The 70/30 Rule: Why AI Owns the Math-and People Still Win Deals
"AI taking entry-level jobs is a 'when,' not an 'if.' But in venture capital, 70% of the decision is reading the founder and team-and that's something AI can't do."
That split-70% people, 30% product-reframes finance. For years, spreadsheets defined credibility. Now, algorithms do most of the heavy lifting, and faster than any junior ever could. The edge has shifted from modeling skill to human judgment.
AI is taking the crunching jobs
AI can scan comps in seconds, build decks, size markets, and flag outliers before you finish coffee. McKinsey estimates that roughly half of finance activities are automatable with current tools.
What used to be a rite of passage-late nights in Excel-looks more like a tax on time. If the entry path was number crunching, that door is narrowing. The question isn't if AI changes the job; it's where you create value when the model work is essentially free.
Source: McKinsey on automation in finance
The venture capital exception
At the earliest stages, the math helps-but people decide outcomes. Market size, revenue projections, technical due diligence: useful, but incomplete. Investors routinely say the same thing in different words: you're backing founders, not spreadsheets.
Call it the 70/30 rule. Seventy percent is the team-resilience, persuasion, hiring judgment, the grit to pivot after three failed prototypes. Thirty percent is the product as it exists today. AI can summarize a track record. It can't tell you if this founder can read a room or keep moving after a brutal week.
As one early-stage investor put it, "AI can show me a founder's history in five seconds, but it can't tell me if they can take a punch. That's still my job."
Why Gen Z gets it
Students already assume AI is in the loop on screening and analysis. They're not panicking; they're recalibrating. The goal isn't to be the fastest modeler-it's to be the clearest thinker in the room.
They trust AI with the grunt work and invest their effort where machines stall: relationships, interviewing, reading behavior, persuading skeptical stakeholders. Growing up digital helps. They know where AI is strong, and where it runs out of road.
A practical playbook for finance pros
- Automate the first pass: Let AI handle comps, sensitivity tables, market maps, and deck drafts. Save your energy for the judgment calls.
- Codify the 70/30 rule: In IC memos, explicitly weight team at 70% and product at 30% for early-stage bets. Force clarity on why the people merit the risk.
- Design a founder scorecard: Rate persuasion, hiring judgment, rate of learning, resilience, and focus. Use structured interviews and back-channel references to reduce bias.
- Train your ear: Watch pitches live. Note micro-signals: crisp answers under pressure, ownership of misses, and how fast they translate feedback into next steps.
- Close the loop: Keep a decision journal. Six to twelve months later, compare your read of the founder to actual execution. Calibrate your instincts with data.
- Upgrade your stack: Standardize prompts and workflows so junior time shifts from building to thinking. If helpful, explore curated AI tools for finance that streamline modeling and ops.
- Protect the human time: Spend more hours on founder interviews, customer calls, and reference checks than on polishing decks. That's where you find edge.
Lessons for the future of work
As AI scales technical analysis, "soft skills" stop being soft. They're scarce and defensible. Even Harvard Business Review points out that organizations get more from AI when humans excel at communication, change leadership, and cross-functional judgment.
HBR: Building the AI-Powered Organization
Back to the 70/30 rule
AI owns the math. You own the gut-the judgment, empathy, and intuition that turn data into decisions. The best investors will still pause, look across the table, and read what no model can quantify: the human pulse of possibility.
If you're in finance, that's the job now. Let the machines run the numbers. Win on what stays human.
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