Hurratul Maleka Taj

Now LIVE: The U Lab Podcast Episode with Jing Kuang, Founding Partner of Y+ Ventures and Co-Founder of Cresca.

If AI is making knowledge abundant, what becomes valuable next?

At the center of our conversation are questions such as:
- What AI cannot commoditize?
- How is AI reshaping the economics of venture capital?
- How is the RootedIn VC Fellowship redesigning the learning journey into venture capital?
- What is the market still underestimating about consumer AI?
- What does it really mean to build a venture firm that is human-centered by nature and AI-native by design?
- What founder signals, traits, or frameworks give investors conviction before the metrics exist?
- Why are venture capital and entrepreneurship not binary, but two sides of the same value-creation process?

Full episode links in the comments.

If AI is making knowledge abundant, what becomes valuable next?

Coming Up: The U Lab Podcast Episode with Jing Kuang.

FAANG → MANGOS

For nearly two decades, one acronym defined the technology industry: FAANG.

Today, a new acronym is going viral on X: MANGOS.

It stands for Meta, Anthropic, Nvidia, Google, OpenAI, and SpaceX, a group that could soon dominate the next era of technology as several companies prepare to go public.

But here's what caught my attention.

FAANG was built around the internet. MANGOS is being built around intelligence.

FAANG was defined by search, social media, e-commerce, smartphones, and streaming.

MANGOS is being defined by AI models, compute infrastructure, autonomous systems, and space infrastructure.

This isn't just a new acronym.

It may represent a shift in the technology economy - from companies that connected people to companies that are building intelligence itself.

The question is no longer, Who owns the biggest platform?

The question is, Who will own the infrastructure of the AI era?
I'm Hurratul, and this is The U Lab Daily Brief on venture capital, technology, and the future of innovation.

Most people assume AI is making it harder for new college graduates to find jobs.

A recent analysis highlighted by points to a more surprising possibility.

What if remote work is a bigger factor?

Researchers found that unemployment among younger college graduates has deteriorated more than for older workers, with the gap appearing particularly pronounced in occupations that can be performed remotely.

Even more interesting, one study found that once remote work is taken into account, much of the observed relationship between AI exposure and declining early-career hiring largely disappears.

By 2025, occupations with higher work-from-home exposure showed nearly a 2 percentage point decline in the share of new hires, while the AI-controlled estimate remained close to zero.

This does not mean AI has no impact.

But it suggests we may be asking the wrong question.

Perhaps the challenge is not simply whether AI is replacing entry-level workers.

Perhaps the challenge is whether remote work is weakening the apprenticeship systems that historically helped develop them.

If firms increasingly hire experienced talent who can contribute immediately, who will train the next generation of managers, operators, and founders?

I'm Hurratul and this is The U Lab Daily Brief on venture capital, technology, and the future of innovation.

What makes Javiera Unstoppable?

The Trust Layer of AI

What happens when intelligence becomes so powerful that access to it starts coming with conditions?

This week, Anthropic released Claude Fable 5, the first public version of its Mythos model. But alongside the launch came something unusual: strict safety limits, blocked responses in high-risk areas, and a mandatory 30-day data retention policy, even for some enterprise customers that previously had zero-retention agreements.

Now, most people will focus on the model.

But I think the bigger story is what this says about where AI is heading.

For years, the race was about building smarter systems.

What's actually happening now is that leading AI labs are building governance systems around those models. Monitoring. Guardrails. Approval layers. Safety infrastructure.

Why's that?

Because once a technology becomes powerful enough, capability alone is no longer the bottleneck. Trust becomes the bottleneck.

And that raises a bigger question.

If the most advanced models require guardrails, monitoring, retention policies, and approval processes, who ultimately controls access to intelligence?

Think about that for a second.

We spend a lot of time talking about who will build the smartest AI.

But maybe that's becoming the wrong question.

Maybe the more important question is: who builds the trust systems that make that intelligence safe enough to use?

Because if intelligence keeps getting cheaper and more abundant, trust may become the scarce resource.

And that could end up being where the real power sits.

I'm Hurratul and this is The U Lab Daily Brief on venture capital, technology and the future of innovation.

Some of the most important ideas don't emerge from certainty.

They emerge from curiosity, thoughtful questions, and the willingness to examine how we think, communicate, and connect with others.

This lecture by Professor Matt Abrahams at during the Me2We event was a reminder that great communication is not about having all the answers. It is about learning the frameworks for better conversations, clearer reasoning, and ultimately better decisions.

In a world overflowing with information, the ability to communicate with clarity may be one of the most valuable skills we can develop.

I feel fortunate to have met him and continue to learn so much from him.

The Trust Layer of AI

What happens when intelligence becomes so powerful that access to it starts coming with conditions?

This week, Anthropic released Claude Fable 5, the first public version of its Mythos model. But alongside the launch came something unusual: strict safety limits, blocked responses in high-risk areas, and a mandatory 30-day data retention policy, even for some enterprise customers that previously had zero-retention agreements.

Now, most people will focus on the model.

But I think the bigger story is what this says about where AI is heading.

For years, the race was about building smarter systems.

What's actually happening now is that leading AI labs are building governance systems around those models. Monitoring. Guardrails. Approval layers. Safety infrastructure.

Why's that?

Because once a technology becomes powerful enough, capability alone is no longer the bottleneck. Trust becomes the bottleneck.

And that raises a bigger question.

If the most advanced models require guardrails, monitoring, retention policies, and approval processes, who ultimately controls access to intelligence?

Think about that for a second.

We spend a lot of time talking about who will build the smartest AI.

But maybe that's becoming the wrong question.

Maybe the more important question is: who builds the trust systems that make that intelligence safe enough to use?

Because if intelligence keeps getting cheaper and more abundant, trust may become the scarce resource.

And that could end up being where the real power sits.

I'm HURRATUL and this is The U Lab Daily Brief on venture capital, technology and the future of innovation.

SpaceX is expected to raise at least $85 billion in what could become the largest IPO in history.

But the most interesting question is not what happens on the listing day.

It is what business SpaceX actually becomes over the next decade.

The bull case is that SpaceX is no longer just a launch company.

Starlink could become a global communications platform. AI compute could become a multi-billion-dollar revenue engine through customers like Anthropic and Google. Grok could emerge as a serious competitor in the foundation model market. And the launches, lunar missions, and even future orbital infrastructure could create entirely new revenue streams.

If that happens, today’s valuation may eventually look conservative.

The bear case is that almost every pillar of that story faces ex*****on risk.

Starship is not yet proven at scale. Starlink is adding subscribers, but revenue per user is declining. Competition is increasing. AI compute is benefiting from today’s scarcity, but scarcity rarely lasts forever. And if AI models become more efficient, the economics of compute could change dramatically.

Then there is Elon Musk.

For two decades, SpaceX and Elon Musk have been almost impossible to separate.

Which raises a deeper question:

How much of SpaceX’s future value comes from its technology, infrastructure, and engineering capabilities—and how much comes from Musk’s ability to repeatedly identify and pursue opportunities that others do not see?

The question isn’t whether SpaceX is valued correctly.

The question is whether traditional valuation frameworks can keep up with a company that keeps changing its own category.

Because the biggest opportunities may not be the markets SpaceX is in today, but the entirely new markets it could create tomorrow.

I am Hurratul, and this is the U Lab Daily Brief on venture capital, technology and the future of innovation