What AI Cannot Commoditize: The Future of Human Advantage

Human Advantage in the AI Era | Venture Capital, Consumer AI, and the Future of Relationships

A Conversation with Jing Kuang

YouTube: https://www.youtube.com/watch?v=GnIHiGpQPbY

Spotify: https://open.spotify.com/episode/2kubdv51CMbD6i8iAcUpjA

Apple Podcasts: https://podcasts.apple.com/cz/podcast/stanford-mba-investor-founders-take-on-what-ai-cannot/id1859801184

AI is changing the economics of building companies.

It is reducing the cost of knowledge, increasing individual leverage, changing how venture firms operate, and allowing smaller teams to accomplish what previously required much larger organizations. But as technological capability expands, the more consequential questions may increasingly be human ones.

What remains distinctly human? What happens to venture capital when AI can perform much of the analytical work traditionally done by investors? What creates defensibility in consumer AI when technology itself becomes easier to replicate? And in a world of abundant information and content, what happens to trust, relationships, and human judgment?

In this edition of The U Lab Conversations, I spoke with Jing Kuang, Founding Partner of Y+ Ventures, Co-Founder of Cresca, and a Stanford GSB alum. Her journey spans Peking University, Procter & Gamble, Stanford GSB, cross-border mergers and acquisitions, venture capital, and entrepreneurship.

Our conversation explored agency, access to venture capital, interdisciplinary founders, AI-native investing, consumer AI, behavioral shifts, trust, relationship intelligence, female founders, early-stage investing, and what Jing calls the “joy of being unemployable.”

What emerged was a discussion about where human advantage remains as AI gives individuals and organizations significantly greater leverage.

THE UNSTOPPABLE MOMENT

My Question to Jing:

You shared when you were little, you were very naive and you did not think that any limits existed. And you've also written that growing up, you wanted to be excellent and you wanted excellence in everything, which also gave you leverage. That leverage gave you agency, and you talk a lot about agency. So tell us something about your childhood, your journey, and what gives you the unstoppable spirit that you have today?

Jing's Perspective

For Jing, the unstoppable spirit she carries today did not come from a single experience. It evolved across different stages of her life. She grew up in China during a period when GDP was expanding at more than 8% annually and watched the physical world around her transform, from a city with barely any traffic lights to one connected by high-speed trains and airports. That environment gave her an early sense of optimism and possibility. At home, her parents reinforced it through unconditional support. They made her feel that if she wanted to go to outer space the next day, she could somehow make it happen. As she described it, she had not yet seen the walls, so she was not afraid of running toward them.

Later, that confidence was reinforced by mentors. After Stanford GSB, Jing was given a leadership opportunity she now believes she was probably not fully prepared for: becoming President and CEO of a subsidiary under one of China's largest financial groups and overseeing cross-border mergers and acquisitions. Someone believed in her before she had enough evidence to believe in herself. She had the courage to say yes, and that yes changed her life.

Today, the source has shifted again. Jing said much of her energy now comes from what other people reflect back to her. Whether through building a company, writing, creating a fellowship, or working with students, she feels she is sending out something small, but people sometimes return with stories about how something she wrote gave them language for their own ambition or how a program she created gave them confidence to attempt something they had thought impossible. Their courage, growth, and feedback now give that energy back to her.

The Takeaway

Agency compounds. Early belief expands what someone is willing to attempt. External conviction from our well wishers can unlock opportunities before internal confidence catches up. Over time, the highest-leverage form of agency may be the ability to create that same sense of possibility for someone else.

MERITOCRACY VS NETWORK EFFECTS

My Question to Jing:

A lot of ambitious people initially believe that meritocracy alone is enough. But in the world we live in, networks, access, trust, and social capital also shape outcomes. What is your take on that? And especially because you are associated with the Stanford GSB network, which is so powerful in itself, what is your take on that?

Jing's Perspective

Jing's view is straightforward: meritocracy is the baseline. Competence may get someone into the room, but it does not determine how far that person ultimately goes. Once a basic threshold of capability has been crossed, she believes character becomes increasingly important: integrity, authenticity, humility, generosity, how someone treats people, whether others trust them, and whether people choose to help them when they have many alternatives.

