Hurratul Maleka Taj

2024 has been an incredible year filled with so many blessings. One such blessing is captured here in this video, shared from the memory book from with all the Stanford professors I had the privilege to meet and learn from, together in one clip. In this video they share with us, all about what truly makes them unstoppable in their life.

This video clip serves as a poignant reminder for me, of just how fortunate I am to have crossed paths with such extraordinary teachers and mentors - truly among the finest in the world. It fills me with immense gratitude towards the universe for aligning my journey with theirs and giving me countless reasons to be thankful.

💡 Prof. Peter DeMarzo : His humility is nothing short of heartwarming. Despite his immense achievements, he carries himself with a simplicity that is both inspiring and humbling.

💡 Prof. Haim Mendelson : His unwavering passion for teaching is unparalleled.

💡 Prof. Matt Abrahams : Time is the most precious commodity, and his generosity is unparalleled. He always says "Yes"—always ready to help, always there for his students. His support is unconditional, and his dedication is unmatched.

💡 Prof. Jack Fuchs : His passion for mentoring entrepreneurs and his insights into building companies with foundational principles that eliminate friction are nothing short of transformative. Spoiler Alert : I’m thrilled to share that he will feature in my next podcast episode.

💡 Prof. Baba Shiv : The way he genuinely cares for his students is remarkable.

💡 Prof. Brian Lowery : After taking his course, I am truly in awe. His profound understanding of self-awareness and its significance to every dimension of our lives has helped me personally a lot.

💡Prof. Sarah Soule : Her vibrant energy is infectious.

Stanford University Stanford GSB

Warren Buffett once gave Jim Keyes a piece of advice that changed how he faced one of the hardest periods of his career.

At the time, the financial markets were collapsing. Blockbuster was carrying roughly a billion dollars in debt. Refinancing was extraordinarily difficult, bankruptcy rumors were everywhere, and James was fighting to keep the company alive.

Then, at a Microsoft CEO event, he ran into Warren Buffett.

After hearing what James was facing, Buffett asked him one question:

“Would you rather be on the bench watching somebody else do this, or be in the game?”

That was it.

No elaborate framework.
No ten-point turnaround plan.
Just the reminder he needed to keep going.

In my conversation with James, what stayed with me was this: Leadership can become most dangerous when the external crisis becomes an internal one. When uncertainty turns into self-doubt. When the voice saying “you may not be good enough” becomes louder than the problem itself. In those moments, the right mentor does not necessarily solve the problem for you. They interrupt the story you have started telling yourself about your ability to solve it.

Sometimes you do not need someone to tell you the road will be easy.

You need someone you trust to remind you: Stay in the game. Dust yourself off. Take another swing.

A powerful lesson from James Keyes on resilience, mentorship, and what leadership looks like when the outcome is anything but certain.

🎥 From my conversation with James Keyes on The U Lab Podcast.

WarrenBuffett

According to Andreessen Horowitz, AI startups have two ways to sell into the enterprise. Lighthouse and Landgrab. It's a sharp map. Today we're going a layer deeper.

The premise underneath it is the one worth sitting with. The buyer who signs is pricing their own risk, not evaluating your product in the abstract.

From that, two questions. How exposed is the buyer. And does social proof travel.
That gives you four boxes.

Top right, Lighthouse. Exposure is high, so the buyer needs proof. You win a few marquee logos so a nervous buyer feels safe. Harvey and Hebbia won law and finance this way.

Bottom left, Landgrab. Exposure is low, so the buyer just needs the math. You skip the logos and sign as many customers as fast as you can. Decagon and Stuut went wide before the incumbent could react.

The other two are edge cases. PLG, where the product spreads itself. And hard markets, where the buyer needs proof but the logos never reach them.

Two real strategies. Proof, or math. Now let's go underneath it.

Look at Lighthouse and Landgrab again. One makes the risk feel safe with social proof. The other makes it look worth it with ROI.
Neither one takes the risk off the buyer.

So they're not two strategies. They're two versions of one move. Both leave the risk on the buyer's desk.

Which means there's a third move the map never names. Take the risk off the buyer entirely.

