This Founder is Making 1B+ Excel Workers 20x Faster | Meridian, John Ling
In a Nutshell
John Ling, co-founder and CEO of Meridian, is building AI tools for spreadsheets to make Excel users 20x faster, viewing Excel as the world's most distributed programming language. After excelling at Scale AI by deeply studying LLMs and data quality, he identified a gap in finance: no one has invested 1,000 hours using AI to automate manual tasks like LBO models, which bankers build by hand. Meridian, backed by a $15M Series A from a16z, fosters a culture of bold experimentation, rapid outreach, and hands-on AI intuition-building to transform knowledge work.
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Enjoyed learning new things; more work meant more opportunities to learn. Faced 50 problems, each teaching something different. Approach: try it, and if you fail, that's okay. No one on the planet has spent a thousand hours trying to build financial models with AI. Experiment: construct AI to build an LBO model that would otherwise be done manually for work. Bankers do it by hand; if you don't know how, you just don't do it.
Think there's a big decomposition where models can handle different parts of the workflow very well, but it requires investigation.
My name is John, co-founder and CEO of Meridian. Building AI for spreadsheets. View Microsoft Excel as the most distributed programming language in the world. Goal: help people who spend a lot of time in spreadsheet software move 20 times faster.
Prior: 1.5 years at Scale AI. Before that, started a couple companies. Raised slightly more than $15 million; Series A led by Andreessen Horowitz (a16z general partnership). Early stage, aiming to grow.
Enjoyed learning; more work = more learning opportunities. 50 problems, each teaching something different. Scale AI allowed learning different sides of the business; job wasn't confined to X, could expand to Y, Z, etc.
Being willing to dig into research is valuable, especially in AI. Easy to get lost in execution; valuable to step back and ask why. Learn by reading research papers. Example: quality of data—what makes data high quality vs. low? What do researchers care about? What makes a data point valuable? Went through much data across domains.
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