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If there’s one thing Charles Hudson knows about backing founders, it’s that you never know whether someone will make a good CEO until you put them in the role.

“Until they make their first [mistake],” he says, “it’s all theoretical. The best founders rise to the occasion.”

And Hudson should know. Since founding Precursor Ventures in 2015, he’s backed over 375 early-stage companies across four funds, becoming one of the most prominent Black investors in Silicon Valley — and one of the most recognizable faces in venture capital, period. But success hasn’t stopped him from asking the hard questions, like how do we decide who gets to be a founder? And are the same people succeeding simply because we keep giving them opportunities?

For Hudson, the issue is simple. While venture capital promotes itself as an industry based on bold investments and even bolder returns, it actually leans on cold data and equations to dispense relatively safe, measured bets. These “measured bets” are much more accessible to someone with a Stanford diploma, a stint at Y Combinator, or the road-tested wisdom of a repeat founder than someone without. In short, someone who is White and often male.

“What we do is focus on founders who are more than one degree removed from the core set of folks who have easy access to venture capital,” Hudson says. “And it turns out when you apply that strategy, you end up with a pretty large pool of women and people of color.”

The environment Hudson and his team have cultivated at Precursor Ventures produces interactions you’d rarely observe elsewhere — like female founders giving fundraising advice to male founders, and Black founders harnessing the social networks of White men.

"What I've found is that the experience of fundraising as a Black founder is really negative. You get a lot of no’s, and some of those no’s are delivered in a really tactless, rough way. I think it wears on you."

Indeed, a lot of the folks who make their way to Precursor’s doorstep haven't fundraised before. They’ve also never hired or fired anyone, set a strategy, or managed a budget — at least not as the operator of a startup. For many of them, building a company is what Hudson describes as “jumping into the deep end while holding a kettlebell.” That said, he’s never been shy about backing them.

Like his peers, Hudson wants to be the best pre-seed investor on the planet. Unlike many of his peers, he believes in following a formula that others see as theoretically impractical — what he calls investing in “strangers with no data.” In less than a decade, he’s built up a colorful roster that spans everything from women’s health apps to Gmail challengers.

Evidently, the results are paying off, one stranger at a time.

Talking to Hudson, you get the sense that he isn’t the type to self-label as a contrarian — he doesn’t come across as having a chip on his shoulder, nor does he seem hell-bent on winning external validation. Sure, he has strong convictions, but he’s patient about them. He’s also paid his dues to get here.

Funnily enough, the journey began at Stanford, where decades ago, Hudson earned two degrees himself and now 

. “I do find it ironic that a two-time Stanford alum is trying to make sure that Stanford doesn't become the default in our industry,” Hudson says, clarifying, “but I'm not against Stanford grads, I just think our industry overindexes on known pedigree archetypes like Stanford, Harvard, etc.”

While working towards his first Stanford degree, a flier in the campus coffee house alerted him to his first startup opportunity, an ecommerce internship with then-Web 1.0 titan, Excite@Home. Strong internship performance and a lucky connection helped him land his next gig at In-Q-Tel, the CIA’s venture capital group. A few more forays into big and little tech later, Hudson became a tech founder himself as the CEO of Bionic Panda Games, a mobile game studio. It was around then that he caught the attention of several micro venture capital firms, including an iconic seed-stage outfit called Uncork Capital (then known as SoftTech VC) that convinced him to leave the world of product-building behind.

The Uncork that Hudson came on board to in 2010 was a one-man operation, the one man being French luminary Jeff Clavier, who was single-handedly sifting through prospects, negotiating and closing deals, and providing ongoing support for companies post-investment. “It was really amazing to watch,” Hudson told Hustle Fund’s Elizabeth Yin in 

, “I didn't fully appreciate how much work [went] into running a fund until I started my own.”

Back then, the nature of seed investing looked a lot like today's pre-seed investment arena. Hudson and Clavier were writing $400K checks into million-dollar seed rounds for fledgling startups that were pre-product-market fit and often seeking a strategic partner to help catapult them to Series A. In return, Uncork would take a 7–10% ownership stake with room to make additional investments if the company managed to break out.

In the span of a few years, the firm went from raising a $55M Fund III to an $85M Fund IV, with the influx of capital justifying heftier checks and a more ambitious follow-on budget. This was paired with a noticeable shift in the people Uncork attracted. “We [started] meeting a different type of entrepreneur than we used to,” Hudson remembers. “More experienced, more credentialed, and more connected.”

