Kite.com Reviews

Based on looking at the website, Kite.com, it’s immediately clear that this isn’t your typical product review.

As of November 16, 2021, Kite, the AI-powered coding assistant, has ceased operations and is no longer supported.

While the product itself is no longer available for use, understanding its journey, the challenges it faced, and the lessons learned can offer valuable insights into the complexities of bringing cutting-edge technology to market, particularly in the developer tools space.

The company’s founder, Adam Smith, openly shared the detailed reasons behind their shutdown, providing an unusual level of transparency that allows for a comprehensive look at what made Kite a significant, if short-lived, player.

Their ambition was to revolutionize coding with AI, aiming for a “10x improvement” in developer productivity.

Despite building a world-class team and attracting 500,000 monthly active users with minimal marketing, Kite ultimately failed to monetize its product.

This outcome highlights critical lessons for any startup, especially those operating at the bleeding edge of technological innovation where market readiness, monetization strategies, and the sheer cost of R&D can be formidable hurdles.

Find detailed reviews on Trustpilot, Reddit, and BBB.org, for software products you can also check Producthunt.

IMPORTANT: We have not personally tested this company’s services. This review is based solely on information provided by the company on their website. For independent, verified user experiences, please refer to trusted sources such as Trustpilot, Reddit, and BBB.org.

The Vision: AI-Assisted Programming and the “10x Improvement” Goal

Kite’s core vision was audacious: to leverage artificial intelligence to dramatically accelerate software development. They weren’t just aiming for incremental improvements. their stated goal was a “10x improvement” in developer productivity. This wasn’t some minor tweak. it was a fundamental shift they believed AI could bring to the coding process.

Pioneering AI for Developers

From 2014 to 2021, Kite positioned itself as a startup at the forefront of AI application in software development.

They aimed to build an intelligent coding assistant that could understand context, suggest completions, and generally make developers more efficient.

At a time when AI in programming was still largely theoretical for many, Kite was actively building and iterating.

• Early Innovator: Kite was one of the earliest players to truly focus on bringing advanced AI into the developer’s integrated development environment IDE.
• Focus on Python: While they expanded, their initial and strong focus was on Python, a language increasingly popular among data scientists and AI/ML practitioners.
• Contextual Completions: Beyond simple autocomplete, Kite sought to provide contextual completions, understanding the developer’s intent and the structure of their code.

The Challenge of “10+ Years Too Early”

• State of ML on Code: Smith explicitly states that the “state of the art for ML on code is not good enough” for the kind of profound impact Kite envisioned. Even with their advanced AI, it “fell short of the 10x improvement required to break through.”
• GitHub Copilot as a Reference: Interestingly, Smith points to GitHub Copilot built by GitHub in collaboration with OpenAI as a contemporary example. As of late 2022, Copilot “still has a long way to go,” particularly because “state-of-the-art models don’t understand the structure of code, such as non-local context.” This validates Kite’s earlier struggles.
• Engineering Intensive Problem: The problem of synthesizing code reliably with AI is “very engineering intensive” and could cost “over $100 million” to build a production-quality tool. This highlights the astronomical investment needed for true breakthroughs in this area, which few, if any, have fully committed to yet.

Product Development and User Acquisition: A Long Road to Product-Market Fit

Kite’s journey through product development and user acquisition illustrates both significant successes and critical missteps in their strategic sequencing.

They poured immense effort into building the right team and product, eventually reaching a substantial user base, but the timeline was protracted and ultimately unsustainable without a clear path to monetization.

Sequencing the Startup Journey: Team, Product, Distribution, Monetization

Adam Smith candidly laid out their strategic sequencing: team first, then product, then distribution, and finally monetization. While this order can make sense for highly complex, technically challenging products, it proved to be their undoing without sufficient runway or early validation of revenue streams.

