like financial services and healthcare. However, witnessing transformer models successfully handling diverse use cases challenged this assumption.

"When we tried even a bad version of GPT-3 like the very old version, we were like, 'This is going to kill sentiment classification. There's no reason why anyone is going to train a very custom model anymore for this task.'"

This revelation made it clear that their fundamental hypothesis was wrong. Varun emphasizes the importance of adapting quickly when market information contradicts your original assumptions.

Timestamp: [4:39-5:36]

When deciding what direction to pivot toward, Varun prioritized finding something that would genuinely excite everyone at the company, recognizing that team motivation would be crucial for success.

As early adopters of GitHub Copilot, they saw its potential but believed it represented just "the tip of the iceberg" of what was possible. Since everyone at the company was a developer, a developer tools approach aligned with their interests and expertise.

Varun acknowledges that historically, developer tools companies haven't performed particularly well, but their limited options made the decision straightforward:

"When you have no other options, it's a very easy decision, right? Like you're going to be a zero with a high probability anyways, you might as well pick something that you think could be valuable and everyone's going to be motivated to work on."

Timestamp: [5:53-6:22]

Facing seemingly insurmountable competition from GitHub Copilot, Varun explains the paradoxical mindset that enabled them to move forward with their pivot.

"Startups require like two distinct beliefs, and they actually kind of run counter to each other. You need this irrational optimism because if you don't have the optimism, you just won't do anything. You're just a pessimist and a skeptic, and those people don't really accomplish anything in life. And you need uncompromising realism, which is that when the facts change, you actually change your mind."

This dual mindset—maintaining optimism while being ruthlessly realistic—creates a challenging balance. The very quality that drives success through optimism can prevent the realism needed to adapt to changing circumstances.

In their case, irrational optimism manifested in the belief that, with their expertise in running and training models, they could create something superior to the competition. They didn't have a detailed roadmap but felt there was significant untapped potential in the space.

"If we couldn't do it, then I guess we'd die, but we might as well bet that we could do it."

Timestamp: [6:54-7:48]

Varun candidly describes their earliest product version, which was inferior to GitHub Copilot but differentiated by being free. This rapid initial deployment demonstrated their agility and commitment to their new direction.

Within just two months of their pivot, the team shipped their first VS Code extension product, launching it on Hacker News. The initial version had significant limitations, particularly in model quality compared to GitHub Copilot.

However, their GPU infrastructure background proved valuable, enabling them to run their inference runtime and provide a free product very quickly. They rapidly improved their training infrastructure and developed their own models tailored specifically to code completion tasks.

"Our earliest version that we shipped out was materially worse than GitHub Copilot. The only difference was it was free."

Timestamp: [7:55-8:24]

Within months of their initial launch, Windsurf developed capabilities that surpassed even GitHub Copilot, demonstrating their rapid innovation and technical expertise.

A key breakthrough was their model's ability to fill in code in the middle of existing code blocks—not just appending code at the end of the cursor. This capability was particularly valuable because such incomplete code patterns differ significantly from the training data used by conventional models.

"Our model could actually fill in the middle of code. So when you're writing code, you're not only just adding code at the end of your cursor, but you're filling it in between two parts, between two parts of a line, right? And that code is very incomplete and looks nothing like the training data of these original models."

By training specialized models for this use case, Windsurf achieved superior quality and lower latency than GitHub Copilot. By early 2023, their autocomplete capabilities had surpassed their primary competitor.

Timestamp: [8:30-9:06]

The interviewer expresses amazement at how quickly Windsurf transitioned from GPU infrastructure to building their own coding models from scratch, highlighting the technical challenge this represented.

Varun explains that their GPU virtualization background gave them a crucial advantage in creating their inference runtime, which enabled them to ship their initial product quickly. However, training their own models was entirely new territory.

Their approach combined hiring smart, capable people with the existential pressure of knowing failure wasn't an option:

"We needed to figure it out. There is no other option, right? Otherwise, you die. Makes the decision really, really simple."

In a remarkably short timeframe, they solved complex challenges around data acquisition, data cleaning, and model training—particularly focusing on handling incomplete code scenarios. Within just two months, they shipped their own model, all with an eight-person team.

Timestamp: [9:12-10:25]

• Successful startups require continuous innovation as all insights depreciate over time
• Windsurf (originally Exofunction) pivoted from GPU virtualization to AI coding assistance over a single weekend
• The company recognized early that transformer models would make custom models obsolete for many tasks
• Despite having millions in revenue and Series A funding, they made the difficult decision to pivot completely
• Startups need both "irrational optimism" and "uncompromising realism" to succeed
• Their initial product was inferior to GitHub Copilot but free, allowing them to quickly gain users
• Within months, they developed unique capabilities that surpassed competitors, like mid-code completion
• Their GPU infrastructure background provided crucial advantages in quickly deploying their new product
• The entire pivot and first product launch was accomplished with just eight people in about two months

Timestamp: [0:00-10:25]

Companies/Products:

• Windsurf - AI-powered coding assistant with over a million users
• Exofunction - Original GPU virtualization company before pivoting to Windsurf
• GitHub Copilot - Early AI coding assistant that Windsurf initially competed with
• Nvidia - Used as an example of a company that must continually innovate to stay ahead
• AMD - Mentioned as Nvidia's competitor that would gain ground if Nvidia stopped innovating
• VMware - Referenced as a parallel to their original business model but for CPUs instead of GPUs
• OpenAI - Creator of Text-Davinci model that influenced their pivot decision
• Y Combinator (YC) - Startup accelerator whose principles like "ramen profitability" influenced their approach

Technologies:

• GPT-3 - Early model that demonstrated to them how transformer models would disrupt their business
• BERT model - Example of custom models that would be replaced by general-purpose transformer models
• VS Code extension - Platform for their initial product after pivoting
• Transformer models - Architecture that catalyzed their pivot decision
• Kodium - Their extension product developed immediately after the pivot

Concepts:

• GPU virtualization - Their original business focus before the pivot
• Sentiment classification - Example of a task that general AI models would make custom models obsolete for

Timestamp: [0:00-10:25]

After launching their free product, Windsurf quickly attracted attention from major enterprises seeking more secure and personalized implementations of the AI coding assistant.

The free product initially gained traction with individual developers across multiple IDEs, including VS Code, JetBrains, Eclipse, and Vim. This widespread adoption caught the attention of large companies looking to deploy the technology securely within their organizations.

"Companies started reaching out because they not only wanted to run the product in a secure way, they also wanted to personalize it to all the private data inside the company."

Shortly after launch, major enterprises like Dell and JP Morgan Chase became customers. These companies had tens of thousands of developers who could benefit from the technology, but they required solutions that could handle enormous codebases—some exceeding 100 million lines of code—while providing personalized suggestions tailored to their specific environments.

Timestamp: [10:32-11:20]

The transition from free product to enterprise solution happened remarkably quickly, despite the company's lean structure and lack of dedicated sales personnel.