This concerned him. “I thought, what about the people we met who were on the outside looking in — who was helping them? And I kind of concluded that the answer was [no one].” It seemed like only a matter of time before the starter checks Hudson had grown so fond of writing would no longer have a place within Uncork’s scaling strategy. But if that were true, perhaps Uncork was no longer the place for him, either.

Despite sensing a growing divergence between what he wanted and where Uncork was going, Hudson wasn’t in a rush to leave. He and his wife, Brandi, had just gotten married, and starting a family was next on the horizon. Having only been a partner at Uncork for a couple of years, the idea of uprooting his career seemed like it could throw off a delicate balance.

But it was Brandi who persuaded him to think with urgency. She knew the risks, but she also knew what her husband was capable of. Here was an opportunity to build something that could not only change the future of their family but touch the lives of others, too. “I remember her saying, ‘We both know this is what you're supposed to be working on — this is the right thing to do.’” Hudson recalls. “I probably would've waited longer to start Precursor if not for her,” he admits.

When Hudson finally shared his decision, Clavier gave him his full blessing. Just a decade earlier, a younger version of Clavier had been the determined visionary when he left Reuter’s venture arm to launch Uncork. And now, it was time to pass the torch.

One look at Precursor’s website and you can tell it’s a place that puts people before companies. The homepage greets you with the warmth of a proud parent’s refrigerator door: a simple gallery of smiling founder headshots — no logo mosaic in sight. Every row has at least one person of color, and almost every other face belongs to a woman.

Getting to this point wasn’t easy — much like any founder, Hudson had to convince people to believe his thesis was worth funding.

In the world of venture capital, limited partners (LPs) are the classified faction of high-net-worth individuals and foundations who selectively finance the visions of general partners like Charles Hudson. (A role not unlike the silent backers behind filmmakers.) To earn their approval, Hudson needed to craft a pitch that would set him apart from a sea of micro VCs, each with an identical business model: giving away someone else’s money.

One day, after an early failed pitch, he drew two intersecting lines on a piece of paper. Along one axis, he scribbled "data on the business," and on the other, "data on the founder,” forming the four quadrants of the venture capital playing field. For most investors, the strategy had been clear: bet on companies with strong metrics or founders with impressive resumes — and if you were willing to pay a premium for it, bet on both. The remaining quadrant, where data was scarce in either aspect, represented the quietest corner of venture capital. It was in this no man’s land that Hudson found his competitive advantage.

Since then, he’s kept Precursor relatively capital-efficient compared to other early-stage firms, choosing to channel its resources into founders, more than their startups. These resources focus on three main areas: fundraising help, tactical conversations, and community. When it comes to fundraising, the value Precursor provides is simple: “We know lots of VCs, and we know what a good deck looks like.” The tactical conversations are a little more intimate.

“I try to be an honest broker for all of our portfolio companies. If I think that they're off track and they don't see it, hopefully, we've built enough trust and respect that I have the space to tell them.” It’s not his style to be prescriptive in these one-on-ones — as he puts it, he’d rather play the role of a founder’s GPS than make them feel like they’re in a self-driving car. That way, they can own their successes and their failures.

As for community, Precursor maintains peer support groups, a steady cadence of social mixers and programming events, and an active Slack channel to encourage as many founder-to-founder conversations as possible.

The uniqueness of Precursor’s atmosphere can’t be summed up by any single element — it emerges from the gestalt of bringing these pieces together with a diverse set of founders. “I think what you miss out [on] with a monoculture is that you don't get the chance to take advantage of what everybody's bringing to the table,” Hudson notes.

And when all is said and done, there’s only so much Precursor can help with — the rest is up to the founders themselves. “[We’re] expecting people to figure out a lot of things that they haven't done before,” Hudson says. “If they can overcome their lack of experience and it doesn't kill the company, they’ll come out the other end with a better company — and [as] a stronger founder.”

Hudson isn’t naive. He knows that it’s impossible to be a contrarian in a vacuum. After all, playing lone wolf won’t earn him any LPs, nor will it get him on any cap tables. The difficulty is that there’s been no precursor to Precursor; its methodology doesn’t fit neatly in any of the preconceived categories. It doesn’t help that Hudson’s commitment to finding people as early as possible — “pre-everything” — has meant embracing first-time founders, solo founders, incomplete teams, and unstructured markets — categories most VCs shy away from — leaving him with a bigger pool of candidates to sift through.

