finance
The Twenty Minute VC (20VC) interviews the world's greatest venture capitalists with prior guests including Sequoia's Doug Leone and Benchmark's Bill Gurley. Once per week, 20VC Host, Harry Stebbings is also joined by one of the great founders of our time with prior founder episodes from Spotify's Daniel Ek, Linkedin's Reid Hoffman, and Snowflake's Frank Slootman. If you would like to see more of The Twenty Minute VC (20VC), head to www.20vc.com for more information on the podcast, show notes, resources and more.
Osvald Nitzky, CPO at McCord, stated that open source model improvements do not cannibalize their core business. He explained that data is most valuable at the frontier of model performance, and open models simply raise the baseline of what customers are interested in, allowing McCord to continue growing by serving new capabilities.
Osvald Nitzky expressed skepticism about the claim that 90% of enterprise workflows can be handled by open models, suggesting these calculations might overlook latent demand for complex tasks. He cited long-horizon tasks like automating procurement teams as examples of use cases not yet captured.
Osvald Nitzky noted that large enterprises show skepticism and fear towards working with frontier models, particularly for core business workflows that differentiate them. However, for more general tasks like HR or procurement, companies are more open to using proprietary models.
Osvald Nitzky believes that approximately 50% of long-horizon workflows are currently addressed by top AI models, based on their Apex benchmarks. He also noted that for sufficiency-based workflows, like updating a CRM, performance ceilings are quickly reached, while areas like legal or medical advice offer continuous improvement potential.
Osvald Nitzky agrees with the notion that specialized models for each company will be the future, as it aligns with McCord's strategy of providing enterprise-specific evaluation and training data. He believes the market diversity for this depends on the demonstrable value customers receive from these specialized models.
Osvald Nitzky believes that companies are not currently facing an ROI problem with AI spend but are in a phase of exploration and experimentation. He suggests there is more tolerance for calculating ROI correctly, and while some spending is tightening, the rapid pace of AI development means ROI calculations can shift significantly.
Osvald Nitzky advised that optimizing AI spend depends entirely on the use case, particularly distinguishing between spending for growth and spending for less critical functions. He noted that for hypergrowth companies like McCord, high spending on AI is justified by insatiable customer demand and the need to scale rapidly.
Osvald Nitzky believes the current AI paradigm makes product management more challenging, requiring a focus on simplifying the product service area and identifying scalable workflows. He notes that with engineering less of a bottleneck, understanding user needs and identifying revenue-driving features has become the new bottleneck.
Apple has filed a lawsuit against OpenAI, alleging trade secret theft. The suit names Tani Tang, a senior executive at OpenAI and a former Apple veteran, and Chang Lu, a six-year Apple employee. The core allegation is that Lu took proprietary information from Apple to OpenAI, with Tang allegedly encouraging this action.
Meta is actively competing in the coding model space, with "Zach" being mentioned as back on X. The discussion implies Meta is looking to leverage its position against competitors like Codex and Claude.
SK Hynix has priced its Nasdaq listing at $26.5 billion, bringing its compute and infrastructure layer to the public domain. This move is seen as significant for the sector.
The accelerating pace of AI adoption introduces new risks, and existing security programs may not be equipped to handle them. Vanta is presented as a solution to monitor and mitigate these risks across various platforms and AI tools.
Deel aims to help founders scale startups faster by handling the complexities of global hiring, including payroll, visas, onboarding, HR, IT, EOR, benefits, and compliance. The service is used by over 40,000 companies.
Framer is highlighted as a pro AI website builder for creators and businesses focused on detail. Its AI agents help bridge the gap between AI-generated ideas and production-ready websites, offering features like custom code components and SEO optimization.
The discussion delves into the legal ramifications of Apple's lawsuit against OpenAI, focusing on trade secret theft. It highlights the risks for individuals involved, such as Chang Lu and Tani Tang, and the broader implications for employee mobility and corporate espionage in the tech industry, referencing California's employment laws.
Wix CEO Avishai Abrahami believes the public market undervalues his company, stating that its current market cap of $2.8 billion does not reflect the value of its Base44 product alone, which he estimates should be worth $8 billion. He argues that while peers in 'vibe coding' receive higher multiples, Wix is not being adequately recognized for its growth and product offerings.
