Pranav Bhasin

Entrepreneur, Product Guy, Ex-Amazon. Writes about Artificial Intelligence, Leadership and Building Winning Products at

[Pranav Bhasin @pranavbhasin]

https://t.co/B0BvOLTQDg

[Pranav Bhasin @pranavbhasin]

Excited to share that Maven has selected me as a featured expert for their "AI Powered Professional" Series. I feel truly humbled and honored to be part of this group.

I've spent a significant part of the last year thinking about how leaders need to upskill themselves as AI takes over more of the execution — and I've built a robust, battle-tested framework that I'll be covering across a series of courses.

I have two free lightning talks coming up, both live, where we'll work through the framework together and apply it to your own situation.

1️⃣ Make Judgment Your Edge When AI Does the Execution
📅 July 14, 2026
RSVP: https://t.co/aurdDLnllU
My latest post on this topic: https://t.co/E20BGDLSXy

2️⃣ How to Lead a Team of People & AI Agents
📅 July 23, 2026
RSVP: https://t.co/cxKHbLsesj

Sign up now if you haven't already — and please share this post to help spread the word.

#Maven #AI #Leadership

[Pranav Bhasin @pranavbhasin]

People keep saying judgment is the one skill AI can't take from you. But almost no one can tell you what judgment even means. So I tried to figure it out — and the more I looked, the more it broke into smaller questions. Is it making good choices? Knowing what really matters? Knowing when you're out of your depth? Or just being the one who has to answer when things go wrong?

My new article walks through all four. And on July 14th 10 AM PT, I am running a free lightning lesson to work through it live- come try it on your own decisions with me: https://t.co/aurdDLnTbs. Tell me what you think. 👇

[Pranav Bhasin @pranavbhasin]

https://t.co/8COIxj9Hu0

[Pranav Bhasin @pranavbhasin]

https://t.co/8COIxj9Hu0

[Pranav Bhasin @pranavbhasin]

Free session alert: I'm running a free 30-minute Lightning Lesson on Tuesday, July 14th — "Make Judgment Your Edge When AI Does the Execution."

As AI takes over the execution layer, I'll share three shifts that put you a level above the tools:
• How to aim AI at bigger problems — not just faster output
• How to strip any goal down to the few things that actually matter
• How to design AI that self-corrects — loops, not brittle workflows

I'm obsessed with this topic because execution is getting cheaper by the day, and the instinct is to compete on speed: learn the tools faster, ship more. That's the one race the machine wins.

The professionals pulling ahead aren't the fastest users of AI. They're the ones with the judgment to point it at the right problem, cut to what matters, and orchestrate it as a system. As execution gets commoditized, that judgment becomes your scarcest edge — what I call being unautomatable.

If you're a director, VP, or senior IC — or a leader who still ships — I hope to see you there.

RSVP here: https://t.co/aurdDLnTbs

[Pranav Bhasin @pranavbhasin]

The frame of reference you've spent your career building actively prevents you from seeing the full extent of the change. That is exactly where most professionals are sitting with AI today.

To protect your career, you need to make a deeper shift in your identity as a leader. In this video, I cover a 3-step framework on how to make this move here: https://t.co/vMS1qeytUM

#AIcareer #futureofwork #AIandjobs #careerdevelopment

[Pranav Bhasin @pranavbhasin]

Consuming info without a feedback loop is just entertainment. Build the vehicle first, then the knowledge becomes fuel. 100% agree.

[Dickie Bush @dickiebush]

You’re stuck because you don’t have a vehicle to practice. You’re reading business books without a business. You’re reading leadership books without a team. You’re reading copywriting books without writing 10 tweets per day. This "just-in-case" learning is a waste—instead, commit to something and learn “just-in-time.”

[Pranav Bhasin @pranavbhasin]

The pendulum of regulation sentiment always swings based on proximity to power and profit. A fascinating observation on the Silicon Valley psyche.

[Marc Andreessen 🇺🇸 @pmarca]

Overheard in Silicon Valley: "Every single person who was in favor of government control of AI, is now opposed to government control of AI."

[Pranav Bhasin @pranavbhasin]

The 'leverage' point is key. AI doesn't replace the architect; it amplifies the one who knows how to debug the 'leaky' abstractions. Mastery of the fundamentals is more valuable than ever.

[Naval @naval]

Is Traditional Software Engineering Dead?

“Does this mean that traditional software engineering is dead? Absolutely not. Software engineers—even the ones who are not necessarily tuning or training AI models—these are now among the most leveraged people on earth. Sure, the guys who are training and tuning models are even more leveraged because they’re building the tool set that software engineers are using.

But software engineers still have two massive advantages on you. First, they think in code, so they actually know what’s going on underneath. And all abstractions are leaky. So when you have a computer programming for you—when you have Claude Code or equivalent programming for you—it’s going to make mistakes.