• Team Building Excellence: Kite successfully built a “world-class engineering team” from top backgrounds, attracting talent even with “below-market salaries” due to the compelling vision. This was a clear success, showcasing their ability to attract top-tier talent.
• Product Development Challenges 5 Years to PMF: It took Kite five years until 2019, from a 2014 start to reach what they considered “product-market fit.” This extended period of iteration and “heavy engineering lifts” consumed significant resources and time.
  • Early Struggles: The difficulty in building the product meant a long period of development before it truly resonated with users in a meaningful way.
  • Technical Complexity: The nature of AI-assisted programming inherently involves deep technical challenges, which naturally extend development cycles.

Growing a User Base: 500,000 Monthly Active Developers

Despite the long development cycle and minimal marketing, Kite achieved remarkable success in user acquisition, growing its user base to 500,000 monthly-active developers. This is a staggering number for a developer tool, especially one that wasn’t free from technical glitches in its early stages.

• Organic Growth: The fact that they achieved this with “almost zero marketing spend” speaks volumes about the inherent value developers saw in the concept, or at least their willingness to experiment with cutting-edge tools.
• Developer Enthusiasm: Smith himself notes that “Developers are so authentically passionate about their craft and anything that can advance it.” This intrinsic motivation likely drove significant organic adoption.
• Community Engagement: Kite actively engaged with its user base through “countless emails, Github posts, and live conversations,” which helped foster a loyal, albeit non-paying, community.

The Monetization Dilemma: Why 500k Users Didn’t Translate to Revenue

This is where the story of Kite takes its most critical turn.

Despite a strong product eventually and half a million active users, the company failed to generate revenue. Drawsql.com Reviews

This highlights a fundamental challenge in the developer tools market: the disconnect between individual utility and willingness to pay.

“Individual Developers Do Not Pay for Tools”

Kite’s diagnosis was stark and direct: “individual developers do not pay for tools.” This is a profound insight for anyone developing software for this audience. While developers are keen adopters of new tech, their personal budgets often aren’t allocated to professional software unless it’s a mandatory, employer-provided solution.

• Free vs. Paid Expectations: Many developer tools, especially those that enhance productivity rather than providing core functionality, often operate on a freemium model where the vast majority of users never convert.
• Employer-Funded Purchases: The key realization was that “Their manager might” pay, but managers look for different value propositions.

The Value Proposition for Engineering Managers

Engineering managers, who control the budgets, have different criteria for purchasing software.

They aren’t swayed by marginal personal productivity gains.

They’re looking for clear, quantifiable improvements that impact the team’s overall output or strategic goals.

• Discrete New Capabilities: Managers “only want to pay for discrete new capabilities.” This means a tool must offer a distinct, measurable advantage that directly contributes to team efficiency or project success.
• “18% Faster” Didn’t Resonate: Kite’s AI making developers “18% faster when writing code did not resonate strongly enough.” This illustrates the difficulty in quantifying and selling abstract productivity gains to decision-makers. Managers need more compelling, tangible benefits to justify expenditure, especially for tools that might feel like “nice-to-haves” rather than “must-haves.”

The Pivot That Never Happened: Code Search and Team Fatigue

Facing the grim reality of non-monetization, Kite explored a pivot, identifying “code search” as a potential new direction.

This strategy aimed to leverage their existing AI technology and developer footprint.

However, after seven years of intense work, the team’s capacity for another major shift had simply run out.

Identifying a New Direction: Code Search

The decision to explore code search was a calculated one, designed to capitalize on Kite’s existing assets:

• Leveraging AI Technology: Kite had built sophisticated AI models for understanding code. Code search, especially intelligent, semantic search, could directly benefit from this core competency.
• Bottoms-Up Developer Footprint: With 500,000 active developers, Kite had an established user base. A code search tool could potentially be adopted by these users, providing a clearer path to monetization, perhaps through enterprise-level subscriptions or advanced features.
• Customer Discovery: The pivot was not arbitrary. it stemmed from “a lot of customer discovery,” indicating a data-driven approach to identifying a market need that could be addressed by their technology.

