In this episode, I speak with Bei Zhang, VP of Growth at Tanka, about the company’s mission to empower AI-native founders. The conversation covers why persistent, organization-wide memory is the missing ingredient for truly proactive agents, how Tanka stitches together chat, email, calendars, and documents into a single “remembering” teammate, and what agentic work could look like over the next 12 to 18 months. We also take a closer look at the future of founding teams and how agent tools can enable a super-individual way of working without losing control, auditability, or taste.

Tanka sits inside a three-layer stack incubated by Shanda Group. EverMind is the AI infrastructure arm that builds a long-form memory orchestration platform. MiroMind is the research lab, built on Qwen models, focused on long-term memory and reasoning. Tanka is the consumer-facing agentic workspace that applies those capabilities to help startup founders run their day-to-day.

All three were incubated by the family office of Tianqiao Chen, the Chinese internet entrepreneur and investor behind Shanda.

In today’s world, there’s no shortage of information. Knowledge is abundant, perspectives are everywhere. But true insight doesn’t come from access alone—it comes from differentiated understanding. It’s the ability to piece together scattered signals, cut through the noise and clutter, and form a clear, original perspective on a situation, a trend, a business, or a person. That’s what makes understanding powerful.

Every episode, I bring in a guest with a unique point of view on a critical matter, phenomenon, or business trend—someone who can help us see things differently.

For more information on the podcast series, see here.

Topics we covered:

* Tanka’s Mission: to empower future AI-native founders to transform their ideas into successful businesses swiftly and efficiently.

* The Problem Tanka Aims to Solve: Founders often struggle with information overload, with critical insights scattered across various platforms such as Slack, Google Drive, and numerous AI tools.

* How Tanka Works: Tanka’s unique AI memory framework.

* The Team: Tanka’s diverse team is rooted in the heart of Silicon Valley, comprising individuals with rich backgrounds in big tech and startups.

* Competition is not with general agents—focused and niche market.

* Connecting Founders with Investors: It actively seeks to connect founders to investors, creating a community and offering consulting services as well.

* Risks of Using AI agents: Human quality control remains essential; a hybrid model is a sustainable, long-term work model.

AI-generated transcript

Grace Shao (00:00)

Hi, Bei thank you so much for joining us today. I understand you lead Tanka’s growth right now. It’s a very, very exciting startup. I’ve heard a lot about it. Why don’t we start with your role and just tell us about the company, Tanka, the founding mission, what problem you guys are trying to solve, and just a bit about the team.

Bei (00:16)

Sounds good. Sounds good. Hi, Grace. Thank you for having me. Hi, everyone. My name is Bei. ⁓ I lead the product and growth in Tanka. Before I joined Tanka, I had been in various roles in different AI and SaaS companies, mostly in the GTM function. So what Tanka is about? Tanka is on a mission to empower the future AI native founders to go from ideas to founded very fast, very efficiently. And the core technology we’re putting behind the Tanka is the long-term memory behind the agents. End of the day, we’re trying to create a proactive companion, or we say that AI co-founder, because compared to typical AI chatbots, we are putting more power behind our AI agents that can remember all the conversation, remember all the relationship, and eventually can be the proactive.

AI partner to propel the founder to move as fast as possible. So that’s essentially our mission. And we’re hoping we believe the future is the world of super individuals and the lean teams. We’re trying to make the Tanka to be the powerful operating system for the future startups.

Grace Shao (01:25)

So I think there’s some fun and irony in that, right? what does it mean really when you say it’s an AI co-founder? Like for someone like myself, I’m a independent or I would say like a founder of a startup, I have a small team. What is Tanka really helping me do in a very practical sense?

Bei (01:43)