"[N]ot all of these folks are looking for a fund that's exclusively focused on people like them. But they are looking for a fund that treats them fairly and gives them an opportunity."

average venture capital partner only makes 3–6 yearly investments

, many question the sustainability of a solo GP who makes 30–50 while also serving as the primary point of support for his entire portfolio. Others furrow their brow at Hudson’s target check, which has historically lingered just between half a percent and one percent of the entire fund. They insist the math can’t possibly work — how could it with Precursor owning such a fractional stake in each company?

And then there are those who fundamentally disagree with Precursor’s mission. “To them, the venture game is [about] finding people who know how the system works [and funding] them,” Hudson explains. “They’re like, why would you look for people who are disconnected?”

Despite the criticisms, Hudson still believes in his approach. Like a lot of VCs, he uses a scorecard to evaluate founders — his just follows different data points. With each candidate, he asks himself: “If I write them a check, do they have the potential to get to $100M ARR in seven years with reasonably good margins?” He doesn’t base that potential on traction or even a completed product — there’s time for that. What Hudson looks for is someone who is internally motivated to grow and has the ability to navigate challenging decisions with limited information. And can’t all kinds of people fit that bill?

Some of his critics seem to have a hard time wrapping their heads around this. Others make hasty judgments. “In the beginning, every time I walked in [to pitch LPs], people would be like, oh, you must be doing a ‘diversity fund.’” With this, Hudson lets out a wry chuckle. “I'm like, first of all, I don't even know what that means. Second of all, nowhere in my deck does it say that we're focused exclusively on BIPOC founders.” Still, compared to the rest of the venture industry, he’s managed to build a portfolio far more reflective of the broader population; 54% of Precursor’s companies have at least one female founder and roughly 44% of all of its founders are people of color. And while Hudson’s micro VC firm isn’t a minority-focused one, he can relate to the scrutiny such funds have faced. “I've told LPs, ‘The irony to me is that you hold the smallest funds who want to solve these problems to the highest standard.’ Most of the funds that have a specific lens on race or gender are small, right? And I think people dissect the racial and gender breakdown of their portfolios with a microscope,” he points out.

As for Precursor, “I just want it to feel like a firm that's available to everyone,” Hudson says plainly. This shouldn’t be a controversial statement, yet it is. He doesn’t spell it out, but the elephant in the room is that certain individuals are just uncomfortable with the idea of letting outsiders in. And that argument goes beyond venture capital.

“What I've found is that the experience of fundraising as a Black founder is really negative. You get a lot of no’s, and some of those no’s are delivered in a really tactless, rough way. I think it wears on you.” There's a hint of weariness in Hudson's voice too, as he says this before continuing, “In my experience, not all of these folks are looking for a fund that's exclusively focused on people like them. But they are looking for a fund that treats them fairly and gives them an opportunity.”

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Throughout the firm’s history, no less than half of Hudson’s investment team has consisted of Black women — that this intentional decision has naturally given way to more diverse investments just underscores the crux of representation: diversity breeds diversity.

And it’s not like Precursor isn’t selective. Out of the over 3,000 pitches that cross his desk every year, Hudson only invests in 1%. “You have to be okay with walking out of a lot more meetings where you know it’s going to be a hard no,” he confesses, “but if you invest in good companies at a good price and you keep your fund size relatively small, it works.”

It’s also hard to argue with the results. By now, Precursor has been around long enough to shepherd several companies through successful exits, including Betty Labs, a live audio app developer that was acquired by Spotify in 2021 for $67.7M; The Athletic, a sports media platform that was purchased by the New York Times in 2022 for over half a billion dollars; and Sora, an automation startup that recently sold to ADP in 2023 for an undisclosed sum. Hudson regularly heaps praise on founders in his portfolio like Iman Abuzeid, a Sudanese immigrant entrepreneur whose digital nurse-hiring platform, Incredible Health, raised an $80M Series B at a $1.65B valuation just last year, making her 

one of only eight Black and Latina female founders to reach unicorn status

. “She’s a fantastic entrepreneur, like an incredibly strong operator. I’m really proud to be associated with that company,” 

. And he’s continuing to lead pre-seed rounds for first-time entrepreneurs like Madison Long and Simone May, whose company, Clutch, helps connect businesses and social media influencers in hopes of helping to close the pay and opportunity gap for Black creators.

