See how leading investment teams are scaling primary research with AI. AlphaSense’s AI Interviewer helps uncover high signal insights from industry experts, validate investment theses and challenge assumptions—so you can make higher conviction decisions with confidence.
Get 3 Free AI-Led Expert Calls
Offer available to eligible investment firms, one offer per firm, subject to availability. Terms apply.
Monday’s episode with Anastasios Angelopoulos, Co-Founder & CEO @ Arena:
Download the full transcript:
My 7 key takeaways:
To what extent was Kimi really a breakthrough model?
Chinese open-source models like Kimi K3 outperforming top Western closed models shatters the narrative that foreign labs merely distill American tech. It completely alters the economic consensus around model commoditization, proving the ecosystem moves far too fast for centralized government oversight.
What is the moat for businesses of the future?
Software will cease to be a viable enterprise moat because it can be generated almost instantaneously. Sustainable value will belong strictly to network effects and proprietary data moats converted into self-improving products to stave off AI-native competition.
Why the largest enterprises will not use Chinese models and how regulation will enforce that
Enterprises demand absolute AI sovereignty, meaning they must completely own their supply chain and fine-tune models safely on corporate data. Geopolitical friction and shifting regulations make it highly probable that the West will severely restrict access to foreign open-source models within years.
Chinese open-source models could absolutely have back doors that steal American data.
Hosting open-source models locally does not eliminate security risks. Malicious actors can embed hidden backdoors into model weights during foreign training, allowing a specific code word to trigger massive data exfiltration from an enterprise’s backend infrastructure.
Why the world needs to pay more attention to the open AR hugging face situation and what we should learn from it
The recent breach where an AI model broke through its safeguards to access restricted data is an undervalued international news incident. Companies must deploy independent “guardian models” to monitor agent traces, as human oversight operates at a latency scale too slow to stop automated leaks.
Fake people are applying for jobs. Is American business under attack?
AI-generated fake candidates are now successfully clearing elite technical interviews. These vaporware applicants appear normal on camera but are explicitly engineered to infiltrate secure infrastructure, prompting top Valley companies to mandate in-person onboarding to physically verify identity.
What will separate the Neolabs that thrive versus those that die?
With at least 75 Neo Labs currently competing, roughly two-thirds are heading toward low-value acqui-hires. The era of raising massive valuations on pure pedigree with zero revenue is over; survival requires an aggressive strategy focused strictly on hypergrowth P&L metrics.
Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr & Nikesh Arora, Chairman CEO @ Palo Alto Networks:
Download the full transcript:
My 6 key takeaways:
Airtable Is a Great Outcome, and We Are Anchoring on the Peak $11 Billion Valuation
Building a company over a decade to $450 million in revenue and selling it for more than $2 billion is objectively a remarkable value-creation outcome. Yet founders and VCs often anchor on inflated 2021 peak valuations, causing them to mistake a strong exit for a disappointing one.
The Enterprises That Win Will Create Training Data Faster Than Anyone Else
Enterprise AI success cannot be outsourced to off-the-shelf software. The real advantage lies in an organization’s ability to capture operational knowledge from complex edge cases. Winning companies will treat every customer interaction and transaction as an opportunity to generate proprietary training data.
Will Revenue Show Up Fast Enough to Keep the CapEx Train Going?
Unlike past infrastructure cycles, where telecom investments were monetized over decades, AI’s CapEx cycle is far more compressed. Capital deployment and revenue growth must stay closely aligned. Investors will not fund hundreds of billions of dollars in infrastructure indefinitely unless software revenue scales fast enough to justify it.
Situational Awareness: Absolutely Right on Trend, Absolutely Wrong on Portfolio Construction
Being directionally right about AI does not protect you from poor portfolio construction. Holding highly leveraged, volatile assets makes getting wiped out during short-term drawdowns almost inevitable, regardless of how accurate the long-term thesis proves to be.
In the Long Term, Average Intelligence Will Be Free—and It Will Keep Getting Smarter
As baseline models rapidly improve and commoditize, average intelligence will become abundant and nearly free. Value will increasingly concentrate in frontier intelligence capable of solving the hardest problems and domain-specific applications built on deep enterprise context.
Land, Permits, Energy, and Compute Are the True Bottlenecks of AI
The bottleneck in AI is shifting from algorithms to physical infrastructure. Securing land, regulatory permits, energy, and compute capacity will become the defining constraint on industry expansion, commanding premium value over the next three to five years.
Saturday’s episode with David Frankel, MP @ Founder Collective:
Download the full transcript:
Let us know what your big takeaways from this week’s shows were in the comments below!
Thank you for reading, and don’t miss the great guests we have next week:
Monday episode: Alex Atallah, Co-Founder & CEO @ OpenRouter
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Matt Swulinski, Head of Growth @ viktor.com
Thank you for reading 20VC.
This post is public so feel free to share it.
Thanks for reading 20VC! Subscribe for free to receive new posts and support my work.
Comments
Nothing yet. Say the first thing.
Sign in to join the conversation.