Yeah, yeah, great question. We are essentially the kind of startup, so we help ourselves, right? We’re trying to leverage the resources to help others too. what it would mean, maybe we’ll take a step back to get down to the problems we’re trying to solve. Essentially, we being the center of the Silicon Valley, we’ve been hanging out with a lot of founders, or a lot of individual, a lot of lean teams, a lot of them are just like you, Grace. ⁓ You are a super individual. We also have friends being just a three to five person teams. And the common problem we’re seeing they’re facing is the highly scattered information, overload of information, a bloat of different AI tools, and a very spread of key knowledge across different platforms. So even though we’re saying we’re putting the many of the platforms are wonderful. You got all the nice conversations on the Slack. You got all your documents in Notion and the Google Drive. And there are some offline chats. I’m sure there are valuable informations embedded into various GPT tools or AI chatbots. So the core challenge is not having the right tool. The core challenge is when founders are all of a sudden going from a single threat, trying to take on the world, trying to build a business, the tendency is that there is overloading of the information from all kinds of directions. Because for example, we’ve had a very good friend being a very technical researcher in Stanford. But the moment when he or she step into the founder role, he or she will have to handle not only the product, but also engineer the sales, the marketing, the product dev, legal and tax and BD, right? All kinds of stuff going on. So essentially having all those information scattered in different places create a few effects. Number one, it create a huge overload on the human brain, right? Nobody can process the information so effectively. Especially, we even come across multiple founders doing multitasking because they are trying different ideas, right? Which they will just multiply the pins. And separately, when the brain is overloaded, it instantly distracts the founder from the core duty, which is building the product. So that is causing many problems to be happening. It is causing key information getting lost. It is causing one part of the valuable information not necessarily getting fit into the other nice tools or very powerful AI agents, so the outcome isn’t as optimal. It’s far from it, right? The outcome is far from optimal when they’re trying to make a progress on the project. So that’s the mission. That’s what we’re trying to solve in Tanka. So in Tanka, here are a few things we’re trying to tackle the problem. Number one is the AI memory. Without putting the fancy word out here, just thinking...

As of you have, let’s say, today, whichever, most of the AI tools are not really memorizing your conversations. Because when you open a window, it has a conversation with you. But the moment you close the session, it doesn’t really record anything. So the next conversation is new. So with the 10Cut AI memory framework, all the conversations and all the documents you put in the tool are automatically compressed, stored properly, and also stored with a high fidelity so that when you have a conversation once, the future conversation will always remember what you had before. So it put a piece of mind to founder’s head so that you know there is a trusted partner that never forgets anything. So every company is about moving forward, not to remember what happened in the past. So on top of that, we’re adding the connectors, making sure Tanka can digest information not only happening within Tanka, but also connected from other sources as a deep memory and context. And with the memory, we’re able to put in the right AI agents, whether to produce the business plan, whether to just do the deep thinking and a deep conversation, or whether to produce an investor-ready pitch deck.

They are all based on the actual information in greater details, without you having to chase across all different things. So that’s what we say. That’s the actual specifics we’re putting in behind the tanker, because we’re not calling that just, we want to go beyond the typical AI assistant, because when we say AI assistant, meaning there is some, it’s a reactive, right? There is a AI sitting there and waiting for me to ask the questions or waiting for me to give the proper prompt. So we almost have to treat the typical, even for the very powerful AI chat bot, we have to carefully curate. We have to carefully protect the conversation, making sure it doesn’t generate anything wrong because garbage in, garbage out principle. But with Tanka, because the more you work with Tanka, the more Tanka knows about you, we almost can forget about prompting. It is an actually intelligent person sitting right next to you as a founder. So whenever the conversation happens, we just keep marching forward. And we’re even building more of a proactive AI functions because now that Tanka knows everything, what do we have happening in theory? You should know what I need to do next. even before, in theory, even before I ask,

Tanka to do anything, there should be more proactive actions. For example, hey, I need to follow up with certain investors. I need to update the pitch deck, for instance. Some of them are already realized, and many are definitely on the road as we speak. But that’s what we mean by AI co-founder, because we want to essentially have an AI that can essentially propel you to go forward instead of just waiting there for you to comment the way I do things for you.

Grace Shao (07:43)

That’s super interesting. think to me, when I heard that, I was like, that’s going to be so helpful for me. Cause like you said, there’s so many to do things on the to do list every morning. And then if someone’s actually proactively reminding me or getting things done, that would be really helpful. I first want to talk about the team.

First before we get into the product. Just like I understand you guys have a pretty diverse team. A lot of you guys, ⁓ including your founder, came from even ex big tech. How did your team come together? What’s the background? And I guess what is your edge right now making an agentic tool like this, especially with a lot of even the big AI labs are pushing out agentic tools. Like what is your niche and edge?

Bei (08:05)

Yeah, yeah, great question. So you’re right, we’re a very diverse team. We’re headquartered in Redwood City, California. We do have a global team across different parts of the world. But the core leadership and the product team are right here located in the center of the Silicon Valley because we are a company building for the founders. We want to be where our customers are to shine light on a few other things you covered. When Tanka was born, essentially it was born within a family office that has been actively curating multiple companies and also has been actively investing in hundreds of early stage startups. All the memory problems and all the context switching, all the information overload are very much experienced firsthand, both for the funding members within the family office and also being well observed by the company, right? The family office has been investing and curating in. So it’s a common problem that hasn’t found a solution yet. So that’s where I would say one of the edge is our deep understanding.