While these major enterprise deals took time to close formally, pilot programs began within just a few months of the product's initial launch. The founding team handled these early pilots themselves:

"Obviously these companies take some time to close, but pilots were starting within like a couple months or a quarter after that. Obviously we had no sales people at the company, so like the founding team was just trying to run as many pilots as possible to see what would ultimately work."

This rapid enterprise adoption validated their pivot decision and demonstrated the strong market demand for AI-powered coding tools in corporate environments.

Timestamp: [11:26-11:44]

Windsurf made the strategic decision to support multiple IDEs very early in their development process, recognizing that enterprise environments required broad compatibility across development tools.

The decision to expand beyond VS Code came quickly after their initial launch. When questioned about this horizontal expansion strategy versus focusing deeply on one platform, Varun explained their reasoning:

"I think maybe the fundamental reason that we thought was quite critical is if we were going to work with companies, companies have developers that write in many languages. For instance, a company like JP Morgan Chase might have over half of their developers writing in Java, and for those developers they are going to use JetBrains and IntelliJ."

This insight into enterprise requirements proved crucial—IntelliJ is used by 70-80% of Java developers worldwide, making it essential for comprehensive enterprise adoption. Without multi-IDE support, Windsurf would have been just "one of many solutions" within companies rather than a standardized tool across all development teams.

Importantly, this early architectural decision enabled efficient implementation across platforms:

"Because we made the decision early enough, it changed the architecture of how we built the product out. We are not building a separate version of the product for every single IDE. We have a lot of shared infrastructure that actually lives on a per-editor basis, so it's actually a very small amount of code that needs to get written to make sure we can support as many IDEs as possible."

Timestamp: [11:44-13:08]

By mid-2023, Windsurf had achieved significant commercial success with its enterprise offering while maintaining a free product for individual developers. However, the rapidly evolving AI landscape prompted them to consider their next strategic move.

"The business has gotten well over eight figures in revenue just from these enterprises using the product. We have this free individual product, but I think one of the things about this industry that we all kind of know is the space moves really, really fast."

Varun emphasizes their philosophy of continuous experimentation, acknowledging that most of their initiatives don't succeed:

"We basically are always making bets on things that are not working. Actually, most of the bets we make in the company don't work, and I'm excited when we're—I'm happy when we're, let's say, only 50% of the things we're doing are actually working."

He explains that if everything they tried was successful, it would indicate one of three concerning issues: insufficient risk-taking, excessive hubris, or inadequate hypothesis testing that fails to push technological boundaries.

Timestamp: [13:13-14:23]

Early in 2023, Windsurf began exploring AI agents as the next frontier in developer tools, though their initial prototypes fell short of expectations.

"We believed actually in the very beginning of last year that agents were going to be extremely huge, and we had prototypes of this in the beginning of last year and they just didn't work."

Despite these early failures, they continued developing foundational capabilities they believed would eventually enable effective agents: understanding large codebases, interpreting developer intent, and making quick code edits. The missing piece was a model capable of efficiently calling these tools.

The breakthrough came mid-year with the release of more capable models:

"Obviously in the middle of last year, that completely changed with the advent of like Claude 3.5."

With these improved models, Windsurf could finally realize their vision for AI agents, but they encountered a new limitation: the traditional IDE experience couldn't fully showcase these capabilities.

Timestamp: [14:23-15:08]

As agent capabilities improved, Windsurf recognized that existing IDE integrations couldn't fully showcase their technology's potential, leading to the decision to create their own dedicated IDE.

"We thought what was going to happen is developers would spend way more time not writing software but reviewing software that the AI was going to put out."

Varun describes their company as fundamentally technology-driven, with products serving as vehicles to deliver that technology:

"I think we are a technology company at heart. I think we are a product company, but I think the product serves the technology, which is to say we want to make the product as good as possible to make it so that people can experience the technology."

By mid-2023, they concluded that VS Code extensions were too limiting for their vision, prompting the decision to develop their own IDE—Windsurf.

Timestamp: [15:08-15:38]

Creating a complete IDE from scratch presented another significant engineering challenge, which the Windsurf team approached with their characteristic speed and focus.

The team chose to fork VS Code as their starting point, requiring them to quickly master this complex codebase. Despite the technical complexity, they maintained their rapid development pace:

"We ended up shipping Windsurf out in less than 3 months of starting the project. That's when we shipped it out across all operating systems."

This remarkable timeline demonstrates their continued ability to execute quickly on major initiatives, a pattern established during their original pivot from GPU virtualization.

Timestamp: [15:44-16:02]

Following its launch, Windsurf IDE gained traction quickly among early adopters, though initial versions had rough edges that affected user retention.

"I think the speed at which it took off among early adopters was quite high. There were obviously some very rough edges, and because of the rough edges, obviously people started coming and leaving the platform fairly quickly."

However, as they enhanced both the agent capabilities and the passive experience, they observed improvements in both product awareness and user retention:

"As we improved the capabilities of the agent, as we improved the capabilities of the passive experience—even the passive tab experience has made massive leaps in the last couple months—we started realizing that not only were people talking about the product more and more, people were also staying on the product more and more at a higher rate."

This growth pattern illustrates their ability to rapidly iterate and improve the product based on early user feedback, gradually transforming an initial minimum viable product into a more polished solution.

Timestamp: [16:02-16:36]

The development of Windsurf IDE represented another pivotal moment for the company, requiring full commitment from the engineering team despite its relatively modest size.

While not as dramatic as their original pivot from GPU virtualization to AI coding, this transition still required significant focus:

"This was another—I wouldn't say it's a bet-the-company moment because it's not a fundamentally different paradigm compared to moving from a GPU virtualization product to an AI code product—but yeah, it was anyone that could work on it needed to kind of drop what they were working on in the past and work on it immediately."

At this stage, the engineering team remained surprisingly lean, with fewer than 25 people accomplishing this major product launch in under three months.

Timestamp: [16:41-17:07]

In contrast to their lean engineering approach, Windsurf developed a robust go-to-market team to support enterprise adoption—a necessity when selling to Fortune 500 companies.

"Interestingly, our company actually from an employee standpoint actually didn't have that few people. We actually had a fairly large go-to-market team."

Varun explains that selling sophisticated AI technology to large enterprises requires significant support beyond just product access:

"We were selling our product to the largest Fortune 500 companies. It's very hard to do that purely by letting them swipe a credit card. You need a lot of support, you need to make sure that the technology is getting adopted properly, which is very different than just give the people the product and see it grow."

This balanced approach—maintaining a lean engineering team while investing in customer-facing roles—allowed them to drive enterprise adoption effectively while continuing to innovate rapidly.

Timestamp: [17:14-17:49]

Windsurf carefully structured their go-to-market team to combine business acumen with deep technical expertise, ensuring effective engagement with enterprise customers.

Their approach includes two key roles:

1. Account executives who are genuinely passionate about the technology:

   "We try to find people that are very curious and excited about the capabilities—in fact, people that would use Windsurf in their free time—because they're providing the product to leaders who also love software and technology."

2. Deployed engineers who implement the technology and ensure customers derive maximum value:

   "We also have these deployed engineer roles that get their hands really dirty with the technology and make sure that our customers get the most value from the technology."