It’s going to have bugs. It’s going to have suboptimal architecture. So it’s not going to be quite right. And someone who understands what’s going on underneath will be able to plug the leaks as they occur.

So if you want to build a well-architected application, if you want to be able to even specify a well-architected application, if you want to be able to make it run at high performance, if you want it to do its best, if you want to catch the bugs early, then you’re going to want to have a software engineering background.

The traditional software engineer is going to be able to use these tools much better. And there are still many kinds of problems in software engineering that are out of scope for these AI programs today. The easiest way to think about those is problems that are outside of their data distribution.

For example, if they need to do a binary sort or reverse a linked list, they’ve seen countless examples of that, so they’re extremely good at it. But when you start getting out of their domain—where you have to write very high-performance code, when you’re running on architectures that are novel or brand new, when you’re actually creating new things or solving new problems, then you still need to get in there and hand code it.

At least until either there are so many of those examples that new models can be trained on them, or until these models can sufficiently reason at even higher levels of abstraction and crack it on their own…

And remember: there is no demand for average. The average app—nobody wants it, at least as long as it’s not filling some niche that is filled by a superior app. The app that is better will win essentially a hundred percent of the market. Maybe there’s some small percentage that will bleed off to the second-best app because it does some little niche feature better than the main app, or it’s cheaper, or something of the sort.

But generally speaking, people only want the best of anything. So the bad news is there’s no point in being number two or number three—like in the famous Glengarry Glen Ross scene where Alec Baldwin says, “First place gets a Cadillac Eldorado, second place gets a set of steak knives, and third place you’re fired.”

That’s absolutely true in these winner-take-all markets. That’s the bad news: You have to be the best at something if you want to win.

However, the set of things you can be best at is infinite. You can always find some niche that is perfect for you, and you can be the best at that thing. This goes back to an old tweet of mine where I said, “Become the best in the world at what you do. Keep redefining what you do until this is true.”

And I think that still applies in this age of AI.”

[Pranav Bhasin @pranavbhasin]

@AndrewYNg Exactly. AI-driven prototyping doesn't just lower costs; it changes the nature of product discovery. We're moving from 'planning' to 'proving' in real-time.

[Pranav Bhasin @pranavbhasin]

@ArthurMacwaters Truth-seeking AI is the only way forward. Grok's real-time capabilities are becoming the gold standard for information accuracy in fast-moving events.

[Pranav Bhasin @pranavbhasin]

Perfect breakdown of the Vision -> Strategy -> Roadmap flow by @PawelHuryn. Essential reading for PMs in 2026. 🗺️✨

[Paweł Huryn @PawelHuryn]

Many PMs struggle to explain the difference between Vision, Strategy, and Roadmap.

But those are extremely simple concepts.

So let's tackle them one by one:

-

1. Product Vision (Why)

Product Vision is the long-term mission of your product. It’s aspirational and motivates your team to wake up every morning and go to work.

For example, “Send humans to the Moon” or “Help tour operators focus on doing what they love.”

Effective vision needs to be:

- Inspiring: people who help to implement the vision should feel inspired

- Achievable: it must have a decent chance of working. Don’t dream of traveling to Alpha Centauri until you send humans to Mars

- Documented: do not let the vision stay in your head. You actually need to write it down to make it work

- Communicated: it seems obvious, yet many forget about this. The Vision will only be effective if you communicate it to others

- Emotional as well: vision becomes much more memorable when others can imagine themselves doing something practical and when it speaks to their hearts

-

2. Product Strategy (Where & How)

Despite what many experienced people repeat, Strategy is not a plan, a goal, or a set of actions.

It's a cohesive set of choices that, as you believe, will allow you to win (achieve your Vision) at the playing field of your choice.

For example:

- market and its constraints (e.g., geography)
- value proposition
- relative costs
- tradeoffs
- growth model (e.g., PLG)

Strategy should pass the “can’t / won’t test” so that competitors can't copy it without sacrificing their existing businesses.

A fantastic, short video by Prof. Roger Martin: https://t.co/nEyXM33cVj

For more information about Strategy, see my free article (no email, no paywall): https://t.co/fJvz3tvofx

-

3. Product Roadmap (What)

Product Roadmap is a communication tool that allows you to align everyone in the organization. It creates focus on what’s important right now. It should also explain the reasoning behind it.

In 3 Ways to Create 10X Better Product Roadmaps, I emphasized that it's extremely important to:

- Focus on goals, not features (an extremely common mistake)
- Do not commit too soon
- Shorten the planning horizon

Free article (no email, no paywall): https://t.co/crGQ0WbCT0

-

More information:

- Vision, mission, and purpose are often confused, and everyone interprets them differently. I use a single term. An article by Prof. Roger Martin: https://t.co/MIgMuQGlVu

- Some say that Vision is part of Strategy. It makes sense, as Strategy is an integrated set of choices. And you develop them together. But that’s not the most common opinion. So during interviews, you might want to keep it simple.