The “Soft Landing” and Team Exhaustion

Despite the promising new direction, the pivot was ultimately abandoned. The primary reason was team fatigue. After “seven years of intense work and early-stage-startup stress,” the team was “too tired to pursue that pivot.” Mdwa.com Reviews

• Burnout: Startup life is inherently demanding, but seven years at the cutting edge, with intense pressure and without significant financial success, inevitably takes a toll.
• Sacrifices Made: Smith acknowledges the “innumerable sacrifices” made by the team, including “below-market salaries to extend our runway” and “long days.” This level of dedication, while admirable, has a finite limit.
• Decision for a “Soft Landing”: Instead of pushing through another arduous pivot, the decision was made to find a “soft landing,” indicating a controlled shutdown rather than a chaotic collapse. This allowed for a more graceful exit for employees and investors.

The Human Side of Startup Failure: Courage, Risk, and No Regrets

Adam Smith’s farewell message goes beyond business metrics, delving into the human element of building and shutting down a startup.

It’s a powerful testament to the courage of founders, teams, and investors, and a refreshingly candid perspective on learning from failure without succumbing to regret.

Acknowledging Courage and Risk-Taking

Smith explicitly recognizes the “courage put into pursuing this journey, from users, team, and investors alike.” This isn’t just corporate platitude.

It underscores the immense personal and financial risk involved in early-stage ventures.

• Investor Risk: He thanks investors for their belief and acknowledges that a “large percentage” of their investments won’t work out, celebrating their “risk-taking” as a “necessary ingredient of progress.” This perspective is crucial for fostering innovation.
• Team Contribution: Smith highlights the “incredible how much life force goes into making early-stage startups move forward,” describing it as an “act of optimism and selflessness contribution to the world.” The team’s dedication, working long hours and taking lower salaries, exemplifies this.

Embracing Failure and Avoiding Regret

One of the most poignant aspects of the message is Smith’s philosophy on failure: “While I believe in taking responsibility and learning from failure, I do not believe in regret.”

• Learning from Failure: He emphasizes the importance of dissecting what went wrong to extract valuable lessons for future endeavors. This is the cornerstone of iterative progress in entrepreneurship.
• 20/20 Hindsight Trap: Smith warns against the “mistake of using 20/20 hindsight to second-guess past decisions.” This is a critical psychological insight for anyone facing setbacks. decisions are made with the information available at the time, not with perfect foresight.
• Looking Back with Love: Despite the outcome, he looks back “with love on our courage to take the risk.” This healthy perspective allows individuals to move forward without being bogged down by negative self-reproach.

The Legacy and Open-Sourced Code: What Remains of Kite

While Kite as a product is no longer supported, its journey wasn’t entirely in vain.

The company’s contributions to the field of AI for code, coupled with its open-sourced technology, leave a tangible legacy that can still benefit the broader developer community.

Open-Sourced Contributions on GitHub

A significant part of Kite’s legacy is its open-sourced code, now available on GitHub.

This is a common practice for startups that shut down, allowing their intellectual property to live on and contribute to public knowledge and future development.

• Data-Driven Python Type Inference Engine: This is a crucial component for understanding and analyzing Python code, enabling better static analysis and tooling.
• Python Public-Package Analyzer: Useful for understanding dependencies, potential issues, and structure within the vast Python ecosystem.
• Desktop Software and Editor Integrations: These components showcase how Kite integrated directly into developers’ workflows, providing valuable insights into building similar tools.
• GitHub Crawler and Analyzer: This part of their infrastructure demonstrates how they collected and processed vast amounts of code data to train their AI models.
• Much More: The open-source repository likely contains various other modules and tools that offer insights into their AI architecture and data processing pipelines.