She returned to principles our grandmothers taught us precisely because they remain useful: be honest, be kind, help people, respect people, and keep your word. Her broader argument is that small behaviors repeated over time become habits, habits shape character, and character ultimately influences outcomes. Merit may get someone into the tent. Character determines what happens after they enter it.

The Takeaway

Merit creates eligibility. Trust creates repeat access. In dense professional networks, the long-term advantage is not simply being competent enough to enter the room, but becoming the person others repeatedly choose to work with, recommend, back, and introduce. Social capital is often the accumulated return on competence plus character.

ROOTEDIN

My Question to Jing:

The RootedIn VC Fellowship, which you've started, is actually redesigning access into venture capital. So, what specific problem were you really trying to solve through RootedIn when you started it?

Jing's Perspective

RootedIn is personal for Jing because it is essentially the program she wishes had existed when she was a student. When she attended Stanford GSB roughly a decade ago as an international student, she had not grown up inside the Silicon Valley network. Venture capital appeared mysterious, closed, and relationship-driven. Years later, she returned and saw that the same chicken-and-egg problem still existed: to enter venture capital, candidates are frequently expected to already have venture capital experience. But getting that experience first requires someone to let them into the industry.

The problem becomes more pronounced for international students, first-generation immigrants, and outsiders who may not even understand the industry's language or unwritten norms. RootedIn was designed to close that gap by bringing several elements together at once: real work experience, mentorship, network access, and knowledge.

The underlying belief is simple: trust can be built and networks can be earned. RootedIn currently operates within the Stanford ecosystem, although Jing hopes that with sufficient bandwidth and partnerships it may eventually expand to other universities, places, and potentially other countries.

The Takeaway

Access in venture compounds. Experience creates credibility, credibility creates relationships, and relationships create the next opportunity. The structural disadvantage for outsiders is often not lack of ability, but lack of an initial mechanism through which that compounding cycle can begin. RootedIn is designed to create that first credible entry point.

BUILD+ AND THE ENGINEER-SCOUT

My Question to Jing:

Through Build+, you are teaching students how to build technology, how to think like investors, opportunity mappers, and what you call engineer scouts. So, do you think the next generation of founders will need to become much more interdisciplinary in the way they operate, and how is Build+ helping them do so?

Jing's Perspective

Jing's starting point is that AI is making knowledge increasingly accessible. If knowledge becomes widely available, the differentiating factor is no longer simply what someone knows. It becomes how they connect what they know.

That is why she believes the next generation of founders will need to think across disciplines. A technical founder may take engineering mental models around systems, constraints, and debugging and apply them to investing or market analysis. A liberal arts student may take ideas from history, art, or storytelling and apply them to engineering and product design. Each can potentially identify something that a specialist operating entirely within one discipline may miss.

Build+ emerged partly from observing technically strong students who were excited to build but had not yet identified the problem they wanted to solve. Jing calls them aspiring technical founders. Instead of immediately asking them to start building, Build+ asks what would happen if they first learned to think like investors. That led to the idea of the Engineer Scout: someone who can build technology while also mapping opportunities, sourcing signals, identifying unmet needs, and understanding the founder-market space.

Jing believes that in the AI era, some of the most powerful people will be interdisciplinary translators: people capable of moving between fields, connecting ideas that do not obviously belong together, and turning scattered knowledge into original insight.

The Takeaway

As AI commoditizes access to knowledge, retrieval becomes less scarce and synthesis becomes more valuable. The edge shifts toward people who can move across domains, recognize non-obvious connections, and identify where technical capability should actually be deployed. In a world where more people can build, problem selection becomes a larger source of differentiation.

AI-NATIVE VENTURE CAPITAL

My Question to Jing:

You run Y+ Ventures, and you describe it as human-centered by nature and AI-native by design. Venture capital has historically been such a trust, relationship, and intuition-driven industry. Structurally, what changes inside a venture firm when AI becomes part of the operating system, rather than just another tool that we use?