Of course, not all of Precursor’s investments have been success stories — but that’s also the nature of venture capital. “Most VCs that I know are wrong far more often than they're right,” Hudson says.

A lot of the choosing boils down to instinct. As Hudson’s wife Brandi reminds him, “Isn't it more important to pick the best companies than it is to be able to explain how you do it?” At the end of the day, Hudson is just a practical investor searching for people who 

 be success stories. “This might sound a little like Pollyanna,” he muses, “but my aspiration is that when we're successful, it'll show the world that you can color outside the lines a bit and still be successful.”

“Right now, I’ve accepted that we’re operating near the theoretical maximum of the Precursor model as it exists today,” he says. Nonetheless, he’s optimistic that someone, someday will figure out how to do it at scale. With a different objective and a different construction, they just might succeed — and if so, Precursor will light the way, true to its namesake.

That being said, no one has a crystal ball. Not even all the data in the world could make any of this a sheer bet. And Hudson never claimed to have all the answers, just like he also never set out to be a contrarian. But if you’re a Black man championing opportunities for people who usually don’t get them, does that make you a contrarian — or are you just a human being advocating for the way things should be?

Either way, Hudson is acutely aware that the work he’s doing — the work that all

VCs are doing, really — has a direct correlation with the way that things 

 be. “Something I think about a lot is that the venture capital industry has a disproportionate impact on the future,” Hudson reflects. He pauses for a moment before continuing, “The things we choose to fund and the people we back really matter.”

These are the people who are beginning to prove, with Hudson’s help, that you don’t need the traditional data points in order to succeed — reminding us that sometimes the boldest returns can come from those who dare to jump into the deep end, kettlebell in hand.

How one investor finds success in the margins.

Charles Hudson is optimistic about outliers

“You should eat at least one small rock per day,” Google’s AI Overviews search feature infamously told a user in the days after it was released.

In another instance, it suggested adding ⅛ cup of non-toxic glue to the sauce after a user asked how to make cheese stick to pizza. (At least it specified the glue should be non-toxic.)

The blunder from Google ricocheted through the tech world and is perhaps the most notable troubled product launch of the rapidly unfolding AI boom, but it's hardly an outlier. MSN published an AI-generated obituary for a celebrity who was still alive and once recommended an Ottawa food bank as one of the city’s top tourist attractions. Employee management software provider Lattice recently announced it would offer official employee records for AI “workers” — only to roll back the product within days after massive backlash. Even Google’s AI troubles stretch far beyond its search function. The company’s stock tumbled by $100 billion in 2023 after its Bard chatbot erroneously said during a demo that the James Webb Space Telescope took the first-ever image of a planet outside our solar system. Earlier this year, its image generator also came under fire for producing historically inaccurate images — for example, including people of color in images of the U.S. founding fathers.

The promises of the generative AI boom are big, from supercharged AI copilots to unprecedented productivity and a new world of creativity. But as everyone rushes to integrate AI into their products and internal processes, there’s no denying companies are building the plane while flying it. For the most part, implementing AI has been a big mess, or at the very least, an extraordinary challenge.

Companies are struggling to wade through the hype, figure out which tools to integrate, how to develop AI quickly without locking themselves out of future breakthroughs, and how to navigate fast-moving regulation. There’s also a variety of intricate copyright, security, privacy, and compliance concerns, as well as lingering kinks with the technology and its penchant for making up information. In a 

, 66% of C-suite executives from 50 markets said they’re ambivalent or outright dissatisfied with their organization’s progress on AI and generative AI so far. They cited an unclear AI roadmap, lack of necessary talent and skills, and an absence of strategy around how to implement AI responsibly. 

30% of generative AI projects will be abandoned after proof of concept by the end of next year.

At the same time, the pressure is on. “Adopt AI or get left behind” has emerged as a common message, and shareholders want to hear about companies’ AI strategies as urgency has become the north star of the technology. James Robinson, chief information security officer at cybersecurity provider Netskope, said the generative AI boom is unique from past technological revolutions in how the pressure is coming from all directions, especially top-down. It’s also unique in its speed, broadness, potential for how AI products can be used for harm, and the rush of new regulations, which he said is “probably faster than we've seen in any other kind of technology adoption.”

“There’s innovation, there’s speed, and then there’s the rigor of the governance structures we have to adhere to,” he said.