I am calling it the Underwrite. This is the third strategy I've built on top of a16z's framework. The vendor guarantees the outcome and holds the risk itself. You stop selling software. You start selling a priced guarantee with your own balance sheet behind it.

And it's already here. Sierra charges per resolved conversation, and nothing when the agent fails. Intercom Fin, 99 cents a resolution.

Why now. Because what AI changed isn't pricing. It's attribution. You could never underwrite a result you couldn't isolate. When an agent owns the whole workflow, the outcome becomes provably yours.

But it only works in one corner. The downside has to be bounded, a loss you can name in advance, and the outcome has to be attributable.

The most popular online payment processor on the planet just got a takeover bid at a discount to what payments businesses normally fetch.

439 million users. Roughly 44 percent of the global online payment market. And revenue still growing.
And the market cheered.

Here is the consensus. Stripe, alongside Advent International, has offered around $53 billion, according to Reuters. $60.50 a share, a 28 percent premium. PayPal closed up more than 17 percent. The press read it as a win, and the opening of a bidding war.

Now the basics, because they carry the whole story.
Every deal has two numbers, and they measure two different things.

The premium tells you how far above today's price the buyer is paying. It says nothing about whether today's price was already low.

The multiple ignores the stock price entirely. It asks a different question. What are you paying for the cash the business actually earns.

This is why a bid can look generous and cheap at the same time. A large premium on a stock that already fell hard is still a low price for the business underneath it. The premium is measured against the fallen share price. The multiple is measured against the earnings. Two different baselines, so both readings hold at once.

28 percent over Tuesday looks like a gift to PayPal's shareholders. But 7 times EBITDA, against 8 to 12 times for payments peers, says the buyer is getting the cash flow at a discount. Both are true. The premium reflects a de-rated stock. The multiple reflects what the business earns. That is the whole point.

So why does a cash machine trade below its peers?

Because public markets price the narrative, not the installed base. PayPal's story soured. A post-pandemic reset, share lost to Stripe and Apple Pay, a tech stack patched together from years of acquisitions. Once the story breaks, the multiple compresses below what the cash flow is worth.

Here is the model to remember. When a durable business loses its story, its public price falls faster than its cash generation does. That opens a gap between what the market pays and what the business earns. And that gap is an invitation.

OpenAI wants to go public at $1 trillion, and this month it signaled it would rather wait until 2027 than list for a dollar less. So the question worth sitting with is simple: what would have to be true for that number to hold?

Start with the scorecard. PitchBook rates the leading AI labs across five dimensions: revenue quality, capital efficiency, governance, moat durability, and how much of their own compute they control. OpenAI comes in at 4.53 out of 10. Anthropic at 8.2. On today's fundamentals, Anthropic grades higher.

Now price that quality. Investors are paying about $188 billion for every point of OpenAI's quality, against $118 billion for Anthropic's. That's a 60% premium, for the company scoring lower. Which raises the real question: what are those investors seeing that the scorecard doesn't?

The clearest place to watch that tension is the multiple. On its own valuation, the market grants OpenAI roughly 34 times revenue. At that multiple, OpenAI needs only about $29 billion in annual revenue to justify a trillion, well within reach. But apply Anthropic's multiple, around 20.5 times, and the same trillion suddenly demands $49 billion in revenue. Nearly double.

So the whole trillion-dollar case comes down to one thing: whether public investors keep extending OpenAI the premium it enjoys in private markets. Around $340 billion of its $852 billion valuation rests on that richer multiple alone. Price it the way the market prices Anthropic, and that's the piece OpenAI has to earn.

And waiting isn't free. On its own projections, OpenAI runs roughly $115 billion in cumulative losses before the business turns self-sustaining around 2030.

But here's the other side, and it's a real one. That premium may not be irrational at all. 900 million people use OpenAI's products every week. The brand is the category. Investors aren't pricing the business it is, they're pricing the business it becomes. If OpenAI grows revenue into that 34-times multiple, a trillion doesn't look aggressive. It looks early.

So don't watch the valuation, watch whether the fundamentals earn it.