Jing's Perspective

For Jing, this change is already underway. In the past, when a startup opportunity arrived, an investor's first instinct might have been to search Google, read reports, and call people. Today, Jing often begins by asking AI models. She uses different systems to cross-check one another, understand markets, validate assumptions, generate counterarguments, and ask questions such as: What am I missing? What would make this company fail or succeed?

That means AI is no longer limited to execution. It has entered the thinking process itself. In sourcing, AI can help identify weak signals earlier. In diligence, it can help analyze markets and competitors. Work that analysts traditionally performed while preparing investment memos can increasingly be completed with AI, provided humans supply the relevant context. This allows a small venture team to cover substantially more ground than before.

But Jing makes an important distinction between using AI frequently and actually being AI-native. An AI-native firm is designed around AI from the beginning. It changes workflows, speed, research, due diligence, how conviction is formed and changed, and even how founders are supported.

One thing does not change: context. AI's output still depends on what data it receives, what questions are asked, and what nuances humans provide. AI can process information and generate scenarios, but humans still determine what matters. That is where the “human-centered” part of Y+ Ventures remains essential.

The Takeaway

AI is compressing the cost of analysis inside venture. If research, market mapping, competitive intelligence, and first-pass diligence become broadly available, those activities become weaker sources of proprietary edge. Differentiation moves toward what is harder to automate: access, context, question quality, judgment, and the ability to form conviction before the evidence becomes obvious.

COASE THEORY AND THE NEW SIZE OF THE FIRM

My Question to Jing:

You shared an article on Substack which I went through, and it mentioned Coase Theory. You've written about how AI reduces coordination costs and dramatically increases individual leverage, which we are actually talking about right now as well. Coase's theory suggests that firms emerge because coordination is costly. So, if AI reduces those costs, do you think we'll eventually see much smaller but far more powerful venture firms and startups? If a handful of people can build what required hundreds, how does that reshape the economics of venture capital itself?

Jing's Perspective

Jing believes we are already beginning to see this shift, but she does not think the answer is simply that every company becomes smaller. Her interpretation of Coase is more nuanced because AI is reducing costs on both sides of the firm's boundary.

AI can lower external market transaction costs such as search and negotiation, which can reduce the need to internalize certain activities inside a company. But AI can also lower internal organizational costs such as reporting, coordination, and monitoring across teams. That can allow organizations to become larger because the same management capacity can coordinate significantly more activity.

The result, in Jing's view, may therefore be polarization rather than universal shrinkage: giant platforms on one side and hyper-leveraged smaller teams on the other, with pressure on parts of the middle. A team of ten may increasingly be able to accomplish work that once required far more people.

She pointed to companies such as Cursor, Lovable, and ElevenLabs as examples of the amount of economic value relatively small teams can now create. Her guess is that a new organizational sweet spot could emerge around teams of roughly 20 to 80 people.

Her larger point is that AI does not simply make companies smaller. It changes the reasons companies need to exist, and that fundamentally changes their optimal size and structure.

The Takeaway

AI does not have a single directional effect on firm size. It lowers the cost of coordinating both markets and organizations. That could create a barbell structure of highly leveraged small teams and enormous platforms while weakening the historical relationship between headcount and scale. For investors, the more important metric may increasingly be economic output per unit of organizational complexity.

HUMAN JUDGMENT IN VENTURE CAPITAL

My Question to Jing:

Traditionally, investors build edge through years of pattern recognition. But now AI can analyze markets, behaviors, and data faster than humans. So where do you think uniquely human investing judgment still matters and will continue to matter the most?

Jing's Perspective

Jing's answer begins with an important limitation of pattern recognition: venture capital is not a fully standardized asset class.

The reason an investor initially backs a company may not be the reason that company eventually succeeds. Markets change. Products pivot. Timing shifts. A startup may be doing one thing when an investor writes the first check and something substantially different by the time it becomes a public company.

The founder is often one of the few relatively constant variables through that process. That is one reason venture investors repeatedly emphasize the founding team.

Human judgment therefore remains particularly important in evaluating people, context, and hidden dynamics. Companies do not always fail because the market thesis was wrong. Sometimes co-founders break apart. Those conflicts may emerge from differences in working style or seemingly small human interactions that are difficult to model from historical data.