We’re not an enterprise tool and we’re not so much to a pure consumer tool. We’re living in a breathing in the startup world because the people has been working in the company or surrounding the company has either been advisors, investors, ex-founders of this kind of startups. So we know the problem from a different angles. So that’s number one. And number two is you’re absolutely right, the CEO, Kisson.

She came from a Meta, from TikTok. So definitely had a good discipline and a very structured approach from well-formed companies. She also co-founded another company that has a similar form of Tanka. So she brought in tremendous discipline in both the AI agent and from 0 to 1 and from 1 to 100 scale.

And I personally come from Grammarly. I happened to have an experienced growing company at a scale and also helped establish the B2B function from the beginning. And other than that, we do have ⁓ members coming from various startups. So we have all been experiencing the problem, first hand, left hand, right? So that gives us a deep understanding on what we want to solve for ourselves.

Grace Shao (10:53)

But $29 a month is quite steep, let’s be honest, especially if founders are cost-conscious. I want to understand what was the thinking behind that. And again, how does it compare with peers, even more general AI tools like Manus coming out of Singapore right now, obviously, as well as the incumbents that have been integrating AI into their apps like Slack, Salesforce, Microsoft Teams, even Zoom AI companion, right? Like in some capacity, they’re all trying to become a more proactive, I guess, whether you can call it a co-founder or a colleague per se, they’re all trying to be there to be more present to help you actually get things done, right? How do you compete with such an array of competitors, essentially?

Bei (11:36)

Yeah, yeah, good question. So to your first question about pricing, we put out a pricing more to create ⁓ a sense of familiarity to begin with. So purely on the number, I think it’s a mid-tier. It’s not that high. It’s not that low either. But it’s something people can, our users can correlate to.

And if you look at our free tier, we actually have a pretty generous free tier. We have daily bonuses. I think for lot of users to get a feeling, the free tier actually can get a lot done to truly feel the memory behind the agents. And also, separately, we’re paying much less attention on the pricing versus our attention on the value.

Because at end of the day, what our users weigh in is how much benefits, how much value they are getting out of the tool. So we’re just putting the pricing as a stake in the ground. We’ve been doubling down on understanding what our users need. They need a collaboration, so we built the AI agents in the chat to empower the team.

They need a generative function to turn the conversation into the actual shareable documents. So we did that. We made a very smooth process to go from the chats and the team conversation into the outcomes without you having to reprompt. The users are also looking for more help in the fundraisin, related features just so when they are ready for investor conversation, they can get it funded faster. So we have a whole pipeline of efforts to empower the founders to realize the benefits. in that, our goal is to make everyone feel like the price is a huge bargain. So that’s something we’ve been actively validating. And also separately, to your point, there are it’s an agentic world, right? Everyone, every company, whether the big ones or whether the startups are making various kind of AI agents. We do keep an eye on a lot of the big names, like you mentioned. I do have a lot of admirations to the great tools. But at this point, our belief is that in this age, the AI tool will come out in different formats and different forms.

So I like to think of them as inspirations and role models, right? More so than the competitions. If they are doing something similar, right? We would say, how can we fill our own gaps, right? How can we do better than them? But often than not, actually have way more gaps. We think even this big names are not even addressing between on the path, right? Between the ideas to startups getting funded. So we’re hyper focusing on filling the gaps more so than worry about the competition. Because we believe the world is big. The world is big. In the future, everyone will be a builder. Everyone will be a founder. If a user don’t use us, it will not be because of a competition. It will be because we’re not delivering our promise and not creating the value for the users. So that’s where our minds are, mainly.

Grace Shao (14:46)

So instead of trying to compete on distribution reach right now, you’re really focused on serving a very niche kind of audience, right? And then really just delivering exactly what they need instead of a general mass audience.

Bei (14:56)

That’s correct. We’re not trying to build a tool for everyone. That’s the job for the big tech. That’s the job for Tech GPT and Cloud. We are in the center of the Silicon Valley. We are hanging out with all the founders who are using all the tools you’re mentioning, but are still struggling in pushing the ideas into tangible business plan. And even for serious entrepreneurs, they are very struggling in getting connecting to the right investors and getting funded very efficiently. So we’re just hyper-focusing on this persona. Because again, we deeply emphasize wisdom because we are them. So if we get this part of the job done, we’ll be very proud of this. We’ll be very proud of our efforts.