This hybrid team structure bridges the gap between technical capabilities and business requirements, facilitating successful enterprise deployments.

Timestamp: [17:56-18:28]

Perhaps the most striking indication of Windsurf's potential impact is its adoption by non-technical users within the company itself, demonstrating how AI coding tools can democratize software development.

One notable example involves a partnerships leader with no programming background:

"One of our biggest users of Windsurf at the company is a non-technical person who leads partnerships at the company. He has actually replaced buying a bunch of sales tools inside the company."

This represents a fundamental shift in organizational dynamics around software development:

"I think Windsurf is giving power back to the domain experts. In the past, what would happen in an organization is he would need to talk to a product manager, who would talk to an engineer, and the engineer would have a large backlog because this clearly doesn't immediately make the product better, so this has to be a lower priority. But now he is actually empowered to actually go and build these apps."

While acknowledging ongoing debates about whether "vibe coding" requires prior programming knowledge, Varun notes that they still provide technical oversight for deploying these applications:

"If we do need to deploy one of these apps, we have a person that actually focuses on making sure that these apps are secure and deployed. But the amount of leverage that that person has is ridiculous—instead of him going out and building all of these apps, the zero-to-one could actually get built by people that are domain experts but non-technical inside the company."

This emerging use case suggests Windsurf's potential to fundamentally transform who can participate in software development—extending beyond professional programmers to domain experts across organizations.

Timestamp: [18:34-19:43]

• Windsurf quickly attracted enterprise customers like Dell and JP Morgan Chase within months of their initial product launch
• Supporting multiple IDEs was a strategic early decision to accommodate enterprise environments with diverse development needs
• The company achieved eight-figure revenue from enterprise customers by mid-2023
• Windsurf embraces experimentation, with Varun considering a 50% success rate ideal for driving innovation
• Early agent prototypes in early 2023 failed, but improved AI models later enabled their vision
• The need to fully showcase agent capabilities drove the decision to build their own IDE
• They shipped Windsurf IDE across all operating systems in less than three months with fewer than 25 engineers
• Despite a lean engineering team, they maintained a robust go-to-market team to support enterprise adoption
• Non-technical employees at Windsurf use the product to build applications, demonstrating its potential to democratize development

Timestamp: [10:32-19:43]

Companies/Products:

• Windsurf - The company's new IDE product launched to better showcase AI agent capabilities
• Codeium - Their earlier product that supported multiple IDEs through extensions
• VS Code - Initial IDE they supported and later forked to create Windsurf
• JetBrains/IntelliJ - IDE used by 70-80% of Java developers worldwide
• Eclipse - IDE supported by their extensions
• Vim - Text editor supported by their extensions
• Dell - Early enterprise customer
• JP Morgan Chase - Major enterprise customer with many Java developers
• Fortune 500 - Target market for their enterprise sales

Technologies:

• Claude 3.5 - AI model mentioned as enabling agent capabilities
• AI agents - Technology direction they believed would be "extremely huge"

Concepts:

• Vibe coding - Controversial concept of non-technical users creating code with AI assistance
• Multi-IDE strategy - Supporting multiple development environments simultaneously
• Go-to-market - Their approach to enterprise sales with specialized roles

Timestamp: [10:32-19:43]

When asked about competing directly against major players like GitHub Copilot and then emerging startups like Cursor, Varun explains Windsurf's unique approach to market competition.

"This might be a weird thing about our company, but our company just doesn't have like—morale is not really affected by what other companies do."

Varun attributes this resilience to the company's history of navigating turbulent times, including their dramatic pivot with just ten employees. He notes that the competitive landscape in their space has constantly fluctuated, with different companies rising to prominence at different times.

"If you were to go to the beginning of 2023, everyone would have thought GitHub Copilot was the product that everyone would use, and there was no point building. And in the middle, kind of DeVin came out, and everyone was like, 'DeVin is going to solve everything.' And then after that, obviously Cursor is doing a really great job."

Rather than obsessing over competitors, Windsurf focuses on their long-term strategy and execution while remaining flexible with the details. This approach gives them what Varun calls "a fighter's chance" in a rapidly evolving market.

Timestamp: [19:49-21:04]

While not allowing competitors to affect team morale, Varun emphasizes the importance of staying aware of what others in the space are doing.

When asked if they pay attention to competitors' products, Varun confirms:

"Yeah, I think we don't want to put our heads in the sand and kind of tell ourselves our product is awesome and just kind of—because it's very easy to do that, especially given the fact that before we worked on Windsurf, the company was also growing very, very quickly from like a revenue standpoint."

This balanced approach—maintaining confidence in their direction while remaining aware of the competitive landscape—helps Windsurf avoid complacency and ensures they stay responsive to market developments.

Timestamp: [21:04-21:22]

When asked specifically about how Windsurf's approach differed from well-received products like Cursor, Varun highlights their early commitment to agent-based interfaces rather than chat-driven interactions.

"At the time actually when we started working on Windsurf, all the products were basically chat and this autocomplete capability. I think that's basically what GitHub Copilot was, what Cursor was at the time."

Windsurf took a deliberately different approach:

"We took a very opinionated stance that we thought agents were where the technology was actually going. We were the first agentic editor that was out there."

Varun explains their philosophical objection to interfaces that required constantly "@-mentioning" the AI, comparing it to early search engines before Google's refinements:

"This almost reminded us of the anti-pattern of what Google and these search engines were before Google improved their product a lot, which was kind of like these landing pages that had every distinct bucket of things you could search for. But Google came out with this very clean search box."

This vision of simplicity and proactive assistance guided Windsurf's development, focusing on creating a system that would understand developer intent and make rapid changes rather than requiring explicit prompting for every action.

Timestamp: [21:30-22:46]

Rather than creating highly configurable systems optimized for present technology, Windsurf invested in capabilities they believed would be essential as AI coding tools evolved toward greater simplicity and intuition.

Varun describes their focus areas:

"We invested in capabilities like how do you deeply understand the codebase to understand the intent of the developer, how do you actually go out and make changes in a way that's very quick to the codebase."

They prioritized a dynamic, action-oriented system over a read-only approach that required explicit tagging:

"We took the approach of, 'Hey, instead of having this read-only system where you tag everything, what happens if you could make changes very quickly?' And that's why at the time we were kind of the first to do that."

While this approach now seems obvious in retrospect, Varun emphasizes that at the time it represented a distinctive vision for the future of AI-assisted development.

Timestamp: [22:46-23:14]

Returning to a theme mentioned at the start of the interview, Varun expands on the concept that technological insights rapidly depreciate, requiring constant innovation to maintain competitive advantage.

"One of the things that I think is true for any startup is you have to keep proving yourself. Every single insight that we have is a depreciating insight—it is a very, very depreciating insight."

He explains that companies don't succeed based on a single insight from the past, but rather through continuously compounding technological advantages:

"The reason why companies win at any given point is not like they had a tech insight one year ago. Actually, if a company wins, other than the fact that they have a monopoly, it's actually like a compounding tech advantage that keeps existing over and over again."