Wix CEO Avishai Abrahami shared his unconventional sleep schedule, typically going to sleep between 5-6 AM and waking around 11 AM, which he finds beneficial for focused work. He contrasts this with the perceived necessity of early sleep schedules in some tech circles, noting that wearable tech companies like Aura have adapted their sleep profiles to include 'very late sleepers'.
Wix CEO Avishai Abrahami asserted that Salesforce's primary value proposition is the trust it instills in large companies to handle their sensitive customer data, rather than its CRM functionality. He believes this trust makes it incredibly difficult for competitors to displace Salesforce, even with advancements in other areas like 'cloud code projects'.
Wix CEO Avishai Abrahami described the company's recent buyback program as a 'disaster', comparing its success to the Titanic. He feels the public market is not properly valuing Wix, particularly its Base44 product, leading to a disconnect between the company's performance and its market capitalization.
Arvind Jain, co-founder of Glean, believes that enterprise leaders are both terrified of and dependent on frontier AI model providers. He acknowledges a skepticism towards these providers due to concerns about business continuity and over-reliance on technology, but also notes that many enterprises are exploring AI despite these fears.
Arvind Jain, co-founder of Glean, explained that the company started as an enterprise search engine to help employees find information across various systems. As AI models improved, Glean evolved into an AI platform, now functioning as a 'super set' of tools like ChatGPT, connecting with a company's internal context to act as a coworker.
Arvind Jain stated that the main reason enterprises are considering open-source AI models is the prohibitive cost of using frontier model providers. Many companies are finding that their AI budgets are exhausted within months due to the expense of these advanced models, making open-source alternatives increasingly attractive.
Arvind Jain highlighted a key concern for enterprises: the potential for operational dependence on frontier AI model providers. As AI agents handle more work, companies worry about losing control over their data and the accumulated institutional learning, which could become embedded within these external models.
Arvind Jain views open-source AI models as both a threat and an opportunity. He acknowledges that they pose a challenge to incumbents like OpenAI and Google, but also notes that the rapid improvement and accessibility of open-source models are driving innovation and allowing startups to build differentiated products on top of them.
Arvind Jain describes his entrepreneurial drive as stemming from a fear of losing rather than a thrill of winning. He believes this constant worry about what could go wrong is what fuels his persistence and success, even after achieving significant milestones like the IPO of Rubric.
Washington has lifted the ban on Fable Five, a move that shifts the regulatory landscape for AI companies like OpenAI and Anthropic. While the exact pre-approval process is still being finalized, this marks a significant change from the previous freedom to ship software without government permission.
Sam Altman has reportedly offered the US government a 5% stake in OpenAI, a move described by Jason Lamkin as "absurd." The reasoning behind this offer is unclear, but it deviates from the more tangible issues of AI safety and regulation.
DeepSeek is reportedly developing its own chip, signaling a move into hardware development. This news comes as the AI industry continues to rapidly evolve and companies seek to control their technological stacks.
Meta Compute has launched a new cloud business, which resulted in a 10% jump in its stock price. This move into the cloud computing market is seen as a positive development for the company.
Scott views the government's approach to AI regulation as fear-based and not based on facts. He believes that while lifting some bans is a step in the right direction, it's not enough, and governments should instead foster innovation.
Clay Bavor, formerly of Google, has co-founded Sierra, an AI company that has raised over $1.5 billion and is valued at nearly $16 billion. Bavor spent 18 years at Google, working on projects like Gmail and Google Photos before deciding to pursue entrepreneurship when the timing felt right with the rise of language models.
Clay Bavor explains that Sierra, his AI company, does not engage in its own pre-training of foundational models. Instead, they focus on fine-tuning existing open-weight models, a strategic decision driven by the immense capital expense and the rapidly depreciating nature of foundational models. This approach allows Sierra to leverage the investments of larger labs and hyperscalers while still building proprietary, specialized models.
Clay Bavor identified two key takeaways from his 18 years at Google: a willingness to invest deeply in the technology stack to build products and the importance of exceptional people. He highlighted Google's practice of building its own infrastructure and the value of managers who bet on employees, giving them more responsibility than they might initially deserve.
Clay Bavor commented on the low percentage of enterprise tasks currently automated by AI, suggesting it's a multi-faceted issue. He believes the problem lies not only in model capabilities but also in the diffusion of the technology within companies and gaps in the application layer, acknowledging that as open-weight models improve, the potential for automating more tasks grows.
Coinbase CEO Brian Armstrong announced a 50% reduction in AI spending, attributing it to a strategic shift towards utilizing open-source models over more expensive frontier models. This move has sparked debate about the efficiency and future of AI investment in the tech industry.