Jing also pointed out that pattern recognition is inherently backward-looking. Events often make sense in hindsight. Investing requires looking forward, where the evidence is incomplete and outcomes remain uncertain. For her, that is why judgment continues to matter deeply even as AI becomes more capable.

The Takeaway

AI can improve the quality and speed of pattern recognition, but venture investing is ultimately a decision about a future for which complete data does not yet exist. As analytical work becomes cheaper, investor edge moves toward interpreting incomplete signals, evaluating founders, distinguishing structural change from historical analogy, and forming conviction under genuine uncertainty.

CONSUMER AI

My Question to Jing:

Most capital initially, as you know, in AI rushed towards AI infrastructure and enterprise AI, but with Y+ Ventures, you're deeply focused on consumer AI. What do you think the broader market is still underestimating about consumer AI, and where's the untapped potential? And what was your reason to focus on consumer AI?

Jing's Perspective

Jing believes consumer AI remains underestimated because its opportunity extends far beyond productivity. In her view, consumer AI touches behavior, identity, aspiration, memory, relationships, daily life, and emotional resonance.

People do not buy products only for utility. They also buy a projection of their future selves. That makes consumer technology fundamentally connected to human motivation and behavior rather than only functionality.

For Jing, this creates significant opportunity for investors because consumer AI can participate in areas of life that are deeply personal and repeated. The opportunity is therefore not limited to making existing tasks faster. It extends into how people behave, what they aspire to become, how they relate to others, and how technology becomes integrated into everyday life.

The Takeaway

Consumer AI is not only a productivity market. Its larger opportunity may sit in products that become embedded in behavior, identity, aspiration, relationships, and daily life. The strategic question shifts from “What task does this automate?” to “What recurring part of the user's life does this product earn the right to become?”

BEHAVIORAL MOATS

My Question to Jing:

You often talk about behavioral shifts and resonance instead of just traditional SaaS metrics. In consumer AI, do you think changing user behavior itself is becoming a stronger moat than the technology?

Jing's Perspective

For Jing, understanding behavior can create a defensible advantage. If a company understands what makes users' lives easier and what future version of themselves they are trying to become, it can build something much harder to copy.

Humans generally prefer less friction. They prefer experiences that are easier, faster, more intuitive, and less mentally demanding. Consumer companies need to understand those preferences and translate them into product design, UI, UX, and functionality.

But Jing also emphasized deeper behavioral shifts that are initially difficult to see. Changes in marriage, children, social structures, education, and communities can develop gradually before becoming obvious at scale. Companies that identify these changes early can build for where behavior is going rather than where it currently sits.

Her view is that a great consumer AI company does not simply build for today's behavior. It senses behavioral shifts before they become obvious, and that can become part of its moat.

The Takeaway

Technical advantages can diffuse quickly. Behavioral advantages compound more slowly but can become harder to displace. If a product correctly anticipates an emerging behavior, becomes part of that behavior, and accumulates context through repeated use, the moat can migrate from the underlying technology to the habit formed around it.

TRUST CAPITAL

My Question to Jing:

In a world where AI can generate almost infinite content and output, do you think trust becomes even more valuable and scarce? And because we're talking about humans, behavioral shifts, and the next generation of how people are going to adapt to technology and many things in the consumer AI space, do you think consumer AI can help solve for it?

Jing's Perspective

Jing agrees that when AI can generate effectively unlimited amounts of content, trust becomes scarcer and therefore more valuable. In the social media era, metrics such as followers, likes, and impressions became signals through which people inferred influence and credibility. But she questions how relevant those signals remain when content itself becomes abundant.

The question increasingly becomes much simpler: who do I actually trust?

Jing was more cautious about assuming that consumer AI itself will solve that problem. Her view is that the future of trust may become more private, more intentional, and more controlled by individuals.

The Takeaway

Abundance changes what carries signal. When content becomes cheap to produce, volume becomes a weaker proxy for credibility and trust becomes relatively scarcer. The next trust layer may therefore move away from public-scale metrics toward private context, reputation, intentional networks, and greater individual control over whose information deserves belief.