Impact on Alumni and the Future of AI in Programming

The experience gained by the Kite team members was invaluable, leading many to new ventures and contributing to the broader tech ecosystem. Silk.com Reviews

• Alumni Founders: Smith proudly mentions that “alumni who have already founded startups such as Silo, Zippy, Pipekit, Skipper, StandardCode, Firezone,” and anticipates “many more to come.” This is a testament to the talent and entrepreneurial spirit fostered at Kite.
• Pioneering Spirit: Kite was a “pioneering startup” in the area of AI revolutionizing programming. Even though they “were not the company to land it,” their efforts undoubtedly pushed the boundaries and provided valuable lessons for successors.
• Optimism for the Future: Smith remains “optimistic” about AI revolutionizing programming, believing it will lead to a “step-function increase in what they can do for us.” Kite’s journey, even in failure, contributed to this ongoing pursuit.

Lessons Learned from Kite.com’s Journey

Kite’s story, while ending in a shutdown, is a rich case study in product development, market timing, and the brutal realities of startup life.

Its transparency offers critical insights for founders, developers, and investors alike.

The Importance of Market Timing for Deep Tech

Kite’s experience powerfully illustrates that even with groundbreaking technology and an exceptional team, being “10+ years too early to market” can be a fatal flaw.

The ecosystem, the underlying technology, and even user expectations need to evolve to a certain point for revolutionary products to truly take hold.

• Maturity of AI Models: The limitations of ML models for code at the time e.g., not understanding non-local context were a significant barrier.
• Infrastructure and Cost: The prohibitive cost $100M+ to build a production-quality, reliable code synthesis tool emphasizes that not all ambitious visions are feasible within typical startup funding cycles.

The Nuance of Monetization in Developer Tools

The stark realization that “individual developers do not pay for tools” is a crucial lesson.

While developers are avid users and early adopters, their personal willingness to pay for productivity enhancements is often low.

• Enterprise vs. Individual Sales: Companies aiming to build developer tools need a clear strategy for how they will monetize. This often involves targeting enterprises, where purchasing decisions are made by managers or teams looking for quantifiable ROI, rather than relying on individual subscriptions.
• Clear ROI for Managers: The “18% faster” didn’t sell. Future tools need to articulate a more compelling and measurable return on investment for managers and organizations.

The Inevitable Toll of Sustained Startup Stress

Seven years of intense work without the breakthrough success led to team fatigue, demonstrating the human cost of long-term, high-pressure startup environments.

• Burnout Prevention: Founders need to be mindful of team well-being and recognize when the cumulative stress can prevent a team from executing a necessary pivot.
• Realistic Timelines: The five years to product-market fit, combined with the subsequent pivot exploration, highlights that deep-tech startups may require exceptionally long runways and patient investors.

Why Kite’s Story Matters for Today’s AI Landscape

A Precursor to Modern AI Coding Tools

Kite was a genuine pioneer.

Its attempts to build AI-assisted programming laid groundwork and provided invaluable lessons that current market leaders are benefiting from.

• Early Research and Development: The challenges Kite faced — particularly around AI’s understanding of code structure — are still active areas of research and improvement for today’s AI models.
• Market Validation of need, not solution: Kite’s 500,000 monthly active users validated that developers want AI assistance. The problem wasn’t a lack of desire, but rather the maturity of the technology and the business model.

Lessons for Future AI Ventures

Kite’s closure offers a template for the complexities involved in building and commercializing cutting-edge AI. Mindomo.com Reviews

• Beware of Hype: While AI is powerful, its practical application needs to be grounded in what the technology can actually deliver today, not just what it might deliver in a decade.
• Business Model First: The failure to monetize, despite a strong product and user base, underscores the absolute necessity of integrating a viable business model from much earlier stages, especially for venture-backed companies.
• The Endurance of Innovation: Even failed experiments contribute to progress. Kite’s open-sourced code and the experience of its team continue to ripple through the tech community, proving that even a “failure” can be a catalyst for future successes.

---

Frequently Asked Questions

What was Kite.com?

Kite.com was a startup that developed an AI-powered coding assistant designed to help developers write code faster and more efficiently by providing contextual completions and documentation.