At the same time, developing the technology was also an uphill battle. Just to integrate AI functions into the sticky notes on its virtual whiteboarding software, for example, Miro had to completely refactor its entire canvas — from how objects land on the canvas to adding metadata and enabling objects to broadcast to each other. Basically, they had to make the sticky notes, which previously operated like static objects, aware of and able to talk to each other.

“It was really challenging, the core technology, to get this done,” Chow said.

There’s one major technical hurdle every company tapping generative AI is facing: hallucinations. One company that’s attempted to measure how often large language models hallucinate (the cheeky term that’s stuck to describe how they fabricate information) put estimates at 

, depending on the model. Looking at specific domains, those numbers get even more concerning. A 

study published in the Cureus Journal of Medical Science

 that investigated the accuracy and authenticity of references in medical articles written by ChatGPT found that out of 115 references, 47% were fabricated and 46% were authentic but inaccurate. Only a meager 7% were both accurate and authentic. A 

 that looked at how leading models respond to legal queries found hallucination rates ranging from 69% to 88%.

“We're used to computers being accurate, but generative AI can hallucinate and make up answers. We’re used to computers being consistent, but generative AI has an element of randomness,” said Briana Brownell, founder and CEO of Pure Strategy, who helps companies develop their AI and data strategies.

For a company like Crunchbase, for example, nothing is more important than accuracy. As a go-to database for information about businesses and the latest venture capital deals, accurate information is quite literally what the company sells.

“We have about 75 million people coming who trust our data. We can't be wrong,” said Megh Gautam, the company’s chief product officer. “Hallucinations are just unacceptable.”

The company has still cautiously experimented with generative AI, using it to more efficiently write descriptions for new companies and surface new kinds of insights for customers. It’s also in the early stages of updating its search function to leverage AI. Still, the team cannot fully trust the AI and has to validate data before pushing it to the site, according to Gautam.

The most difficult part of the entire process, however, was figuring out how to communicate the new AI-powered features to users, Gautam said. In the case of offering a new data point, such as the new Growth Insight feature designed to indicate how much a specific company is growing, Crunchbase had to balance offering enough information needed to render trust without bombarding the user with an overwhelming amount of text. This included information about the new feature, the data sources, and how the growth number was determined. It was an exercise in bringing together a lot of moving pieces and impacted information architecture, UI, and UX.

While traditional computer programming is largely understood, so much about AI is still up in the air. Companies are left grappling with questions about what’s an acceptable use case for AI, how to test use cases, and whether to tap off-the-shelf models from companies like OpenAI or attempt to build their own. The rapidly evolving nature of the technology also poses major challenges in terms of how to develop without structurally locking yourself out of future progress.

“If there's an amazing breakthrough two months from now, we do not want to get backed into an architectural choice we made,” Gautam said.

Companies looking to integrate AI into their workflows are also having to navigate “

,” wherein providers overinflate the degree to which their offerings are actually based on AI to capitalize on the hype, as well as “AI sprawl.” In a 

, 71% of CIOs said they expect to adopt between 30 and 60 new apps in 2024, with 72% expressing concerns about sprawl. Tapping too many tools can increase the complexity of a company’s tech stack and open up new security risks, not to mention drive up the IT budget. The challenge comes from the bombardment of AI tools that are available, many with redundant capabilities, which makes it difficult to determine which ones to use. In the survey, 84% of CIOs said there are already too many AI tools available. Not only are the vendors that companies already work with rushing to adopt AI, but there are a slew of new offerings from startups, too.

Whether deploying AI into internal workflows or building products, cost is also a major factor. The costs associated with generative AI — from specialized GPUs and cloud resources to top talent and compliance work — can be astronomical. An increasing number of Wall Street analysts and Silicon Valley investors are beginning to sound the alarm that all the spending might be premature, 

 arguing it’s hype-driven and that there’s “little to show for” the huge amount of spending on generative AI.

“The big tech companies have no choice but to engage in the AI arms race right now given the hype around the space and FOMO,” wrote Jim Covello, head of global equity research at Goldman Sachs, in the paper, adding that “over-building things the world doesn’t have use for, or is not ready for, typically ends badly.”

When OpenAI released ChatGPT in November 2022, it launched a tidal wave toward generative AI. Leaders across sectors looked toward the problems it could solve and opportunities it could unlock, but many jumped on it simply because it was the hottest new tech in town — or because there was immense pressure to do so. Generative AI-products are still new and time will tell how successful they are, but across the board, tech leaders implementing AI stress that it has to offer value and not just be AI for AI’s sake.

