Podbit · The a16z Show
Simply giving employees ChatGPT changes nothing. Kavak's first move was to rebuild its APIs, systems, and feedback loops so agents could actually do the work. The second bet: agents could be superhuman — outperforming the best human hire on every metric that mattered.
Where this was said
At 5:42 · chapter starts 4:00
Angela asks Alejandro to ground the listener in what actually happens when a customer arrives. The answer is striking: a dedicated agent is spawned specifically for that customer, running in its own virtual machine. It remembers years of interaction history — a webpage visit, a call two years ago — formulates a long-term strategy, and relentlessly works to maximise lifetime value across all of Kavak's products over time. Alejandro then explains the three foundational decisions that made this possible. First, Kavak had to resist the instinct to simply hand ChatGPT to employees and instead rebuild its entire API and system layer so agents could actually act. Second, it made the bold bet that agents could become superhuman — not just helpful, but genuinely better than the best human ever hired. Third, it reoriented the company's success metrics from transactional (cars sold, parts ordered) to relational, assigning agents to all 10 million customers with a mandate to maximise lifetime value. [1] — Alejandro Maza Ayala "Most companies build task-specific agents. Kavak builds one agent per customer, with its own virtual machine, long-term memory, and a singl…" 04:00
Most companies build task-specific agents. Kavak builds one agent per customer, with its own virtual machine, long-term memory, and a single goal: maximize that customer's lifetime value over years. The agent wakes up, works, sets an alarm, and comes back — it never forgets.
Kavak has 10 million customers in its database, each with AI agents assigned to maximize their lifetime value.
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.
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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