Grace Shao (15:40)

Actually, one thing you just mentioned, how do you connect these founders with investors? What’s the strategy there? Because that’s not a product strategy. Is that just your connection, your network?

Bei (15:50)

More so than that. So there are multiple approaches. ⁓ number, think about this in a few different approaches. So number one, this is actually interesting challenge because our founder friends are, most of our founder friends are struggling looking for investors and most of our investor friends are still struggling and looking for quality startups, even though they might be in the same room. So that’s still a ⁓ friction. we tackle this in a few different layers. So many of the founders are not effectively connecting to the investors because they’re not ready. They’re not ready. first, we want to make sure Tanka has the capability for them to chat with the team, for them to carry through all the conversations, and making sure all the minute details are reflected in the business plan and the pitch deck so they appear. They are more buttoned up.

So that’s where we do the effort in preparing them to be investor ready, because investors are ready in the other end of the room. So that’s low-hanging fruit. And then separately, we are very active in the Bay Area funder communities. ⁓ So if anything, we have no lack of is there is an abundant funder communities here in the valley.

And we’ve been actively in the community facilitating the conversation. We’re inviting investors to give advice on how we can build a tool to better empower the founders. We’re doing this in different directions. So in a way, by having a presence in such communities, we’re already acting as a connector between the two parties. And furthermore, what do we do have on the product roadmap. our features like investor database and the investor matching, because that’s a low-hanging fruit. We do want to provide the founders more value by making it very easy for them to see that based on their business plan and the sector, who might be the right person they should be talking to. we are also evaluating the options such as the data room analyzer or the even warm intros because we’re even discussing with the actual human expert fundraising agencies as a potential layer because we do believe this is, AI is not ready to take over the world yet, As awesome as AI can ever be, humans do bring tremendous amount of value. So on a needed basis, there needs to be a human layer on top of the AI workflow.

And even if the human layer just evolved for 10 % of the time, we believe that’s where potentially the 90 % of the value may come from. So this is where end of the day we foresee we likely will build ourself into a hybrid solution where 90 % are conducted by the AI or focusing on this path addressing the problems many of the tools are really not addressing specifically.

And we’re connecting the human brain, the different part of the party much, much closer in solving this problem. So yeah, does that make sense?

Grace Shao (18:58)

And you know where else founders should be talking? They should be talking on my podcast because that’s where investors are listening as well and media is listening. And that’s how you get your story out there as well.

Bei (19:07)

They should. Investors should be listening, too.

Grace Shao (19:14)

Investors are listening. Actually, my main audience are investors in the US and Europe. I think, you know, interesting founders should be DMing me now. But on a more serious note, I think you just talked about like, agents can do what 90 % of work, you still got to have 10 % of human quality control, right? So end of the day, what are things at least at this point, or the next, say, 12 months, we can delegate agents, what are things that we still really need that human touch or humanity to kind of guardrail, the kind of progression of technology or our workflow or the usage of AI.

Bei (19:49)

Yeah, we’ve been thinking this day in and day out. So definitely when it’s related to the information gathering, information collecting, the document generation, document refinement, and web scraping. So without saying the features, that basically meaning how you turn from your conversations and inputs, documents, team chats into the pitch deck, into their data room documents, and how to scrape online, how to go to the linking. Those delegatable missions, those missions that tend to be competitive but yet time consuming. If it’s a delegatable, if you can put into a ⁓ SOP or standard operating procedure, we should try our best to let AI to do this as much as possible.

However, we do acknowledge that sometimes it takes a lot of judgment in this process because when the funders are so early, would the investors invest into the project or are they investing into the persons? Most likely, earlier they are, the earlier the investors are putting their weight on the persons. But many of the persons’ attributes and experience are not quantifiable.

So there are certain things that I cannot build into the AI agents to automate everything. So that’s where we do need a human to better probably connecting with the macro, better putting in the latest reflections, and better just to step in, making sure we’re not misjudging certain startups in either of the directions. And also separately, I would say, we also, Even with all the AI tools out there, we also had very, very top-notch founders who are deeply in the research world. So they just don’t have time. They are very busy. They do want to focus on building their product. Can they learn how to do the whole fundraising business plan or so? They surely can. But it’s more valuable for them to focus on what they do best.