“I have met zero customers who have said, ‘I wish I had more AI.’ But they have asked me, ‘Could it tell me more? Could it do more for me?’’’ Chow said.

Governance, regulations, hallucinations, complicated architectural challenges, AI sprawl, new cybersecurity vulnerabilities, and massive costs still represent just a snapshot of the hurdles companies are facing to embrace AI. There are companies getting creative to overcome them: Getty, for example, avoided the contentious copyright issues around text-to-image models by creating its own, trained only on data it owns outright. With such a wide range of challenges surrounding the technology, however, one thing is clear: AI has to be an all-hands-on-deck effort. 

“You have to bring your whole team into it. The product managers, the designers, the marketing team, engineers,” Chow said. “Everyone has to be part of the process, otherwise some process will break.”

As everyone rushes to integrate AI into their products and internal processes, it's a lot like building the plane while flying it.

The messy road to AI implementation

 receiving emotional support from a friend in the past week, compared to 2 in 5 women. Only half of these men also reported feeling satisfied in their friendships. And in 2023, the Surgeon General released an 

 to raise awareness of the nationwide “loneliness epidemic” and its dire implications on our physical health. 

Type “girlfriend” into OpenAI’s new 

 and the first three options all focus on emotional support: “a virtual girlfriend for casual supportive chats,” a “friendly supportive virtual girlfriend offering companionship and conversation,” and “your virtual companion for engaging chats and emotional support.”

With this clear deficit in emotional support, it’s no surprise that Americans are leveraging chatbots like 

 for companionship, friendship, and romance —  especially following the months of isolation throughout the COVID-19 pandemic. Sharing intimate moments largely over  Zoom, if at all, during that time has had inevitable long-term effects on social norms and health and wellness that we have barely begun to acknowledge or understand. Connecting with a chatbot is a low-stakes, low-investment interaction, but is there enough evidence to believe that it’s an acceptable substitute for connection with real people?

Michelle Mouhtis, a licensed therapist and relationship coach, believes it depends on the exact context in which people use AI. While she thinks it can be useful for elderly and homebound communities — or even prompting someone through a 

 while they wait for their therapist to get back to them — using it as a replacement or guide to human connection could prove detrimental.

And Sherry Turkle, American sociologist, author, researcher, and professor at MIT says “Some chatbot users say that a connection to their avatar is ‘practice’ for human connection. But when we talk about it, the conversation turns to how the chatbot is a release from people, a place for more validation and simpler exchanges.

“There is nothing about chatbot interactions that is ‘training’ for the complex and nuanced way that people communicate in relationships.”

According to Mark Minevich of Going Global Ventures, business leaders from every industry — in banking, travel, healthcare, and more — will look to adopt chatbots in 2024.

But much of this enthusiasm around AI comes from people who are either uninformed or who see a benefit in the technology's potential to replace human labor: 

, and even the workforce at large. But this overlooks everything about humans that isn’t quantifiable. As it stands, AI cannot be more than a supplement or tool of support and is something that needs to be closely monitored across all industries.

Using call centers as an example, Minevich says, “If you're emotional and you want to describe your problem — let’s say you lost a job or there's an issue with your bank account and you can't get to it — you cannot have that discussion [with a chatbot] in a way you could with humans.”

We’ve all been there, pressing ‘0’ on the line with the customer service menu trying to get a human to solve the problem at hand. Now, we stand to reach a chatbot or, as Minevich refers to them, a cognitive agent.

"There is no relationship with a chatbot. There is no empathy. When you turn away from the screen, the chatbot doesn’t care if you cook dinner or [die]."

“They can try to give you accurate information and try to be efficient, but I think humans are looking for empathy,” Minevich continues. “You don't know how those systems were designed, how they take your information, and how they score you. [They] are not necessarily dealing with your emotions as they're very bad at [it]. These cognitive agents have very limited — what they call in AI language — sentiment analysis.”

Truthfully, there’s also a large number of humans who struggle with sentiment analysis (the process of analyzing digital text to ascertain whether the emotional tone is positive, negative, or neutral). And, of course, not all emotions fit neatly into just one of three boxes. Arguably, most emotions don’t.