To illustrate this principle, Varun references Nvidia, noting that despite being one of the world's largest companies, they must continue innovating or risk AMD eroding their impressive profit margins within just two years.

Timestamp: [23:14-24:02]

Varun elaborates on his philosophy of continuous innovation, emphasizing that it's not just about growth but about organizational survival.

"I also think for us—and I tell the company this—if we're not continuing to have insights, and that's why I'm completely okay with a lot of our insights being wrong... if we don't continually have insights that we are executing on, we are just slowly dying. That's what's actually happening."

This perspective frames innovation not as an optional activity for growth but as an existential necessity—without it, the company begins a slow decline regardless of current success.

Timestamp: [24:14-24:29]

The interviewer notes how Windsurf's various technological bets unexpectedly compounded over time, creating unique advantages that differentiated their product.

Their initial expertise in GPU deployment and optimization—developed during their time as a GPU virtualization company—later enabled them to provide blazingly fast autocomplete functionality that outperformed competitors.

Similarly, their experience building plugins for enterprise environments gave them expertise in reading and processing large codebases efficiently. This created a virtuous cycle where seemingly unrelated technological investments eventually converged to create distinctive product capabilities.

Timestamp: [24:35-25:08]

When discussing how Windsurf processes large codebases, Varun explains their distinctive approach that goes beyond the standard retrieval-augmented generation (RAG) methods popular in many AI coding tools.

While acknowledging that RAG is conceptually sound—retrieving relevant information to augment generation—Varun suggests that many implementations became too narrowly focused on vector databases:

"I think what people got maybe a little too opinionated about was the way RAG is implemented—it has to be a vector database that you go out and search. I think a vector database is a tool in the toolkit."

Instead of relying solely on vector search, Windsurf developed a multi-faceted approach to identify the most relevant code contexts:

"What we ended up doing is having a series of systems that enable us to pack the context with the most relevant snippets of code. And the way we ultimately did that was a combination of keyword search, RAG, abstract syntax tree parsing, and then on top of that, using all the GPU infrastructure we have to take large chunks of the codebase and in real-time rank it as the query is coming in."

This comprehensive approach was motivated by practical user needs, such as finding all instances of an API that needed updating—a task where missing even a few occurrences would render the feature ineffective. To ensure high precision and recall, they integrated multiple technologies rather than relying on a single method.

Timestamp: [25:14-26:46]

• Windsurf maintains resilience against market competition by focusing on long-term strategy rather than letting competitor actions affect team morale
• While acknowledging competitors, they avoid the complacency of assuming their product is superior without verification
• Windsurf deliberately chose an agent-based approach when most competitors were using chat interfaces, becoming "the first agentic editor"
• They invested in understanding developer intent and enabling quick code changes rather than creating highly configurable interfaces
• Varun emphasizes that technological insights rapidly depreciate, requiring continuous innovation to maintain competitive advantage
• Companies succeed through compounding technological advantages rather than single insights
• Windsurf's past investments in GPU optimization and enterprise code parsing unexpectedly converged to create unique product capabilities
• Rather than relying solely on vector databases for RAG, they developed a multi-method approach combining keyword search, AST parsing, and real-time ranking

Timestamp: [19:49-26:46]

Companies/Products:

• GitHub Copilot - Established competitor in the AI coding space
• Cursor - Emerging startup competitor when Windsurf launched their IDE
• DeVin - AI development tool mentioned as having brief prominence
• Nvidia - Used as example of a company that must continuously innovate despite market dominance
• AMD - Mentioned as Nvidia's competitor that would gain ground if Nvidia stopped innovating
• Google - Referenced for their clean search interface that inspired Windsurf's approach to simplicity

Technologies:

• Retrieval-Augmented Generation (RAG) - Approach to AI that combines retrieval and generation
• Vector databases - Tool commonly used for RAG implementations that Windsurf viewed as just one option
• Abstract Syntax Tree (AST) parsing - Method used by Windsurf to understand code structure
• Keyword search - Part of Windsurf's multi-method approach to context retrieval
• Agentic editor - Windsurf describes itself as the first editor with this approach

Concepts:

• Depreciating insights - Varun's term for the declining value of technological advantages over time
• Compounding tech advantage - The concept that successful companies continuously build on their innovations
• @-mentioning - Interface pattern where users explicitly prompt AI with commands, which Windsurf sought to avoid

Timestamp: [19:49-26:46]

Varun explains how Windsurf's approach to evaluation sets them apart from other AI startups, drawing on their team's background in autonomous vehicles where rigorous testing is essential.

"We started off—a lot of the companies started off working on autonomous vehicles, and the reason why that's kind of important is these are systems where you can't just YOLO these systems, which is to say you build the software and then you kind of let it run. You need really good evaluation."

Rather than pursuing complexity for its own sake, Windsurf focuses on what works. The sophistication of their system evolved in response to their evaluation requirements:

"We don't strive for complexity, we strive for what works. And then the question then is like, 'Why is the system so much more complex now?' It was because we built really good evaluation systems."

This methodical approach to evaluation has enabled them to make informed technical decisions rather than taking intellectual shortcuts that might be common among other AI startups.

Timestamp: [26:52-27:35]

When asked about their evaluation methods, Varun details how Windsurf leverages the executable nature of code to create robust evaluation systems.

"The evals for code are actually really cool. Basically, the idea is code—you can leverage a property of code which it can be run."

Their approach uses open-source projects and their commit history to create realistic test scenarios:

"We can take a lot of open source projects and find commits in these open source projects with tests attached to them. You can take the intent of a commit, delete all the code that is not the unit test, and then you can see: 'Hey, are you able to retrieve the parts where the change needs to get made? Do you have a good high-level intent to make those changes? And then after making the changes, does the test pass?'"

This methodology allows them to evaluate multiple aspects of their system's performance:

"You can break it down into so much granularity. You can be like, 'What is my retrieval accuracy? What is my intent accuracy? What is my test passing accuracy?' You can do that, and then now you have a hill to climb."

Varun emphasizes that establishing this "hill to climb"—a clear metric for improvement—is essential before adding complexity to AI applications. Without such metrics, development becomes directionless:

"I think that's actually important before you add a lot of complexity for any of these AI apps. I think you need to make a rigorous hill that you can actually climb. Otherwise, you're just shooting in the dark."

Timestamp: [27:35-28:54]

Varun discusses how Windsurf balances data-driven development based on evaluation metrics with intuition-based product decisions.

When asked whether their development is primarily driven by improving evaluation scores or by intuitive "vibes-based" feedback, Varun acknowledges it's a combination of both approaches:

"It's a little bit of both, but obviously for some kinds of systems, I think evals are more important than vibes."

For complex technical components like their parallel GPU code processing system, quantitative evaluation is essential:

"For the system that basically takes a large chunk of the code, chunks it up, and passes it to hundreds of GPUs in parallel giving you a result in one second—it's very hard to have an intuition of like, 'Is this way better?' Because that's a very complex sort of retrieval question."

However, certain features are better guided by user experience:

"What if we looked at the open files in a codebase? This is actually a harder thing to eval because when you're evaling, you don't know what the user is doing in real time. This is one of those cases where having a product in market helps us a lot."

