Key Takeaways
- AI represents the fastest and largest product shift in software history, accelerating adoption compared to prior platform waves.
- Three core AI investment themes include AI-native software, labor replacement, and applications using proprietary data.
- Defensibility, workflow ownership, and data moats are crucial for scaling AI applications beyond novelty.
- AI's societal impact is seen more as augmenting labor and filling difficult roles rather than wholesale job elimination.
- Successful AI companies leverage proprietary data, whether discovered or aggregated from public sources, to create unique value.
Deep Dive
- AI applications are driving significant net new revenue, surpassing earlier expectations for artificial intelligence's impact.
- The adoption of AI is accelerating rapidly, building on prior technology platforms like PCs, the internet, cloud, and mobile.
- Enterprise adoption is evident; one expense management example reported a significant increase in AI usage.
- Approximately 15% of adults globally use ChatGPT weekly for various daily tasks, indicating broad consumer integration.
- One core investment theme focuses on software directly replacing human labor, a market potentially larger than existing software categories.
- This addresses needs where hiring human labor is difficult, impossible, or cost-prohibitive, such as front desk receptionist tasks.
- Companies like Eve target plaintiff law firms, where AI assists with evidence collection and case valuation to increase case capacity for contingency-paid attorneys.
- The labor market's scale suggests substantial opportunities for AI to automate significant portions of human roles.
- Mission-critical AI applications require defensibility, focusing on owning end-to-end workflows and building contextual products.
- Proprietary data drives compounding competitive advantage, creating a 'walled garden' where increased usage improves the product.
- Companies like Eve become essential tools by acting as a system of record for end-to-end workflows in their respective industries.
- Moats are more critical than ever due to rapid software development, necessitating strong competitive advantages beyond novelty.
- AI augments labor by addressing tasks difficult to hire humans for, such as collecting payments or handling insurance claims.
- Salient, an auto loan servicing company, increased revenue collection by 50% through AI, also ensuring regulatory compliance.
- AI deployment can invert the value-to-cost equation, with the primary value proposition being increased revenue, not solely cost savings.
- AI solutions address critical needs in industries with high employee churn and complex, changing regulations.
- The integration of AI significantly enhances the value of proprietary data, creating powerful finished products.
- Open Evidence uses exclusive medical journal access to provide doctors with evidence-based insights, surpassing general AI tools.
- Vlex, a legal information company, quintupled revenue by adding AI capabilities to its database of Spanish legal records.
- AskLeo, a procurement product, leverages proprietary historical vendor contracts, like 50 previous Deloitte agreements, to prevent overspending.
- Evaluating AI investment opportunities involves contrasting startup success in prior eras with the current AI landscape.
- AI enables startups to build defensible, data-rich businesses by discovering new data sources or leveraging existing ones.
- Unlike the cloud era where incumbents often resisted, the AI wave sees existing businesses embracing AI-native transformations.
- New greenfield opportunities arise as AI makes previously unviable business models accessible at lower price points.
- Investment strategies include acquiring companies with existing client bases to transform them with AI, such as a stagnant debt collector.
- Monetizing existing assets through new channels, like Michael Jackson's estate leveraging digital distribution, provides a strategic analogy.
- Companies should aim to sell directly to end-users rather than acting as intermediaries, as exemplified by Vlex's direct data sales strategy.
- A pricing strategy similar to OpenAI focuses on direct customer engagement and data enrichment, enhancing base data with proprietary information.
- The consumer AI market leverages three core investment themes: AI-native, new categories, and proprietary data, with examples like Kria for AI-native design.
- Consumers often prefer aggregators of AI models, as different models have unique specializations, over first-party models from large tech companies.
- Foundational models can train consumer products sold directly, using proprietary data to differentiate from competitors like ChatGPT.
- Slingshot, an AI therapist, uses AI scribes to collect data from existing therapists to train its models.
- The VC firm operates as a 'media firm that monetizes with venture capital,' proactively creating content to establish expertise.
- They aim to be market leaders by publishing benchmarks, like an AI productivity benchmark, to identify promising companies.
- The firm leverages deep market knowledge to attract top entrepreneurs and win competitive investment rounds, as seen with Rillet.
- They facilitate connections, provide resources, and empower partners with a conviction-oriented decision-making process for investments.