“We expect those agents to give us information that is accurate, transparent, and fair. And that's a lot to assume because the agents are written by data scientists — usually white males, usually having their own pathology — and they're not necessarily fair, accurate, or transparent. So we have to be very careful,” he adds.

If the current chatbots don’t even have the emotional capacity to be empathetic customer service agents, one has to assume — despite the countless Reddit posts debating the best AI girlfriend or the hours logged by AI girlfriend users — it’s currently impossible for them to replicate healthy, human relationships.

Perhaps what these users experience feels good only because they’re paying to manufacture the image of something they desire and haven’t yet been able to accomplish with another person.

“It justifies continual flight from people, conversation, learning the practices that make democracy work. It normalizes calling something that is not a relationship, a relationship. There is no relationship with a chatbot. There is no empathy,” Turkle says. “When you turn away from the screen, the chatbot doesn’t care if you cook dinner or [die].“

Yet we are learning to call what happens between people and the bot a relationship. We are learning to call what it puts out ‘empathy.’”

These “relationships” at their core are similar to that of an unhealthy human-to-human relationship and are built from an underdeveloped understanding of emotions.  “It's a human need to feel seen or heard. That is one of our core human needs,” Mouhtis says. “A chatbot reframes what you’ve shared with them and says it back. It ‘remembers’ everything you say and conflates compliance with ‘emotional support.’

“When you feel someone who is important to you, like a parent or a sibling or a close friend, isn't empathizing with you, seeing or hearing you in your pain and struggle, and they jump right to problem-solving, or they jump to one-upping you or making it about them, it can make you feel even more alone than when you picked up the phone to try and feel less alone,” Mouhtis explains. 

While the chatbot may talk the talk of a human, it's always going to feel artificial and forced (not all that different than someone spinning therapy-speak into something cold and 

). Participating in a relationship where core emotional needs can’t be functionally met and are unequivocally one-way can lead to a dependency or addiction-like response from the person who may continue to engage with an AI.

It’s also very common to try to seek — and fail to find — connection with the people we care about. “People have narcissistic parents, and they're continuing to interact with their parent hoping for an empathetic response, knowing well and good they're not going to get that response,” Mouhtis says. “It's human nature to want to connect and get the thing that they really need.”

Chatbots will, of course, keep engaging with no regard for or awareness of users' well-being because that is what they are built to do.

In the United States, AI regulations will largely be handled state-by-state and focus primarily on corporate and federal use cases. Whereas, in the EU, regulations are handled very differently. “The European Act is trying to find out what is risky in an AI system and the risks not only for the business, but for consumers,” Minevich says.

“The extensive model for clearance could be a product itself,” he notes. However, the system is so rigorous, that he also believes it’s limiting innovation and competition.

Even so, the mostly hands-off approach taken by the United States is a recipe for disaster. 

“There are two levels of impact," Turkle says on the dysfunctional social implications of this laissez-faire approach. "Social media algorithms try to secure engagement by getting people angry and then keeping them talking to equally enraged people. That is not teaching skills of listening, empathy, conversation, [or] respect. It is not teaching the skills you need for intimacy and connection.

"And then, AI comes in and offers pretend intimacy — programs that don’t just say ‘I intellectually understand,’ but that profess love and care. What is striking is that over time, and as our culture gets more used to talking to technology, pretend empathy becomes empathy enough.”

Despite listing numerous AI girlfriends, even OpenAI writes in its store’s usage policy “We also don’t allow GPTs dedicated to fostering romantic companionship or performing regulated activities.”

Going as far as to forge a one-sided romantic relationship with a bot is comparable to entering and remaining in an unhealthy relationship. “So, technology contributes to a problem that then, technology says it can cure,” Turkle says. That sounds a lot like an empty promise intended to keep users hooked. Do you really need to be treated like that by a machine?

Can companion chatbots actually serve as a replacement or guide to human connection?

Love bug: the limits of AI companionship

At Mercury, we believe the arts have more in common with business than meets the eye. The best artists and entrepreneurs build and iterate on things with skill, creativity, and care.

 — a tradition that dates back centuries and still manages to forge a connection between people.