CRESCA AND RELATIONSHIP INTELLIGENCE

My Question to Jing:

And because you made that point, it brings me to the next segment, which is what you're building with Cresca. It feels much bigger than productivity software. It's relationship infrastructure for the AI era. What made you believe that memory, follow-up, and relationship intelligence would become such an important category? And tell us why and how you started Cresca, and who's your co-founder?

Jing's Perspective

Cresca began with a simple belief for Jing: meaningful relationships shape our lives, opportunities, and happiness. But relationships are not built from generic knowledge. They are built from specific memory.

Jing describes her own memory as highly visual and story-based. She remembers where she met someone, what they were wearing, what they ate, what the room looked like, and what was said in that setting. The important feature of human memory, in her view, is not simply storage. It is retrieval. Memories can sit quietly until a small trigger brings them back and allows seemingly unrelated information to connect.

She used a simple example. Eating lobster today might remind her of someone she ate lobster with months ago. She might then remember that several otherwise unrelated people she knows all love lobster and decide to bring them together for dinner. That moves beyond memory into what she calls relationship intelligence: connecting dots that do not appear connected on the surface.

The need became particularly visible after Jing and her family moved back to the United States three years ago. As immigrants, they had to rebuild parts of their social world: reconnecting with old friends and alumni, meeting new people, remembering where people came from, what they cared about, how their names were pronounced, who their children were, and what they had discussed previously. The process became overwhelming.

The broader problem is that people now have more contacts, messages, and LinkedIn connections but often less context. A LinkedIn connection is not automatically a relationship, and a contact stored in a phone is not a relationship. Relationships become meaningful when people remember context, follow up with care, and turn weak ties into stronger ties.

Jing saw how manual this process remained through notes, memos, notebooks, and memory. She even joked that she had become a human version of what she and her husband were trying to build.

Cresca is their response. Jing describes it not simply as a productivity tool but as a relationship memory layer designed to preserve context, remember what matters, enable thoughtful follow-up, and turn scattered interactions into real relationships.

The Takeaway

Digital networks optimized for connection volume, not relationship depth. As generic knowledge becomes abundant, relationship-specific context becomes relatively more valuable because it is personal, proprietary, and accumulated over time. The opportunity behind relationship intelligence is therefore not better contact storage. It is making previously unusable social context actionable.

BUILDING WITH HER HUSBAND

My Question to Jing:

You made a very specific point that you're building Cresca with your husband, but while we were talking before, you said that he's the one entrepreneur you would invest in if you could only invest in one. Why do you say that, if you could unpack for us?

Jing's Perspective

Jing readily admits that she is biased. He is her husband, they have been married for ten years, and they have two children together. But she also jokes that this has given her ten years of due diligence and ten years of data points.

She would invest in him because he possesses many of the qualities she looks for in founders: adaptability, resilience, big-picture thinking, care, and the orientation of a lifelong founder. Over a decade together, she has seen how he handles pressure, ambiguity, responsibility, and change in ways that investors normally have only a few meetings to assess.

Building together has also reinforced Jing's thinking about trust. When two people have deep trust and unspoken understanding, friction costs fall. They do not need to explain every small decision or constantly protect their egos. They can move faster, disagree more honestly, and return to the mission more quickly. That kind of trust is not created overnight. It develops through shared history, shared pressure, and shared responsibility.

Their skills are also complementary. Jing described how her husband sees structure while she sees emotion; he sees product while she sees narrative; she tends to see risk while he sees possibility. Different strengths can create something neither person would create independently.

For Jing, the strongest teams therefore combine four things: trust, complementary skills, shared conviction, and a mission that pulls them through difficult moments. AI may provide increasingly powerful tools, but human judgment and perspective determine how much of that capability becomes useful.

The Takeaway

Co-founder trust is not merely cultural compatibility. It is an operating advantage. Deep trust reduces coordination costs, complementary strengths increase the team's effective field of view, and shared conviction reduces the probability that inevitable disagreement becomes organizational fracture. Founder-founder fit can therefore be as consequential as founder-market fit.

VENTURE AND ENTREPRENEURSHIP

My Question to Jing:

You said something really fascinating in the beginning, that venture and entrepreneurship are not binary. Investors and founders are not sitting on the opposite side of the table. Can you unpack what you mean by that?

