What Ramp’s data tells us about AI, unemployment and more with CEO Eric Glyman | E2192
🎯 Summary
Podcast Episode Summary: What Ramp’s Data Tells Us About AI, Unemployment and More with CEO Eric Glyman | E2192
This episode of This Week in Startups features Jason Calacanis and Alex Wilhelm interviewing Eric Glyman, CEO and Co-founder of Ramp, focusing heavily on analyzing aggregated, anonymized corporate spending data to derive insights into the current economic and technological landscape, particularly concerning AI adoption and employment trends.
1. Focus Area
The discussion centers on FinTech/Corporate Spend Data Analysis, leveraging Ramp’s platform data (over $100 billion spent annually across 50,000 organizations) to create a real-time economic index. Key themes include the impact of AI adoption on business spending, employment dynamics (static team sizes vs. low unemployment), and the information asymmetry prevalent in financial markets (highlighted by a recent, suspicious crypto trade).
2. Key Technical Insights
- Data as an Economic Index: Ramp’s aggregated, anonymized spend data functions as a powerful, public economic indicator, allowing observers to track real-time trends in SaaS adoption, vendor performance, and sector growth (e.g., AI model layer spending).
- Price Intelligence Utility: Ramp’s “Price Intelligence” feature allows businesses to benchmark their vendor contracts (like Salesforce) against what the rest of the market is paying, democratizing data previously only available to high-level hedge funds.
- AI Leverage on Productivity: The data suggests that large, established tech companies are achieving higher revenue/valuation per employee, indicating that technology (and likely AI) is significantly increasing leverage per worker, leading to “static team sizes” despite growth.
3. Market/Investment Angle
- AI Vendor Leaders: The data highlights top SaaS vendors by new customer count (OpenAI, Anthropic, Canva) and by new spend (HubSpot, Carta, Vanta), signaling where startup capital is flowing for essential infrastructure and AI tools.
- Market Brittleness & Geopolitics: The immediate, severe market reaction (trillions wiped off market cap) to a potential Trump tariff announcement underscores the current fragility of markets sensitive to geopolitical uncertainty.
- Crypto Risk Assessment: The discussion surrounding a potential $200 million insider trade in crypto emphasizes the lack of regulatory oversight, warning listeners that in the unregulated “global casino” of crypto, participants who aren’t running the market are likely “the sucker at the table.”
4. Notable Companies/People
- Ramp (Eric Glyman): The central company, providing the data set and expanding AI-driven FinTech solutions.
- OpenAI & Anthropic: Mentioned as top recipients of new customer acquisition, confirming the high demand for foundational AI models.
- Zillow: Used as a historical example of how publishing proprietary data (Zestimates) can generate massive earned media and market engagement.
- Joshua DeVos (CoinDesk) & CoffeeZill: Mentioned in the context of reporting and analyzing the suspicious crypto trade.
5. Regulatory/Policy Discussion
The conversation heavily touched upon the lack of comprehensive regulation in the crypto space. The suspicious trade demonstrated that the traditional rules against insider trading are difficult (or impossible) to enforce in the decentralized, anonymous global crypto market, leading to concerns about information asymmetry and manipulation.
6. Future Implications
The conversation suggests a future where productivity per employee continues to rise due to AI integration, potentially leading to a structural shift where fewer employees are needed for high-scale operations. Furthermore, the lack of jobs for recent graduates might push a new cohort of frustrated, smart individuals toward entrepreneurship (e.g., applying to accelerators like Y Combinator).
7. Target Audience
This episode is highly valuable for Venture Capitalists, Startup Founders, Finance Professionals, and Technology Strategists interested in real-time economic indicators, the practical adoption curve of AI tools, and the evolving dynamics of corporate employment.
Comprehensive Summary
The podcast episode begins by dissecting recent market chaos triggered by potential Trump tariffs on China, noting the $2 trillion market cap wipeout and the subsequent bounce. A significant tangent addresses a highly suspicious $700 million short trade made in the crypto market 30 minutes before the tariff news broke, netting the trader up to $200 million. This event serves as a stark illustration of the information asymmetry and regulatory vacuum in crypto, leading hosts to caution listeners that without robust rules, participants are essentially betting against the “casino operators.”
The main segment features Eric Glyman, CEO of Ramp, who discusses how his company leverages $100 billion in annual corporate spend data to provide unprecedented economic transparency. Glyman emphasizes that this data is now open-sourced via ramp.com/data, contrasting it with the past where such granular insights were expensive, proprietary assets for hedge funds. The data revealed that OpenAI and Anthropic are leading in new customer acquisition among AI vendors, while companies like Carta and Vanta dominate in new spend for startup infrastructure.
A key discussion point revolves around unemployment and team size. Despite the Fed targeting low unemployment (currently 4.2%), large tech companies are exhibiting “static team sizes”—maintaining the same headcount despite revenue growth. Glyman posits this isn’t necessarily negative; instead, it reflects massive leverage per employee driven by technology adoption, potentially freeing up talent to start new, lean companies rather than remaining in “mid-level hell” at mega-caps. The hosts connect this to the difficulty recent graduates face finding entry-level roles, suggesting AI is automating those tasks,
🏢 Companies Mentioned
💬 Key Insights
"I think there have been very threatened by some of the new speedruns from a16z, ARC, Bicycoya, Pear has their summer program. We have Launch Accelerator and Founding University for a long time. We're not a new entrant. Techstars is coming back, Antler. All of these programs are better for founders, in my mind. They can go into YC. Not that YC is bad. YC is, you know, as good. But I think these other programs are better because they give better terms."
"But the deal's not signed until the deal's signed. You have the right to back out of it."
"Boohoo YC Combinator complaining about this and using the YC brand to say is the YC dropout. Harvard doesn't complain when Zuckerberg does it. And in fact, YC Combinator is known for asking that question: 'Tell us when you broke some rules.' I don't have the exact question, but they ask people and they sort for people like Sam Altman, who are rule breakers, who do crazy things, like take a nonprofit for open source, you know, make it a for-profit."
"I think even for the first month that people go and take, you know, do transactions, I think we, you know, one shot see, you know, it's 90% plus accuracy on our recommendations are ultimately accepted. In every progressive month that that goes in, it tends to the 95, 99, and goes from there."
"And so, if our customers are not saying like, "Hey, I'm coming to you for, you know, go sell me the AI product," they're just saying, "I want to close my books faster, I want, you know, convenience to get, you know, the expenses in quicker." And so, if it's easier and it's quicker and intuitive and it's embedded, they'll just turn it on."
"Functionally, what we built in the the policy agent was, you know, we built an AI that knows your expense policy in detail, can see all the context around the transaction, and with 99% plus accuracy is able to approve, flag, or deny transactions on the manager's behalf."