The company's approach often begins with intuition and direct user feedback, followed by the development of appropriate evaluation metrics:

"That maybe starts with vibes, and then after that you can build eval."

Timestamp: [28:54-30:11]

Addressing online discussions about whether AI coding tools are only suitable for simple applications, Varun explains how Windsurf is being used for substantial engineering projects.

The interviewer notes that there's been "a lot of chatter on the internet about vibe code is only for toy apps" and asks Varun to explain how power users employ Windsurf for serious engineering tasks.

Varun begins by drawing an interesting parallel with the adoption of ChatGPT, noting that many experienced developers at Windsurf didn't initially find substantial value in it—not because it wasn't useful, but because they already had established workflows with tools like Stack Overflow.

The real transformation came with the introduction of agent capabilities:

"Very recently with agents, the agent is making larger and larger scale changes with time, and I think what developers now at our company do is they have felt the hills and valleys of this product."

Despite some challenges—such as agents sometimes making more changes than necessary when given insufficient instructions—developers at Windsurf have adapted their workflows:

"The very first time they have a task, they are putting it into Windsurf. Their first thing is not to actually go out and type in the actual editor, it's to actually put the intent and actually make those changes."

These capabilities have enabled automation of significant development tasks:

"They're doing kind of very interesting things now, like deploying our software to our servers actually now gets done with the workflows that are entirely built inside Windsurf. So just a lot of boilerplate and repetitive tasks have been completely eliminated inside our company."

The key to enabling these sophisticated use cases is Windsurf's ability to effectively process large codebases:

"The reason why this is possible is kind of because we're able to operate over a codebase that has many millions of lines of code really, really effectively."

Timestamp: [30:17-31:52]

When asked for advice on how users can provide better instructions to achieve more precise code changes, Varun emphasizes the importance of patience and persistence.

"I think this is one of those things where I think you kind of need to have a little bit of faith in the system and let it kind of mess up a little bit."

He acknowledges this approach might be challenging for most users, as they tend to have high expectations and quickly dismiss tools that aren't immediately perfect:

"People's expectations are very high, and maybe that's the main piece of feedback I'd give, which is that our product actually for these larger and larger changes, it might make 90% of the changes correctly, but if 10% is wrong, people will just write off the entire tool."

Instead of abandoning the tool when it makes mistakes, Varun recommends either using the revert feature or continuing to work with the system. His most practical advice focuses on frequent code commits:

"Maybe the most important aspect is commit your code as frequently as possible. I think that maybe that's the big tip there, which is that you don't want to get in a situation where you've made 20 changes, and on top of that made some changes yourself, and you can't revert it, and then you get very frustrated at the end of it."

This approach of working incrementally with frequent saves helps manage the learning curve and reduces frustration when working with AI coding tools.

Timestamp: [31:57-33:18]

The discussion turns to whether traditional version control systems like Git need to evolve to better accommodate collaborative work between humans and AI agents.

The interviewer poses a thoughtful question about whether Git's traditional commit-based workflow remains appropriate in an AI-driven development paradigm. Varun acknowledges they've considered this challenge, especially as they envision a future with multiple AI agents working in parallel:

"One of the things that we always think of is in the future you're going to have many, many agents running in parallel on your codebase. That has some trade-offs. If you have two agents that kind of modify the same piece of code at the same time, it's hard to actually know what's going on."

However, he notes that these challenges aren't entirely unique to AI:

"But hey, that's how real software development works too. When you have a lot of engineers that operate on a codebase, they're all kind of mucking around with the codebase at the same time, so that's not a very unique thing."

Rather than replacing Git, Varun suggests adapting it with features like work trees:

"Git has these things called work trees, which is like you can have many work trees and versions of the repository all in your same directory. Perhaps you can have many of these agents working on different work trees."

Windsurf's approach emphasizes maintaining a unified timeline that captures both human and AI actions:

"One of the things that we think about at the company in terms of why we think our agent is really good is we try to have a unified timeline on everything that happened. The unified timeline is not just what the developer did, but actually what the developer did in addition to what the agent did."

This integrated approach helps the AI maintain awareness of all actions:

"Our product, if you end up doing things in the editor, if you end up doing things in the terminal, all of those things are captured and the intent is actually kind of tracked in such a way where when you use the AI, the AI knows in that situation."

Varun concludes that this remains an open problem without a definitive solution yet:

"In some ways, we'd like this thing where the agent is not operating on a completely different timeline, but it's something that's kind of getting merged in at a fairly high cadence. So I think this is like an open problem. I don't think we have the exact right answer for this."

Timestamp: [33:24-35:10]

• Windsurf's robust evaluation systems are influenced by the team's background in autonomous vehicles, where rigorous testing is essential
• Their code evaluations leverage open-source projects and test-driven commits to create realistic test scenarios
• Development at Windsurf balances quantitative metrics with intuitive "vibes-based" feedback depending on the feature
• Contrary to online discussions suggesting AI coding is only for toy apps, Windsurf is being used for serious engineering tasks including software deployment
• When using AI coding tools, frequent commits are essential to prevent frustration when the system makes mistakes
• Traditional version control systems like Git may need to evolve to better accommodate parallel work between humans and multiple AI agents
• Windsurf emphasizes maintaining a unified timeline that captures both human and AI actions to improve context awareness

Timestamp: [26:52-35:10]

Technologies:

• Git - Version control system discussed as potentially needing adaptation for AI coding workflows
• Work trees - Git feature mentioned as potential solution for parallel agent work
• Stack Overflow - Referenced as a tool developers used before ChatGPT
• ChatGPT - Mentioned in comparison to developer workflow adaptations

Concepts:

• Evaluation systems (evals) - Rigorous testing frameworks to measure AI performance
• YOLO approach - "You Only Live Once" mentality of shipping without thorough testing (criticized)
• Retrieval accuracy - Metric for how well system finds relevant code
• Intent accuracy - Metric for how well system understands developer goals
• Test passing accuracy - Metric for functional correctness of AI-generated code
• Vibes-based development - Intuitive approach to product decisions contrasted with metrics-driven development
• Hills and valleys - Metaphor for strengths and limitations of AI tools that users learn over time
• Unified timeline - Windsurf's approach to integrating human and AI actions in a single history

Development Practices:

• Commit frequency - Recommended practice of committing code often when using AI tools
• Parallel agents - Future vision of multiple AI agents working simultaneously on codebase
• Boilerplate elimination - Use case where AI replaces repetitive coding tasks

Timestamp: [26:52-35:10]

When asked about how Windsurf will evolve in the future, Varun dismisses the idea that AI-assisted coding is merely a passing trend, instead predicting dramatic increases in capability.

"I think that's going to get more and more capable with time. I think whenever I hear someone saying, 'Hey, this is not going to work for this complex use case,' it feels like a lite saying something."