Jing's Perspective

For Jing, entrepreneurship is usually the driving force. The founder sees something that does not yet exist and decides to make it real. That is the center of value creation.

Venture capital can then become a catalyst. At its best, capital brings more than money. It can bring belief, networks, talent, credibility, and resources around a founder's vision, helping transform something initially seen by one person into something many people become willing to build toward.

That is why Jing does not view venture capital and entrepreneurship as opposing worlds. A strong founder can become a strong investor, and a strong investor may have been or become a founder. They operate within the same value-creation ecosystem.

Founders who understand how investors think can communicate timing, risk, and scale more clearly. Investors who understand what building actually requires can develop greater empathy for founders and recognize that a company is not merely a pitch deck. It is execution, uncertainty, trade-offs, and people.

For Jing, the best founder-investor relationship is therefore not based on judgment. It is based on alignment. The goal is not for one side to win against the other. It is to sit on the same side of the table and build something neither side could build alone.

Venture should not be the judge of entrepreneurship. It should be a catalyst for entrepreneurship.

The Takeaway

Capital is an input into value creation, not the source of value creation itself. The founder originates the vision and execution; venture becomes differentiated when it increases the probability, speed, or scale of that outcome through more than money. The best investor relationship is therefore not adversarial selection followed by passive ownership. It is aligned amplification.

FEMALE FOUNDERS AND ACCESS

My Question to Jing:

I'm quoting here the PitchBook data, which shows that only around 6% of US venture deals in 2025 went to female-only founding teams. As both a female founder and an investor, do you think the deeper issue is capital access, pattern-matching bias, network asymmetry, or something even more structural inside venture itself? And why do you think those numbers remain so persistently low despite years of industry awareness and inclusion initiatives? And this number of 6% is just for the US. We're not even talking about the developing world and other parts of the world. Why is this number so low?

Jing's Perspective

Jing believes structural issues do exist. If female founders receive a small share of venture funding, she argues that the industry also needs to examine who is writing the checks. When women remain underrepresented at the partner level in venture capital, the networks and judgments operating through the system are likely to reflect some of that imbalance.

She pointed to two barriers she had encountered in research on women reaching senior positions: networks and role models. These can reinforce one another. She also drew an important distinction between advisors and sponsors. Women may have people willing to advise them while lacking people willing to actively sponsor them and create opportunities on their behalf.

Awareness matters. More open discussion matters. Greater visibility for female investors and founders matters. Over time, Jing believes these factors can change the system.

But she is also careful about the narrative. She prefers to describe someone as a great founder who happens to be female rather than making gender the primary lens through which the founder is evaluated. She does not believe that is fair to female or male founders.

The goal should still be rigorous evaluation: market size, quality of insight, founder traits, execution capability, resilience, and the ability to build something meaningful. Gender, ethnicity, immigration background, or whether someone is technical or non-technical may be part of the founder's story, but it should not become the entire story.

Jing does not argue for ignoring gender because structural gaps are real. She argues against reducing founders to gender.

Her formulation is clear: the goal is not to lower the bar. The goal is to widen the door.

The Takeaway

The core distinction is between standards and access. A high investment bar can remain unchanged while the system expands who receives the networks, sponsorship, visibility, and opportunity required to reach it. The objective is not preferential evaluation. It is a broader and potentially higher-quality opportunity set reaching the same rigorous investment process.

THE FIRST MILE OF VENTURE CAPITAL

My Question to Jing:

At Y+ Ventures, you talk about solving the first mile with different levels of conviction, from a $5,000 handshake check to larger follow-on investments. At the earliest stage, before financials and metrics fully exist, what signals, traits, or frameworks give you conviction to back a founder?

Jing's Perspective

For Jing, the reason to make a handshake investment at the earliest stage is precisely that there are not yet enough signals.

If a startup already had substantial ARR, metrics, and operating data, Y+ Ventures could make a conventional investment decision. But at the first mile, founders may still be pre-product, pre-team, or pre-metrics. The company may not contain enough evidence to support a normal venture decision.

The question then becomes what an investor should do when something looks promising but proof does not yet exist.