Hope that helps.

What are your thoughts?

-

P.S. In my latest newsletter, I debunked 16 other misconceptions in Product Management. Read it here by becoming a subscriber: https://t.co/QeioCUYnAh

[Pranav Bhasin @pranavbhasin]

Spot on analysis from @Beth_Kindig on $NVDA. Timing the cycle and watching the suppliers is where the real alpha is. 📈🔥

[Beth Kindig @Beth_Kindig]

Nvidia and its suppliers are the best trade of the year – the question that remains is timing.

There is $100 billion pointed at the NVL 72 systems, if these were shipping in volume (and on time), the supply chain would be on fire right now – is what I mean by timing. $NVDA $SMH

You can’t put $100 billion into production for one SKU and ship in volume without a splash in the large supply chain that builds these AI systems. There was no splash (yet).

I also pointed out yesterday before the bell that investors do better when proxies participate – whether that’s suppliers providing a clear, green light or an ETF like SMH, which is 14% off ATHs.

The Street was exuberant going into the print, aggressively raising price targets -- yet, Nvidia's stock has been range bound for 9 months stuck between $110 and $150. The much-needed catalyst is likely arriving in H2. 👇

[Pranav Bhasin @pranavbhasin]

A critical perspective on the endurance vs. strength trade-off for long-term health from @hubermanlab. Longevity is about balance. 🏃‍♂️💪

[Andrew D. Huberman, Ph.D. @hubermanlab]

A consistent observation is that most pure endurance athletes are withered & frail from 65-70 onwards. The pure strength folks do a bit better (unless too big; plenty seem to die early). The 70yo+ athletes that sprint & do gymnastic-type training are clearly winning the (physical) longevity game. Cognitive longevity = a separate matter.

[Pranav Bhasin @pranavbhasin]

Agentic AI is the next frontier of productivity. This new course from @AndrewYNg is a must-watch for anyone building in this space. 🤖💡

[Andrew Ng @AndrewYNg]

Announcing my new course: Agentic AI!

Building AI agents is one of the most in-demand skills in the job market. This course, available now at https://t.co/zGHUh1loPO, teaches you how.

You'll learn to implement four key agentic design patterns:
- Reflection, in which an agent examines its own output and figures out how to improve it
- Tool use, in which an LLM-driven application decides which functions to call to carry out web search, access calendars, send email, write code, etc.
- Planning, where you'll use an LLM to decide how to break down a task into sub-tasks for execution, and
- Multi-agent collaboration, in which you build multiple specialized agents — much like how a company might hire multiple employees — to perform a complex task

You'll also learn to take a complex application and systematically decompose it into a sequence of tasks to implement using these design patterns.

But here's what I think is the most important part of this course: Having worked with many teams on AI agents, I've found that the single biggest predictor of whether someone executes well is their ability to drive a disciplined process for evals and error analysis. In this course, you'll learn how to do this, so you can efficiently home in on which components to improve in a complex agentic workflow. Instead of guessing what to work on, you'll let evals data guide you. This will put you significantly ahead of the game compared to the vast majority of teams building agents.

Together, we'll build a deep research agent that searches, synthesizes, and reports, using all of these agentic design patterns and best practices.

This self-paced course is taught in a vendor neutral way, using raw Python - without hiding details in a framework. You'll see how each step works, and learn the core concepts that you can then implement using any popular agentic AI framework, or using no framework. The only prerequisite is familiarity with Python, though knowing a bit about LLMs helps.

Come join me, and let's build some agentic AI systems!

Sign up to get started: https://t.co/FX35dloqw4

[Pranav Bhasin @pranavbhasin]

@InvestInAssets The most underrated lesson here is #14 on compounding. Most people focus on picking the right stocks — but the bigger unlock is simply staying invested long enough for time to do the heavy lifting. Consistency beats cleverness in the long run.

[Pranav Bhasin @pranavbhasin]

The part that doesn't get talked about enough: AI agents don't just save time — they force you to document and systematize everything you do. You can't delegate to an agent what you haven't clearly defined yourself. Building this stack is actually a masterclass in operational clarity.

[Pranav Bhasin @pranavbhasin]

@thedankoe Self-improvement is just the first domino. Once you realize you can reprogram your habits, your mindset, your body — the logical next step is: why not reprogram your income too? Every serious entrepreneur I know started as someone who just wanted to be better.

[Pranav Bhasin @pranavbhasin]

@naval Vibe coding is the new PM, and vibe PMing is the new strategy. When anyone can build, the bottleneck shifts entirely to taste, judgment, and knowing what to build at all. That's still deeply human — and still deeply hard to scale.