To illustrate the rapid progress of AI systems, Varun shares a personal anecdote about the American Mathematics Competitions (AMC):

"There's this math Olympiad called AMC, and I used to do that in high school. I was very excited about how well I would do—my high score was somewhere close to 14, and that's a very high score. But the crazy thing is that was one of those things that I thought, 'Wow, the AI systems, they're not going to get anywhere near as good.' Beginning of the year last year, it was probably well under five, and now the average that OpenAI is getting is 14 and a half to 15 for O-4 Mini."

This exponential improvement trajectory leads Varun to predict that AI will transform every aspect of software development:

"Basically every part of the software development life cycle, whether it be writing code, reviewing code, testing code, debugging code, designing code—AI is going to be adding 10 times the amount of leverage very shortly. It's going to happen much, much more quickly than people imagine."

Timestamp: [35:16-36:33]

As AI tools automate routine tasks, Varun explains how this is transforming the nature of engineering work at Windsurf, shifting it toward a more research-oriented approach.

When asked what engineers do with the time freed up from handling boilerplate code, Varun emphasizes the vast potential for innovation:

"One of the things about our company, and probably every startup that is building in this space, is the ceiling of where the technology can go is so high—it's so high. If developers can spend less time doing boilerplate, they can spend more time testing hypotheses for things that they're not sure work."

This shift creates a fundamental change in engineering culture:

"In some ways, engineering becomes a lot more of like a research kind of culture, where you're testing hypotheses really, really quickly. And that has some high cycle time attached to it—you need to go out and implement things, you need to build the evaluation, you need to test it out with our users. But that's the things that actually make the product way better."

This transition suggests a future where engineers focus more on exploration and innovation rather than implementation details.

Timestamp: [36:44-37:14]

The changing nature of engineering work is also influencing the types of skills and qualities Windsurf looks for when hiring new team members.

Varun describes their ideal engineer profile:

"For engineers that we hire, we want to look for people with really high agency that are willing to be wrong, and bold."

However, he notes that these qualities have always been valuable in startup environments:

"Weirdly, I don't know if that's changed for a startup. Startups should never be hiring people that—the reason why they're joining a company is to very quickly write boilerplate code. Because in some sense, and I don't want to—this is not the goal—but a startup can succeed even if they have extremely ugly code. That's not usually the reason why a startup fails."

He emphasizes that successful startups focus on creating differentiated value rather than perfect code:

"The reason why a startup fails is they didn't build a product that was differentially good for their users. That's why they ultimately failed."

Timestamp: [37:20-37:56]

The conversation turns to whether AI is eliminating the need for "workhorse" engineers who traditionally handled repetitive, specialized tasks within organizations.

The interviewer raises an interesting point about how organizations previously needed specialists for certain niche tasks:

"In the old days, this was like building Android apps. It's like you hired someone to do it because there were very few people who would just be willing to do it."

They then ask if Varun envisions AI becoming the "infinite workhorse" that makes such specialized roles obsolete. Varun largely agrees:

"Maybe the aspects of software that are really niche, that are undesirable for a lot of people to do except a handful of people—those things kind of get democratized a lot more, unless that has a lot of depth attached to it."

He suggests that dedicated specialists for routine tasks may become unnecessary:

"If something is like, 'Hey, we need to change a system to use a new version,' and there was someone that deeply always got in the weeds with version changes—I don't think you have people that are just focused on that inside companies."

This indicates a significant shift in how technical organizations might structure their teams in the future, with AI handling specialized but routine work.

Timestamp: [38:02-38:48]

Windsurf has developed a unique technical interview process that both incorporates AI tools and tests candidates' fundamental problem-solving abilities without them.

Varun describes their balanced approach:

"We have a fairly rigorous and high technical bar. That's a combination of: we give interviews that actually allow people to use the AI to kind of solve a problem, because we want to validate if people kind of hate these tools or not. There are still some developers that do, and obviously if you do, we're probably the wrong company to work at."

However, they also conduct interviews without AI access:

"At the same time, we do have interviews in person, on-site, where we don't give them the AI, and we want to see them think. It would be a bad thing if ultimately when someone needs to write a nested for loop, they need to go to ChatGPT."

The goal is to assess fundamental problem-solving abilities:

"I think problem-solving skills are just at a high level, still should go at a premium. That is the valuable skill that humans have."

Timestamp: [38:48-39:34]

The interviewer raises a significant challenge: as AI tools like Windsurf become increasingly capable, it's becoming difficult to design technical interviews that genuinely assess a candidate's abilities.

"A challenge that a lot of companies we've talked to have had, that we've even had ourselves, is that Windsurf has gotten so good that if you give people Windsurf, it's difficult to even come up with an interview question that Windsurf can't just one-shot."

They note that when candidates can simply copy and paste a question into the tool and get the answer, the interview process loses its evaluative power. Varun acknowledges this reality:

"That's true, and you're totally right. There's very few problems now that something like O-4 Mini is not able to solve. I mean, if you look at competitive programming, it's just in a league of its own already at this point."

He explains why traditional interview problems are particularly vulnerable to AI assistance:

"The crazy thing is, interviews by nature are going to be kind of isolated problems. They're by nature, because if the problem actually required so much understanding to do, you wouldn't be able to explain the problem. So that's perfect for the LLMs, where you give them an isolated problem where you can test and run code extremely quickly."

This leads to his conclusion:

"If you only have algorithmic interviews and you let people use the AI, I don't know—you're not really testing anything at that point."

Timestamp: [39:34-40:36]

In response to the interview challenge paradox, Windsurf has evolved its interview approach to focus on open-ended problems and evaluating how candidates think through complex trade-offs.

When asked if they've moved away from algorithmic questions toward different types of problems better suited for the AI era, Varun explains:

"We have questions that are both system design-y plus algorithms related, but these are questions that are fairly open-ended. There may not be a correct answer. There are trade-offs that you can ultimately make, and I think what we want to do is just see how people think given different trade-offs and different constraints."

Beyond technical skills, they're assessing qualities like intellectual curiosity:

"We're trying to validate for intellectual curiosity. If someone ultimately says 'I don't know why,' that's totally fine as long as they've gone to a depth that we feel shows interest and good problem-solving skills."

He concludes with an observation about genuine curiosity:

"You can tell when someone is curious and wants to learn things—it's very obvious."

Timestamp: [40:43-41:20]

Counterintuitively, despite creating tools that automate coding tasks, Windsurf is actually increasing their engineering hiring rather than reducing it.

The interviewer notes this apparent paradox:

"You're at the forefront of building all these AI coding tools. It hasn't affected at all your hiring plans—on the contrary, you actually need way more engineers to execute."

Varun explains that this stems from the ambitious scope of what they're trying to achieve:

"I think that just boils down to—I think the problem has a very high ceiling. There's so many more things that we really want to do. The mission of the company is to reduce the time it takes to build technology and apps by 99%. It's going to take a lot of work to go out and do that."

While AI has increased individual productivity significantly, the vastness of the challenge requires more talent:

"Granted, each person in our company is way more productive than they were a year ago. But I think for us to go out and accomplish that, it's a Herculean task."

He outlines how much of the development process still needs to be addressed:

"Right now, we've helped a lot with the code writing process and maybe the navigation of code process, but we have not touched much on the design process, on the deploying process. The debugging process is fairly rudimentary right now. There's just so many different pieces."

