TheOnePoint: AI and Data in the Venture Business
Massive and rapid advancements in AI have captured the attention of various sectors, including startups, VCs, corporates, and Big Tech. While the focus of the broader ecosystem is often on AI's technical innovations, the application of AI within venture capital remains less discussed.
So, to discuss this topic from multiple dimensions, I was joined by Gabriel Shin from Landscape.
Key points that we covered:
Listen to the complete podcast.
A concise (🙏🏼 ChatGPT'ed) version of the recording is below.
+++ I'd recommend listening to the original recording +++
Rohit Yadav (00:00): Welcome to The One Point podcast. AI has captured everyone’s imagination, from VCs to big tech spending billions. The GenAI ecosystem only emerged in the last 18 months, and foundation models are just six years old. While AI is all over the news, one area that doesn’t get much attention is its impact on the venture business. Today’s guest is Gabriel Shin to discuss AI and data in venture capital. Welcome, Gabriel.
Gabriel Shin (01:07): Thanks for having me. Great to be here on a Friday night.
Rohit Yadav (01:09): Quick intro: Gabriel worked in banking before jumping into startups. He joined Vauban, which was acquired by Carta, where he led business development in the US and Europe. Now, he’s co-founding Landscape, building an AI operating system for venture firms. Excited to discuss this!
Gabriel Shin (01:59): Thanks! At Landscape, we’re building an AI system for venture capital, aiming to bring transparency to private markets. We saw inefficiencies and wanted to fix that using data-driven approaches, AI, and automation.
Rohit Yadav (02:40): You’re focused on VC now, but do you plan to move into other asset classes?
Gabriel Shin (02:50): Yes, we’re starting with VC but aim to expand to other areas like private equity using the same data-driven approach.
Rohit Yadav (03:03): What motivated you to join Landscape?
Gabriel Shin (03:14): At Carta, I saw inefficiencies in the investing process. Landscape tackles the investment phase with AI, data, and automation. Investors are time-poor, and tools like this can help save time, drive value, and improve decision-making.
Rohit Yadav (04:00): Private markets are booming! Where do you think we are in the AI cycle?
Gabriel Shin (05:22): We’re in a transitional phase. AI has evolved from experimentation to real applications, but it still requires human oversight. In the future, AI will likely become fully autonomous, though getting from 90% to 100% accuracy will be challenging.
Rohit Yadav (06:25): I see it as AI’s second act. Startups are adjusting to real-world expectations now. What’s your view on how AI applications are evolving?
Gabriel Shin (07:26): AI evolution, like OpenAI’s ChatGPT, made some applications obsolete and created better use cases for others. Finding product-market fit is now crucial for AI startups to deliver real results.
Rohit Yadav (08:00): What’s currently possible with AI and data?
Gabriel Shin (08:16): AI combined with data allows us to create algorithms, predict outcomes, and generate insights without explicit programming. Concepts like synthetic data are also interesting, as they help build models more effectively.
Rohit Yadav (09:07): Is there any overhyped area in AI right now?
Gabriel Shin (09:17): There’s hype around AI displacing jobs, but I think it will mostly take over repetitive tasks, allowing people to focus on creative and relational work. Some jobs will be affected, but people can upskill.
Rohit Yadav (10:22): What’s a top AI use case you see coming in the next year?
Gabriel Shin (10:53): Everyone’s waiting for ChatGPT 5! The challenge now is improving accuracy from 90% to 100%. Reaching full autonomy will be exciting but difficult.
Rohit Yadav (11:41): Let’s talk about AI in venture capital. Where does Landscape focus in the VC value chain?
Gabriel Shin (13:04): We focus on the investment phase, helping VCs with sourcing, due diligence, and evaluation using data-driven insights. We aggregate data from sources like LinkedIn and Crunchbase to identify relevant companies faster.
Rohit Yadav (14:04): How many VCs are using AI in this phase now?
Gabriel Shin (14:05): More VCs are exploring AI now, and it will eventually become essential. AI tools make investors 10x more efficient, speeding up processes like sourcing and evaluation.
Rohit Yadav (15:37): If everyone uses AI tools, where’s the alpha?
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Gabriel Shin (16:33): The alpha lies in the ability to win deals, not in proprietary deal flow. VCs need to focus on relationships and value creation rather than just seeing the whole market.
Rohit Yadav (17:24): So tools like Landscape help with discovery, but the real win is in securing the deal.
Gabriel Shin (18:01): Exactly. AI gives VCs more time to build relationships with founders, which is key to winning deals.
Rohit Yadav (19:15): What are some early examples of AI in VC?
Gabriel Shin (19:15): Funds like EQT with Motherbrain and SignalFire have taken data-driven approaches for a while. Many firms are now using AI for sourcing and due diligence.
Rohit Yadav (21:01): Where is Landscape being used the most?
Gabriel Shin (22:16): Mostly by early-stage funds, but we’re seeing interest from Series A and later-stage funds as well. They need tools to become more data-driven and efficient.
Rohit Yadav (24:17): Will AI replace VC managers?
Gabriel Shin (24:52): No, AI will make them more efficient, but human judgment will still be critical. AI enriches information, but people will continue making final decisions.
Rohit Yadav (25:53): How will the role of junior VCs change?
Gabriel Shin (26:59): Junior roles focused on data aggregation and research may be impacted, but relationship-building and people skills will remain essential. Smaller firms may rely more on partners with AI tools.
Rohit Yadav (28:13): Do you see more senior tech operators starting VC firms?
Gabriel Shin (28:45): Yes, platforms like AngelList have democratized venture creation, and tools like Landscape will make it easier for smaller VC firms to thrive.
Rohit Yadav (32:09): How do you technically design your product to filter out noise and deliver accurate data?
Gabriel Shin (33:01): We use a combination of AI, proprietary scrapers, and third-party APIs to process data accurately. We aim for 100% accuracy, especially since trust is crucial in financial services.
Rohit Yadav (34:26): Do investors provide input into your platform’s results?
Gabriel Shin (34:54): Yes, we have our own algorithm, but investors can customize it based on their specific criteria, allowing more flexibility.
Rohit Yadav (35:37): How do you help narrow down the best opportunities?
Gabriel Shin (36:32): We track metrics like web traffic, team composition, product reviews, and market trends to give investors a detailed view of how companies are performing, complemented by their proprietary insights.
Rohit Yadav (37:22): What’s your secret sauce?
Gabriel Shin (37:31): It’s continuous product iteration and learning from customers. Our deep understanding of private markets and talented engineers give us an edge.
Rohit Yadav (38:51): Is there a first-mover advantage in AI for VC?
Gabriel Shin (39:05): No, it’s more about delivering the best product and listening to customers. There will be fragmentation initially, but eventually, consolidation will happen.
Rohit Yadav (40:32): How do you stay motivated?
Gabriel Shin (41:26): I have a good support network, stay healthy, and take time away from work to recharge. Passion for building in this space also keeps me going.
Rohit Yadav (41:48): What’s the role of advisors in your journey?
Gabriel Shin (42:20): Advisors have been crucial. We keep open communication, bounce ideas off them, and get valuable feedback.
Rohit Yadav (43:17): Anything inspirational you’ve read recently?
Gabriel Shin (43:55): I’m reading Focus, which talks about how our digital world is designed to distract us. Disconnecting from screens and spending time in nature helps me regain clarity and focus.
Rohit Yadav (44:45): Thanks, Gabriel! Wishing you all the best with Landscape.
Gabriel Shin (44:46): Thank you! Appreciate your time.
Disclaimer: All views are personal. The content above is for informational purposes only and is not investment advice.