The discussion around Coinbase's AI spending cuts has ignited a broader debate about the authenticity of AI claims from CEOs. Some argue that many companies are engaging in 'performative' behavior rather than genuine innovation, while others see it as a necessary cost management step.
Many companies are realizing that despite increased AI spending, the expected gains in productivity and revenue have not materialized. This observation is leading to a re-evaluation of AI strategies and a focus on tangible results.
The increasing capability and adoption of open-source AI models are raising questions about their potential impact on the growth trajectory of proprietary frontier models. It remains uncertain whether open-source solutions will significantly disrupt the market.
Checkout.com processed over $300 billion in total volume in 2025, a 64% year-over-year increase, and returned to full-year EBITDA profitability. CEO Giam Puzhas described this period as the 'velocity years,' emphasizing relentless product building and ruthless focus on digital payments.
Invisible aims to solve the implementation challenges of AI in large companies by training models and adapting them to specific business contexts. They claim to have successfully used their technology to help the Charlotte Hornets NBA team achieve a draft pick in weeks, not seasons.
Bloom Energy CEO KR Sridhar highlighted the critical role of electricity in the AI revolution, stating that electricity, not AI models, will determine the ultimate winners. He noted that Bloom Energy's significant revenue growth, with revenues hitting $2 billion in 2025, was largely driven by demand from AI data centers. Sridhar also shared his long-term conviction in Bloom Energy's business model, which he has believed in since its inception 25 years ago.
Bloom Energy CEO KR Sridhar recounted a pivotal moment where Intel co-founder Andy Grove advised him on overcoming manufacturing challenges. Grove steered Sridhar away from relying on others to solve his product's issues, instead urging him to directly engage with the people building the product to understand and resolve the problems.
The rapid advancement of AI is creating a significant and growing demand for electricity. This trend is fundamentally altering the energy landscape, with electricity becoming a primary factor in the success of AI-driven technologies. The global electricity market is substantial, valued at $5.5 trillion.
Bloom Energy's founder and CEO, KR Sridhar, shared that his largest holding in Leo Aschenbrenner's fund is Bloom Energy, which constitutes 16% of the fund. The company has experienced remarkable growth, with its market cap reaching approximately $93 billion, a rise of over 1500% in a single year. Sridhar expressed his unwavering belief in the company's mission and technology since its inception 25 years ago.
KR Sridhar, CEO of Bloom Energy, shared his perspective on facing adversity, emphasizing that falling down is less important than getting up. He believes that difficult experiences shape individuals and that the key is to learn from them without dwelling on the past. Sridhar advocates for looking forward and maintaining the belief that tomorrow will be better.
Paul Erlanger, CEO of Fomo, announced their Series B funding round of $75 million at a $550 million valuation, with leadership from Index and Fred Wilson at USV. The company, which offers a mobile trading app for on-chain assets with plans to expand to equities and perpetuals globally, previously had its entire team forgo salary for the first eight months of building.
Fomo's CEO, Paul Erlanger, explains their unconventional early funding strategy, opting for 140 angel investors instead of institutions to solve the "cold start problem" for their consumer product. This approach aimed to create distribution by giving early users ownership, which Erlanger believes was core to Fomo's success.
When asked for advice on scaling to the first thousand users, Fomo's CEO, Paul Erlanger, emphasized the critical importance of direct user feedback. He suggests continuous iteration based on feedback from 10, 100, and then 1,000 users, highlighting that passionate users in the crypto industry have provided significant competitive advantage through their input.
Paul Erlanger disagrees with the 'financial super app' theory, arguing that platforms like Revolut or Robinhood are too bundled and lack intentionality. He believes Fomo's strength lies in its social graph and the ability for users to express specific theses across different market types, such as on-chain assets, equities, and prediction markets.
Fomo is considering integrating prediction markets, but is waiting to observe regulatory developments. Erlanger notes that their initial product versions were built on similar concepts to poll markets and Calshi, acknowledging them as great businesses but emphasizing the need to monitor regulatory flux before committing to a roadmap for these features.
Paul Erlanger highlights that 'perps' (perpetuals) and prediction markets are enabling retail investors to access capital markets earlier. He uses Shopify's IPO as an example of retail investors achieving private-scale returns in public markets, suggesting that pre-IPO markets and prediction markets are democratizing access to investment opportunities that were previously unavailable.