So we searched high and low for potters who could make the perfect handmade ceramic tea cups for some of the entrepreneurs we’re lucky to have as customers. The nine companies represented in this project come from a diverse range of industries and stages — ecommerce to fintech, pre-launch to Series B. We spent months on the search, enlisting the help of “pottery consultant” 

 and scrolling endlessly through Instagram. Each potter has a brand or visual connection (sometimes both) to the company they’re creating for. And each potter grapples with the business of their pottery differently — as potter Kenny Sing says, “all artists, at least in the beginning, struggle to find the balance between how much it costs to make their art and how much to sell it for.”

is Mercury’s first pottery collection and we’re excited to share it with the world. Join us to get a handle on the shapes, ridges, and grooves shared by both founders and potters.

I'm focused on technology, people, and systems — that's the common thread. Mostly, I solve problems. I'm the most general definition of an engineer. I solve problems so that I don't have the problem afterwards.

 is on a mission to scale natural carbon capture through reforestation. They’re aiding with everything from 

 that help organizations track reforestation efforts.

When asked about his day-to-day work, founder Yishan Wong says that tackling climate change is the largest problem humans have had to solve. The scale of this problem influences how he runs his business. “Half of my job is putting the right people in the right jobs and the other half is getting lots of data to make decisions,” he says.

 completed the tea cup for Terraformation, using a mixture of both wild and reclaimed clay (versus synthetic or unrecycled.) She incorporated an illustration of 

, one of the plants that Terraformation grows on their pilot site in Hawaii, on her 

 style tea cup. Amanda’s love of pottery comes from her love of food, which she considers the primordial source of connection between humans and the environment.

Making and using handmade pottery is the medium I've chosen for talking about the connection humans have with our natural environment. Pottery can say a million, zillion things. Pottery can sometimes get roped into the craft field instead of the art field. But I don't think that line actually exists.

I got into technology as a way of leveraging impact. I've always been less interested in software on a theoretical level. I wanted to build stuff. It became a form of self-expression. I didn't expect that I would actually be a founder; in some ways, that was an accident.

Founded by Siqi Chen and Arya Asemanfar, 

 wants to give every startup leader access to financial data and insights. According to Siqi, the obfuscation of finances is “a grand conspiracy” that makes it hard for regular employees to understand business finances.

When Siqi started his first company, he kept a job at the time to pay the bills. “Frankly, the things I've made often haven’t been commercially popular or long-lasting because I build for myself,” he says. Today he is an investor in several companies, including 

, and continues to find ways to give back to the founder community.

’s teacup shows the Runway logo — a natural fit because his pottery often involves bright colorblock — and features his signature, two-finger handle (which he continues to improve on). His inspiration comes from his father, an artist whose postmodern paintings were very “clean,” “geometric,” and “sculptural.” When asked if he considers himself a founder, he says that “this, for me, really came out of personal enjoyment. I had to find a way to keep doing it and to generate enough income from it, so that I could quit my other job.”

Even now, thirty years later, I look at my work and see things that bother me. It tells me what I want to try next week. My handles are still changing. I love the fact that I can come into the studio and spend the time making whatever I feel like making.

I want creators to be able to have full control over their work and the tools to make it happen. NFTs open up avenues for creators to benefit from their work directly through royalties.

Founded by Ilya Komolkin, Alexei Falin, and Alex Salnikov, 

 is an NFT marketplace that has over 2M users and saw over 

Salnikov believes that NFTs and web3 center openness and community, so his team prioritizes community-friendly features. For instance, Rarible’s multi-chain 

 make it easier for users to connect with one another. One of their goals this year is to enable communities to have their own, tailor-made platforms for NFT trading.

 worked on the Rarible teacup and saucer set. Sing often merges tech and art and likes that his work can be experienced both physically (as a usable, handheld cup) and digitally (as pictures and videos). He runs 

 and many of his works feature animation which he crafts with the help of software. The block imagery on the saucer and the chain shape of the animation are a nod to the blockchain technology behind NFTs.

All art is expensive to do, which is why you see a lot of entrepreneurs in any art. A lot of people fall in love with their art, and to sustain it, you have to figure out how to sell it. You can only give your mom so many free pieces.

Mercury’s first pottery collection challenges potters to zero in on an unlikely form of inspiration: the intrepid startup.

Startups through the hands of potters

. Mercury offers banking* for startups — at any size or stage. Founders can access banking, credit cards, treasury, venture debt, and more, and manage their businesses with confidence. Launched in 2019, Mercury is trusted by more than 100,000 startups. 

*Mercury is a financial technology company, not a bank. Banking services provided by Choice Financial Group and Evolve Bank & Trust, Members FDIC.

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