For Jing, the handshake check is a way of putting skin in the game. The amount itself is not the point. The attitude behind it is. Rather than remaining on the sidelines and continuing to give advice without commitment, she wants founders to know that if Y+ sees something promising, the firm is willing to participate.

She is equally clear about the alternative. If she is not interested, she would rather give a founder a quick no than consume months of the founder's time without making a decision.

At this very early stage, Jing pays particular attention to founder traits such as adaptability, resilience, and ownership. Those traits may matter more than the initial idea because the idea itself can still change substantially.

The handshake investment allows the investor and founder to begin building trust and conviction together. It is not the end of conviction.

It is the beginning of conviction.

The Takeaway

At pre-signal stages, waiting for certainty means waiting until the asymmetry has already compressed. A small early commitment lets an investor buy information through participation, build a relationship before consensus forms, and increase exposure as conviction strengthens. The first check is not a declaration of certainty. It is a mechanism for learning under uncertainty.

THE JOY OF BEING UNEMPLOYABLE

My Question to Jing:

As someone who has operated across entrepreneurship, venture capital, M&A, and now AI-native company building, what advice would you give to founders and aspiring investors trying to build meaningful careers in this new AI-driven world?

Jing's Perspective

Jing's advice is unusual: taste the feeling of being unemployed as early and as cheaply as possible.

She does not mean unemployment negatively. She means experiencing the psychological state of not belonging to an institution, not having a job title define you, and not having a company structure telling you what comes next.

Then observe what happens internally.

Do you feel lost or free? Anxious or agentic? Do you feel that an identity has disappeared, or do you feel more like yourself?

Jing believes those reactions provide important information. Not everyone needs to become a founder. There is nothing wrong with wanting a stable job, regular paycheck, clear structure, and predictable life.

But founders may be wired differently. They may feel uncomfortable inside too much structure and more alive in ambiguity. Jing connects this to an idea she learned from Stanford professor Irv Grousbeck: the joy of being unemployable.

For Jing, it is a way of being in the world. She feels agency when she is responsible for her own path.

Her recommendation is therefore not to romanticize entrepreneurship from a distance. Test it. Create a low-cost period in which a title or institution does not protect your identity and observe how you respond.

If you hate the experience, that is valuable information.

If you feel freer, more energized, and more yourself, that is valuable information too.

This becomes even more relevant in the AI era because individual leverage is increasing. One person can build more, learn more, and create more than before. But that leverage matters only if someone understands what kind of person they are and what kind of life gives them energy.

The deeper question is not simply whether you can build something.

It is whether you can feel joy when you are on your own and nobody else is giving you permission.

The Takeaway

AI lowers the cost of testing agency. Individuals can now build, learn, distribute, and experiment with far less institutional infrastructure than before. That makes entrepreneurship easier to test, but not necessarily easier to live. Before optimizing for founder status, test your revealed preference for ambiguity, autonomy, and responsibility when no external institution is supplying identity or permission.

CLOSING REFLECTION

When I began this conversation with Jing, I expected us to spend much of our time discussing venture capital, consumer AI, and how AI is changing the structure of companies.

We did.

But underneath those discussions was a consistent theme: as AI increases technological capability, human judgment does not disappear.

In venture capital, AI can analyze markets, generate counterarguments, accelerate diligence, and make small teams significantly more productive. But investors still have to evaluate founders, relationships, context, and uncertainty.

In consumer AI, technology matters, but understanding behavior, identity, aspiration, memory, relationships, and how those behaviors are changing can become a significant source of advantage.

In organizations, AI changes coordination costs and therefore changes the size and structure of firms.

In relationships, abundant connectivity does not automatically create meaningful connection. Context, memory, thoughtful follow-up, and trust still matter.

And for individuals, greater technological leverage makes agency even more consequential.

Perhaps one of the most important ideas from the conversation came from Jing's distinction between knowledge and connection. If AI makes knowledge increasingly accessible, the advantage is no longer simply what we know. It is how we connect knowledge across disciplines, people, and context, and how we decide what deserves to be built in the first place.

AI can increase the amount of capability available to an individual, a founder, an investor, or an organization.

What remains human is the judgment about what to do with it.