Quantifying their progress, he adds:

"If you say you have 100 units of time, we have an ax, we've cut off maybe like 40 or 50 in that time, but there's just a lot more snippets that we need to cut out."

Timestamp: [41:31-42:29]

As AI systems become increasingly capable, the interviewer observes that humans themselves are becoming the limiting factor in the development process.

"At this point, it does feel like when I'm using Windsurf, I am often the extremely slow bridge between different pieces of technology, copying and pasting data back and forth. That's probably actually still a large chunk of your time. All the pieces have gotten so fast that now it's like the glue between them, but I'm the glue, but I'm much slower."

This observation highlights an emerging challenge: as individual AI components become more efficient, the integration points where human decision-making or data transfer is required become the new bottlenecks in the process.

Timestamp: [42:35-42:47]

The interviewer raises a provocative question about whether AI might fundamentally transform software development from pre-packaged products to dynamically generated, just-in-time solutions.

Referencing a recent essay about giving agents direct database access, they ask:

"If codegen gets a lot better, which based on this conversation I think we can count on getting like 10x, 100x better from here—what if instead of building packaged software, there's like just-in-time software that the agent basically just builds for you as you need it? Does that change the nature of software and SaaS, and what happens to all of us in Windsurf?"

Varun responds with a profound vision of how the concept of "developer" might evolve:

"I think this notion of just a developer is probably going to broaden out to what's called a builder, and I think everyone is going to be a builder. They can decide how deep they want to go and build things. Maybe our current version of developers can go deep enough that they can build more complex things in the shorter term."

He envisions a future where software creation becomes completely democratized:

"I think software is going to be this very, very democratized thing. I imagine a future in which what actually happens when you ask an AI assistant, 'Hey, build me something that tracks the amount of calories I have'—why would you have a very custom app that goes out and does this? It's probably something that takes all the inputs from your AR glasses and everything and has a custom piece of software that comes out, like an app that is there, and it has tasks that go and tell you, 'Are you on track with all the calories you're consuming?'"

This leads to his conclusion about a fundamental shift in our relationship with technology:

"I think that's a very, very custom piece of software that you have for yourself that you can keep tweaking. I can imagine a future like that where effectively everyone is building, but people don't know what they're building as software—they just—they're kind of just building capabilities and technology that they have for themselves."

This vision suggests a world where the line between user and developer blurs completely, with AI enabling everyone to create personalized technical solutions without necessarily understanding the underlying code.

Timestamp: [42:53-44:42]

• AI-assisted coding is not a passing trend but will become dramatically more capable, transforming every aspect of software development
• The ceiling for AI coding technology is extremely high, with progress occurring faster than most people anticipate
• As routine tasks are automated, engineering work is shifting toward a research-oriented culture focused on testing hypotheses
• Windsurf prioritizes hiring engineers with high agency who are willing to take risks and be wrong
• AI is increasingly becoming the "infinite workhorse" that handles specialized but routine programming tasks
• Technical interviews now face the challenge of evaluating candidates when AI can solve most standard coding problems
• Companies are evolving interview techniques to focus on open-ended problems and how candidates think through trade-offs
• Despite creating automation tools, Windsurf is increasing engineering hiring to tackle the vast scope of their mission
• Humans are becoming the bottleneck in development as they serve as the "slow bridge" between efficient AI systems
• The concept of "developer" may evolve into "builder," with everyone creating personalized software solutions through AI

Timestamp: [35:16-44:42]

Companies/Products:

• Windsurf - AI coding tool company discussed throughout the segment
• OpenAI - Referenced for their O-4 Mini model's performance on math competitions
• ChatGPT - Mentioned in the context of over-reliance in technical tasks

Technologies:

• O-4 Mini - AI model mentioned as solving competitive programming problems
• AR glasses - Referenced in future vision of personalized application development
• SaaS - Software as a Service model discussed as potentially being transformed by just-in-time software

Concepts:

• AMC (American Mathematics Competitions) - Math olympiad used as example of rapid AI progress
• Just-in-time software - Concept of AI dynamically generating software as needed rather than using pre-packaged solutions
• Vibe coding - Term for AI-assisted coding that some consider a passing trend
• Software development lifecycle - Framework including writing, reviewing, testing, debugging, and designing code
• Builder vs. Developer - Evolution of terminology as software creation becomes more democratized
• System design interviews - More open-ended technical assessment approach being used in the AI era
• Competitive programming - Mentioned as being dominated by AI models

Metrics:

• 99% reduction - Windsurf's mission to reduce the time it takes to build technology and apps
• 40-50 out of 100 units - Varun's estimate of how much of the software development process they've improved so far

Timestamp: [35:16-44:42]

The conversation turns to a surprising discovery: a significant number of Windsurf users have no coding background at all.

"It's actually a large number of our users."

The interviewer expresses surprise, noting that they had perceived Windsurf as primarily targeting professional developers rather than non-technical users. Varun shares their own surprise at this phenomenon:

"We were shocked by this too, because we were like, 'Hey, our product is an IDE,' but there's actually a non-trivial chunk of our developers that have never opened the editor up."

He explains that these users interact exclusively with Cascade, Windsurf's agent interface:

"They just live in Cascade. We have browser preview, so they just open up the browser preview, they can click on things and make changes."

Despite never directly engaging with the code, these users can still be productive because of Windsurf's codebase understanding capabilities:

"The benefit is because we kind of understand the code, when they come back to the repository and the code has actually gotten quite gnarly, we're actually able to pick up from where the developer left off—or the builder left off—and keep going from where they were."

While acknowledging that they haven't specifically optimized for this use case, Varun expresses amazement at how prevalent it has become.

Timestamp: [44:48-45:46]

The interviewer poses a strategic question about whether Windsurf might eventually split into distinct products for different user audiences or maintain a unified approach.

"Do you think in the long term that this ends up being one product that targets both of these audiences, or do you think actually there's different products for different audiences? There's like a Windsurf which is focused on serious developers who want to see the code and be in the details, and then there's maybe other products for folks who are totally non-technical, who don't even want to see the code."

Varun leans toward eventual unification but acknowledges the practical constraints of their current position:

"I don't know what the long term is going to look like. Something tells me it's going to become more unified, but one of the things that I will just say is like, as a startup, for us, even though we do have a good number of people, there's a limit to what we can focus on internally as well."

He explains that their current resource constraints prevent them from fully optimizing for both developer and non-developer experiences simultaneously. However, he suggests that as their technology improves, particularly in code understanding, the experience for non-developers will naturally improve as well.

Varun also notes an interesting market dynamic, where some competitors are approaching from the opposite direction:

"I assume a bunch of companies in the space will go from non-developers to then supporting an ability to edit the code, and I think we're starting to see this already where the lines are sort of getting blurred."

Timestamp: [45:54-47:00]

Varun explores a fundamental challenge in creating products for non-technical users: how to effectively evaluate and improve them when you can't rely on technical metrics.

"You need to care about it for your evals. That's maybe the hard part for me to imagine for the pure non-developer product—what is the hill you're climbing? If you're not kind of understanding the code, how do you know your product is getting better and better? That's like an open question."

