For decades, the traditional tech team looked something like this: Engineers (3-5 of them) + PM + Designer = good stuff built. Sometimes, we’d add more “rings” (Sales! Data! Marketing! Customer Success! User Research!) to fight bigger villains while trying to abide by the best practice that 2 pizzas should be able to feed this group. (View Highlight)
In the era of AI, all this breaks. It’s time for a new group of heroes to take the stage. (View Highlight)
Traditional product development has been best practice-ized until it resembles an Ikea instruction manual:
The Idea (conceived in enthusiastic brainstorming chats deemed “groundbreaking” and “innovative”)
→ Mocks (polished until it’s a sparkling good storyboard for stakeholder alignment)
→ PRD (a beautifully formatted 12-page novella admired by many but read closely by precisely zero)
→ Code (with deadlines extended as frequently as the MCU)
→ Launch (with ample hopes and dreams and craft beer and pizza)
→ Surprise! (most of the time, the surprise is why users reacted with a collective shrug rather than thunderous applause) (View Highlight)
This worked when software moved in cycles of months. But now that we have AI at our disposal (who is today an insanely productive, cheap yet junior talent that must be aggressively micromanaged), that tidy linear path is going the way of the dinosaurs. (View Highlight)
Ask yourself:
• Why brainstorm for hours when AI can generate 50 ideas (and roast the bad ones) in minutes?
• Why show static mocks when AI can build a version of the damn thing for your stakeholders to play with?
• How much effort should you put into your PRD when AI changes the rules of what’s possible every other Tuesday?
• Why spend a bunch of time debating engineering flows when you can’t deterministically predict what the technology will do?
• How much more would you achieve if you could learn the Surprise! of your launch in a fraction of the time? (View Highlight)
Forget IKEA manuals. You don’t want to be stuck in a situation where you follow every step, screw by screw, only to realize halfway through that what you actually need is a ladder. (View Highlight)
The Idea Garden
Don’t settle prematurely. Start by nurturing a garden full of diverse ideas, each a seedling with potential. Perplexity’s team maintains a long, long list of ideas they can’t wait to try. (View Highlight)
Prototype-and-prune
The bulk of future product development will be spent researching whether an idea has legs. Say goodbye to cycles of mock and doc feedback, and say hello to code-first prototypes with constant iteration (on prompts, models, etc) to answer three key questions. If the answer to any of the below is no, the idea is pruned.
Capability:can this idea be done with today’s technology?
Accuracy:Will the results consistently meet the user’s expectations?
Speed:Can it perform smoothly and quickly enough for real-world use? (View Highlight)
→ Polish-and-productionize
Once an idea demonstrates clear promise, it can now get refined for real customer launch, whether to a small beta group or beyond. This is the time for optimizing, aligning with brand vibes, and coordinating release logistics and communication. (View Highlight)
→ Launch
Still with plenty of hopes and dreams and craft beer and pizza (View Highlight)
→ Further pruning
Selectively prune ideas that don’t thrive in real-world testing, swiftly retiring unsuccessful experiments to free up space for fresh seeds. (View Highlight)
Imagine you’re assembling a team for an escape room challenge. Would you rather have six managers debating strategies or two sharp thinkers who rapidly test every combination to unlock the door? (View Highlight)
Welcome to the era of AI-native companies, where individual contributors (ICs) reign supreme. (View Highlight)
Tiny teams working in parallel — teams with a handful of people (think 1-3) can simultaneously explore a wider range of ideas to see if they work. (View Highlight)
Blurred functional roles: why do you need a stool with three (or more) legs when a classically-trained engineer can use AI tools to iterate on mocks, a classically-trained designer can use AI tools to iterate on marketing copy, or a classically trained product manager can use AI tools to get insights from their data? (View Highlight)
Reduction of “management” roles: the cost of communication overhead shrinks drastically when you go from 7-10 people to 3. The PM-as-coordinator, or manager-as-coordinator role is no longer needed until (maybe) the polish-and-productionize step. (View Highlight)
Good taste: taste is the ability to distinguish excellence from mediocrity. The hand can never consistently produce better than the eye can discern. (View Highlight)
High agency: AI favors those who bias towards action and see themselves as problem solvers with the power to get shit done. Those who prefer structure, consensus, and waiting to be told what to do will find themselves struggling. (View Highlight)
How to Survive (and Thrive)
For leaders:
• Hire curious generalists, not specialists.
• Track speed of feature development: ensure that it’s going up.
• Reward doers over sayers: look for the proof in the problem solved, not in the discussion had (View Highlight)
For ICs:
• Identify as a problem-solver: not an engineer / designer / pm.
• Make AI your constant collaborator: experiment, experiment, experiment with doing things you would have previously relied on a teammate for. Experiment with doing whatever you know how to do well even faster.
• Just do it: try stuff out, don’t wait around for permission.
For Everyone:
• Embrace the chaos. The future isn’t built on plans—it’s grown in gardens. (View Highlight)
