Podbit · The a16z Show
Kavak's AI sales agents now convert customers at 2.1x the rate of its human team, and also tripled NPS. The agents handle everything from car recommendations to financing to trade-in quotes — a 15-skill job that no single human could match.
Where this was said
At 11:00 · chapter starts 8:10
Gabriel asks how Kavak evaluates whether agents are working at the scale of 96–98% of all interactions. Alejandro's answer reframes the question entirely: evals are not a safety tax, they are the accelerator. His analogy is elegant — you'll only floor the gas if you have good brakes. Most companies go slow on AI deployment because they lack rigorous evaluation, not because the models are weak. Kavak inverts this by spending roughly equal engineering time, tokens, and money on building evals as on building agents themselves [1] — Alejandro Maza Ayala "Equal eng effort on evals vs agents: Kavak spends roughly equal engineering time, tokens, and money on building evals as it does on buildin…" 09:05 . The measurement focus is ruthlessly commercial: did the customer convert? Did they come back? Not vanity metrics like call duration or number of interactions. This discipline, Alejandro argues, is what separates genuine agentic deployment from theatre.
96% of all customer interactions and 95% of all transactions at Kavak are now handled by AI agents. Between 100,000 and 200,000 agent instances wake up every day, each with its own virtual machine, working anywhere from 3 minutes to 3 days before setting an alarm for their next task.
Kavak's AI agents handle 96% of all customer interactions with no human involvement.
95% of all Kavak transactions are completed end-to-end by AI agents, not humans.
Between 100,000 and 200,000 unique agent instances are spun up at Kavak every single day, each with its own virtual machine.
Most companies treat evals as an afterthought. Kavak treats them as the foundation. Spending equal engineering effort on evals and agents is what allows Kavak to deploy at massive scale without blowing up — because better brakes mean you can press the gas harder.
Kavak spends roughly equal engineering time, tokens, and money on building evals as it does on building agents themselves.
Audience-building isn't a shortcut — it's a 3-year content grind before the product even exists. The speaker reveals that his monetisation success was entirely downstream of years spent tweeting daily and creating content, not talent or luck.
Building a monetisable audience on Twitter costs just 5 minutes a day — but it has to happen every day for years. The time barrier is low; the consistency barrier is where most people fail.
Sam built Algrow, a SaaS for finding viral content formats, with zero coding experience using ChatGPT and Cursor. Six months later: 10,000 users, $14K/month in revenue.
Sam's first MVP threw an application error on its very first user — and he shipped it anyway. The core idea worked, and that was enough to validate the product and keep users coming back.
Sam joined Discord voice chats, muted himself, and silently screen-shared his product. Users in the chat started tagging him asking what the tool was. No pitch needed — curiosity did the selling.
Most founders post links in Discord and immediately get banned for self-promotion. Sam's approach was the opposite: build rapport, help people with the tool, let word of mouth do the work.
Find where your ICP lives. Listen before building. Validate with DMs and Loom recordings. Build in public with users inside your own Discord server. Turn early adopters into advocates with free access.
Instead of fearing the self-promo ban in large Discord servers, create your own private server for your product. You funnel in ideal customers and build a relationship that email can't replicate.
Algrow helps creators find and replicate viral video formats, starting at $25/month. It analyzes subscriber counts, average views, and trending formats — and can even generate the videos with AI.
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