Feb 7, 2022 · 35m · mad
Fireside Chat: Emil Eifrem (Co-Founder & CEO, Neo4j) with Matt Turck (Partner, FirstMark)
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions
In this Data Driven NYC fireside chat, Matt Turck interviews Neo4j Co-Founder and CEO Emil Eifrem on the rise of graph databases, key enterprise use cases, product-led growth strategy, and the historic ISO standardization of GQL.
How this conversation actually went
Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Matt holds 18.8% of the talking time here. How this is scored →
Matt as informed peer 4.9 Guest teaching 4.9 Guest disagreement 1.4 Matt pushing back 1.3
speaking balance: gold is Matt, purple is the guest (3 minute bins)
Sharpest disagreement ▶ 29:00 Gently rejecting host premise on enterprise transition
Emil directly challenges Matt's framing about transitioning from bottoms-up to top-down sales, emphasizing that Neo4j never sells top-down to CIOs.
Hardest push from Matt ▶ 7:53 Host pushes guest to explain fraud use caseMatt interrupts Emil's general list of use cases to specifically challenge him to justify why fraud detection constitutes a graph problem.
Biggest teaching moment ▶ 27:35 Guest explains unique database status among data scientistsEmil educates the audience on how out of 350+ databases, Neo4j is the sole database where data scientists input data to leverage relationships as ML features.
Matt holds his own ▶ 3:04 Host displays deep market data knowledgeMatt cites specific industry benchmarks from DB-Engines and Gartner research reports, demonstrating clear technical familiarity with the database ecosystem.
the scores for every segment, with the reasoning behind each
| Chapter | Topic | Matt as informed peer | Guest teaching | Guest disagreement | Matt pushing back | Why |
|---|---|---|---|---|---|---|
| Neo4j's Record Funding Round and $100M ARR Milestone | 3 | 4 | 1 | 1 | Matt opens by asking about company size and metrics. Emil provides context on Neo4j's historic $200M+ funding round, $2B+ valuation, and $100M ARR milestone compared to peers like MongoDB and Couchbase. | |
| The Rise of Connected Data and Native Graph Architecture | 6 | 5 | 1 | 1 | Matt shows strong domain knowledge by citing specific DB-Engines ranking charts and Gartner quotes on graph databases being the foundation of modern analytics. Emil explains native graph architecture advantages over legacy relational databases. | |
| Real-World Applications: Fraud, Supply Chain, and Enterprise Use Cases | 6 | 5 | 1 | 2 | Matt accurately recaps the core definition of graph databases as elevating relationships to first-class citizens and probes on why fraud is a graph problem. Emil educates on multi-dimensional fraud ring detection and supply chain graph shifts. | |
| Selecting Database Tools and the Standardization of GQL | 5 | 5 | 2 | 1 | Matt asks informed technical questions contrasting key-value, document, relational, and graph DBs, as well as Cypher versus SQL learning curves. Emil details the historical significance of GQL becoming an official sibling standard to SQL. | |
| Market Competition and Neo4j's Enterprise Footprint | 4 | 4 | 1 | 1 | Matt relays audience questions regarding market differentiation and customer vertical growth. Emil explains native graph moat vs layered graph entrants and cites impressive metrics like powering 99% of airfare calculations. | |
| Category Creation and the Expansion into Data Science | 5 | 6 | 2 | 2 | Matt humorously acknowledges his well-known annual Data & AI landscape diagram when Emil lightheartedly references it. Emil educates on how data scientists use Neo4j uniquely to feed relationship signals directly into machine learning pipelines. | |
| Bottom-Up Go-To-Market Strategy and Cloud Expansion | 5 | 5 | 2 | 1 | Matt asks an insightful question about managing the transition from bottom-up developer adoption to top-down enterprise sales. Emil reframes the premise by explaining that over 85% of ARR still originates bottom-up with practitioners rather than top-down pitches. |
Statements from this episode (15)
Eifrem: Neo4j's 2021 funding round valued the company over $2B
“We raised this round, you know, last summer, and it's the first time that we went out with some numbers, like the valuation for the first time, which was north of north of two billion. And there's actually the largest round in database history, right?”
Eifrem: Neo4j crossed $100M ARR in 2021
“We crossed a hundred million ARR, you know, in, you know, last year.”
Eifrem: Only 5 modern NoSQL database companies have crossed $100M ARR
“There's five database companies that have crossed a hundred million, right, of the, kind of the, let's call it the NoSQL crowd, or like modern operational database companies. It's, You know, Mongo, and then it's us and Redis. We're on that kind of Mongo path. …”
Eifrem: Neo4j is frequently 1,000 times faster for connected data queries
“We've optimized every layer in the stack of the database architecture completely around connected data. We're not built on top of a different database or anything. It's a native architecture. And that means that if you want to query along how things are connec…”
Eifrem: Detecting normal-looking fraud rings requires graph databases
“What it won't capture is that what if you have a number of transactions that are all within this band of what's normal, but they're connected in fraudulent ways, like a fraud way. Like the only way you can find that is if you can operate and connect the data, …”
Eifrem: Physical goods supply chains are frequently 20 to 30 hops deep
“Any company that is producing physical goods is tapping into this global supply chain spanning continent to continent, right? That is frequently 20, 30 hops deep.”
Eifrem: Cypher is the most popular graph database query language
“Cypher is the most popular graph database query language.”
Eifrem: GQL standard query language is 98% identical to Cypher
“For the first time ever, In the history of databases, the SQL committee looked at Cypher, looked at graph databases, and then said, you know what? This category is here to last. This is an actual sibling to SQL, and they created the GQL query language, which i…”
Eifrem: Graph databases could become a $20B to $40B market
“Databases is the biggest market in all enterprise software. It'll soon be a hundred billion dollar market. I think graph databases can be a significant chunk of that, 20, 30, forty billion dollar, right?”
Eifrem: Over 75% of Fortune 100 companies use Neo4j
“Over 75% of the Fortune 100 are using NeoFj today, so all 20 of the biggest banks in North America, all 20 of them are using NeoFj, seven of the 10 The biggest retailers in the world are using NeoFj for the five biggest, biggest telcos.”
Eifrem: Neo4j powers 99% of all flight ticket route calculations
“99% of all flight ticket calculations, so which route should I go from point A to point B when I fly from Paris to New York? Is that a direct flight or connecting Heathrow? Like, how do I get there, right? Is done with Neo for J. 99% of our airfare.”
Eifrem: Successful category creation requires attracting major tech incumbents as competitors
“What does success look like? 10 years down the line, what does success look like? Well, success looks like we have a bunch of big companies that are competing against us. That's what success looks like, right? ... That, that you have a thriving category, becau…”
Eifrem: Data scientists now match developers as Neo4j's primary user persona
“Today, and this happened just in the last 12 to 18 to maybe 20, at most 24 months, data scientists are an equally like as big of a persona for us as the developer. So if you look at kind of our top line metrics around kind of awareness or Visits to neo-for-day…”