He identifies a potential risk for companies that don't establish their own evaluation metrics:

"Are you completely relying on the base models getting better, which is fine, but then you should imagine your product is an extremely light layer on top of the base model, which is a scary place to be. That means you're going to get competed across all different axes."

This insight highlights a strategic vulnerability for AI products that don't develop proprietary evaluation frameworks or differentiated technology layers beyond the foundation models they leverage.

Timestamp: [47:05-47:29]

The interviewer brings up the once-prevalent concern about AI startups being mere "GPT wrappers"—thin layers of functionality on top of foundation models that could be easily replicated or obsoleted by advances from major AI labs.

"Something we've talked about a lot on this podcast is just the GPT wrapper meme. It has completely gone away, I feel, though every big release from one of the labs sort of brings it back a little bit, and everyone's a little bit scared that OpenAI is just going to eat everything."

Varun frames this challenge as a moving target that requires continuous innovation:

"The company, as I mentioned before, it's a moving goalpost, which is to say today if we're generating sort of 80-90% of all committed software, I think when the new model comes out, we're going to need to up our game. We can't just be at the same stage. Maybe we need to be generating 95% of all committed code."

He explains that their opportunity lies in the gap between what foundation models offer and what users ultimately need:

"I think our opportunity is the gap between where the foundation model is and what 100% is. And as long as we can continue to deliver an experience where there is a gap between the two, which I think there is as long as there's any human in the loop at all in the experience, there's a gap. We'll be able to go out and build things."

Varun offers a mathematical perspective that reframes the challenge in a more optimistic light:

"Let's suppose that you were to take the foundation model, and it's providing 90%—it's reducing the time it takes by 90%. That actually means if we can deliver one or two percentage points more, that's a 20% gain on top of what the new baseline is. If 90 becomes 92 or 93, which is still very, very valuable at that point, because effectively the 90 becomes the new baseline for everyone."

This approach focuses on relative rather than absolute advantages, suggesting that even small improvements beyond the capabilities of foundation models can deliver significant value.

Timestamp: [47:29-49:11]

When asked about advice for new startups in the AI coding space, Varun highlights specialized, high-value niches that aren't being adequately addressed.

"There are so many different pieces of how people build software, and I'm not going to say 'niche,' but there are so many different types of workloads out there. I've not really seen a lot of startups in the space that are just like, 'We do this one thing really, really well.'"

He illustrates this with a significant example:

"I'll give you an example—like we do these kind of Java migrations really, really well. Crazy enough, if you look at this category, the amount that people spend on this is probably maybe billions, if not tens of billions of dollars, doing these migrations every year. It's a massive category."

When asked to clarify what kinds of migrations he means, Varun expands:

"JVM 7 to 8 or something, JVM Rails versions. Even more than that actually—a lot of companies write COBOL. Have COBOL, and crazy enough, most of the IRS software is written in COBOL. Apparently in the early 2000s, they tried to migrate from COBOL to Java. I think it was a five-plus billion dollar project. Surprise, surprise—it didn't happen."

Beyond migrations, Varun identifies another high-value area:

"The second key piece is there are so many things that developers do that are also not making the product better, but important—like automatic resolution of alerts and bugs in software. That's also a huge, huge amount of spend out there."

He expresses enthusiasm about the potential for specialized solutions in these areas:

"I'd be curious to see what a best-in-class product in that category actually looks like. I'm sure someone that if they got truly in the weeds on that, they could build an awesome product, but I've not heard of one that is tremendously taken off."

Timestamp: [49:17-51:09]

The interviewer builds on Varun's point about language migrations, highlighting the massive market potential in modernizing legacy systems.

"One thing I like about them is that there's not just an opportunity for like two startups—each one of those is like a bucket that could have like a hundred large companies in it."

They mention a specific YCombinator company working in this space:

"We actually do have a company from S21 called Bloop that does these COBOL to Java migrations with agents."

Varun emphasizes just how significant this opportunity is:

"It's a very gnarly problem, but if you were to talk to any company that has existed for over 30 years, this is probably something that is costing them hundreds of millions a year."

This exchange highlights how AI can address long-standing, expensive technical challenges that have resisted previous solution attempts, creating opportunities for startups to capture substantial value.

Timestamp: [51:15-51:39]

As the conversation concludes, the interviewer asks Varun what advice he would give to his past self before starting this journey. His response centers on the courage to pivot rapidly.

"The biggest thing I would say is change your mind much, much faster than you believe is reasonable. It's very easy to kind of fall in love with your ideas over and over again, and you do need to, otherwise you won't really do anything."

Varun frames the willingness to pivot as a positive attribute rather than a failure:

"Pivot as quickly as possible and treat pivots as a badge of honor. Most people don't have the courage to change their mind on things, and they would rather kind of fail doing the thing that they told everyone they were doing than change their mind, take a bold step, and succeed."

This powerful conclusion encapsulates Varun's entrepreneurial philosophy and reflects the pivotal decisions that transformed his company from a GPU virtualization startup to a leading AI coding platform.

Timestamp: [51:51-52:19]

• A surprisingly large number of Windsurf users have no coding background but use the product exclusively through its agent interface
• The lines between developer and non-developer tools are blurring, with companies approaching from both directions
• A fundamental challenge for non-developer AI products is establishing evaluation metrics beyond relying on foundation model improvements
• Successful AI companies must continuously improve relative to foundation models, focusing on the gap between what models provide and what users need
• Even small percentage improvements beyond foundation model capabilities can represent significant value in a world where the baseline keeps advancing
• Massive opportunities exist in specialized, high-value niches like language migrations (Java, COBOL) and automated bug resolution
• Legacy system modernization represents a multi-billion dollar market opportunity that AI is uniquely positioned to address
• The willingness to change direction quickly and treat pivots as "badges of honor" rather than failures is crucial for startup success

Timestamp: [44:48-52:19]

Companies/Products:

• Windsurf - AI coding platform discussed throughout
• Cascade - Windsurf's agent interface used by non-technical users
• Bloop - YCombinator S21 company that handles COBOL to Java migrations
• OpenAI - Referenced in discussion about foundation model providers potentially "eating" startups

Technologies/Languages:

• Java - Programming language mentioned in migration examples
• COBOL - Legacy programming language used in many older systems including IRS software
• JVM (Java Virtual Machine) - Versioned runtime environment mentioned in migration examples
• Rails - Web application framework mentioned in version migration context

Organizations:

• IRS (Internal Revenue Service) - Government agency mentioned as having COBOL systems
• YCombinator S21 - Startup accelerator batch that included Bloop

Concepts:

• GPT wrapper meme - Industry concern about AI startups being thin layers on foundation models
• Browser preview - Feature that allows non-developers to use Windsurf without seeing code
• Moving goalpost - Varun's description of how AI startups must continually improve relative to advancing foundation models
• Legacy migrations - Converting code from older programming languages to modern ones
• Pivots as badges of honor - Varun's philosophy on embracing directional changes

Timestamp: [44:48-52:19]