Is Kite.com still active or supported?

No, as of November 16, 2021, Kite.com ceased operations and is no longer actively supported or maintained. The company shut down its services.

Why did Kite.com shut down?

Kite.com shut down primarily because it failed to monetize its product effectively, despite attracting 500,000 monthly active users.

The founder cited being “10+ years too early to market” for AI-assisted programming and the difficulty in convincing individual developers to pay for tools.

Did Kite.com achieve product-market fit?

Yes, according to the founder, Kite.com did reach product-market fit in 2019, five years after the company started.

However, this fit did not translate into a viable business model.

How many users did Kite.com have?

Kite.com successfully grew its user base to 500,000 monthly active developers.

Was Kite.com’s technology open-sourced?

Yes, much of Kite.com’s code has been open-sourced and is available on GitHub.

This includes their data-driven Python type inference engine, Python public-package analyzer, desktop software, and editor integrations.

What kind of AI technology did Kite.com use?

Kite.com used advanced AI and machine learning models to understand code structure and provide intelligent code completions, documentation lookups, and other productivity enhancements for developers. Eco.com Reviews

How did Kite.com’s AI compare to tools like GitHub Copilot?

Kite was a pioneer in the field, preceding the widespread adoption of tools like GitHub Copilot.

The founder noted that even as of late 2022, Copilot faced similar challenges regarding AI’s understanding of code structure, validating Kite’s earlier struggles.

What was Kite.com’s monetization strategy?

Kite’s product failed to generate revenue.

Their diagnosis was that individual developers typically do not pay for tools, and while managers might, Kite’s productivity gains e.g., making developers 18% faster did not resonate strongly enough as a discrete, valuable capability for enterprise purchases.

Did Kite.com try to pivot its business model?

Yes, Kite explored pivoting towards code search, which could leverage their AI technology and existing developer footprint.

However, the team was too fatigued after seven years of intense work to pursue this new direction.

What lessons can be learned from Kite.com’s failure?

Key lessons include the importance of market timing for deep tech, the challenge of monetizing individual developer tools, and the significant toll that sustained startup stress can take on a team.

Were Kite.com’s team members able to find new opportunities?

Yes, the founder mentioned that many Kite alumni have gone on to found their own successful startups, such as Silo, Zippy, Pipekit, Skipper, StandardCode, and Firezone, demonstrating the high caliber of the team.

What integrations did Kite.com offer?

Kite offered integrations with popular IDEs and editors such as VS Code, IntelliJ, PyCharm, Sublime Text, Atom, Spyder, WebStorm, JupyterLab, JupyterHub, and Vim.

Did Kite.com support multiple programming languages?

Kite initially focused heavily on Python and expanded to support JavaScript completions, though their core strength was in Python. Nfx.com Reviews

What specific open-source components are available from Kite.com?

The open-sourced components include their data-driven Python type inference engine, Python public-package analyzer, desktop software, editor integrations, and a GitHub crawler and analyzer, among others.

How does Kite.com’s story relate to the current AI boom in coding?

Kite’s story serves as a crucial historical case study, demonstrating the early challenges and potential of AI in coding, providing valuable context for understanding the advancements and continued hurdles faced by today’s AI-powered development tools.

Did Kite.com raise significant funding?

The founder thanked investors for committing their capital, indicating that Kite did receive venture funding, but the specific amounts are not detailed on the farewell page.

What did the founder of Kite.com say about regret?

Adam Smith, the founder, stated, “While I believe in taking responsibility and learning from failure, I do not believe in regret.” He emphasized learning from the past without being held back by hindsight bias.

Was Kite.com’s product fully developed when it shut down?

Kite had reached product-market fit by 2019, meaning the product was mature enough to resonate with users, but the business model was not.

What was the original goal of Kite.com?

The original goal of Kite.com was to use AI to achieve a “10x improvement” in developer productivity, dramatically accelerating the rate of software development.