That’s where I think sometimes often it just makes sense for the human layer to just step in and take it over. And it could also be entirely 100 % human touch, which could be well suited for the situation. But just wanting to make it possible whether the human touch is 0 % or 10 % or 100%, it is how this startup works. And we should build our product to be seamlessly connected and adapted to the reality here.

Grace Shao (22:22)

And I think it’s important to kind of note, like, you know, as you mentioned, as we’re all hyping up the AI agents right now, there is some mindfulness to be said to have to about the potential risks, right? So when people are using AI agents, I think this is as an AI agent question as a whole, not just Tanka but who audits the process ensures there are no mistakes, right? When the machines are starting to complete tasks, how do we actually ensure or how do we human ensure that we minimize the mistakes and the risks that they may come with.

Bei (22:55)

Yeah, it’s increasingly a more critical question as the adoption rate for the AI are increasing. So I don’t have a perfect answer. I don’t think anyone has really found the answer yet. I would say it’s the process. Process meaning when we’re building the product, because we’re building Tanka to be very deep thinking, deep researching, and working on very, very serious projects.

We try to use our best model, most expensive one that does the deep thinking and the reasoning to the best extent. So we don’t try to save money using the cheaper model for faster speed, ⁓ which might be introducing more errors. We’re carefully balancing that. We would rather deliver higher quality at a higher cost, but for higher quality. So that’s number one.

And number two is because that’s really actually where the memory comes in. Whenever we build a 10-cut AI to help brainstorm with the founders on next steps, we make sure it all ties back to the prior memory or it ties back to the traceable sources. for all the conversation and the generations, there is a link back to where you can point out to.

But that being said, it’s not 100%. It’s not like we can disregard any human efforts not to look closely. We still are constantly calibrating, and sometimes errors happen. And that’s even because the LLM, sometimes because the core server, it has variations. Maybe a question from the same LLM vendor may generate different answers.

One is more correct than the other one. So I would say it takes both efforts, even though that’s why we do want to emphasize the value of the human, because here’s AI. And we as a human, we still need to be very carefully guarding our own outcome. And then we introduce the human expert to further enhance the quality. I mentioned a lot of fundraising, and we actually have a lot of friends and mentors and advisors from other areas, such as sales and go to market, tax and legal, who are actually ready to engage and looking to find ways to help out the founders. So we’re not building us as a marketplace yet, but essentially we do want to, our vision is we do want to make a Tanka to be the center console where the founders work with Tanka, but also using other tools where it applies. We’re not here to replace anyone.

And we would definitely encourage or we may build a bridge between the Tanka with the human experts so that the human and the AI and human harmonically work together to further minimize the hallucination and the errors.

Grace Shao (25:40)

That’s really interesting. didn’t realize it’s kind of like building up an in-house incubator or like a consultancy, right? Like you have Tanka as your main touch point, and then you expand into your human expertise. Actually on the technicalities, I want to ask what models are you using and how is that decided by the agent? What I put in a prom when I’m using your agent, how does the backend look?

Bei (26:00)

Yeah, so I’ll say, maybe without disclosing a specific model, we do use a combination of the top tier models. maybe that’s the best way to say it. Using the AI memory, I was too aspect. The AI memory layer is built a little differently. It is called EverMind. It’s actually went open source a few days back. So we built our own prior proprietary and memory layer using a set of the algorithm. And that’s one. And when we build our Tanka AI agents, we do have a router option. We do build a AI. We do have a few preset prompt. Whereas depending on the type of the questions and depending on how the different steps of the agents that can execute, you will automatically pick the best model for the task. So it’s not just one, one deal, right? The kind of large language model will vary. It depends on whether you’re asking to generate a rough idea or whether you’re generating a very buttoned up business plan. So it’s different. And separately, I do want to say because of the memory, that’s where things are a little different, right? So because we do have the AI memory,

The large language model is capable of working with ever evolving context and the memory. So even the same question would absolutely yell the different answer the more you engage, the more you evolve with the AI. So I would say the LLM is a commodity. They’re very powerful. They are the necessity. But that’s where at the end of the day, we do think it’s probably going to be safe, whether you’re using Google or OpenAI or Cloud. At some point, it’s going to be indifferentiable, So that’s where, how to make sure it works for you, right? Not for a general purpose. It’s more critical.

Grace Shao (27:49)

That’s interesting. think that’s what a lot of the AI agents companies been saying as well. Like eventually, you know, the user experience will not, the users will not be able to actually differentiate which model they’re using, but it’s really just on how the interface interacts with the user and if it’s for a specific task. So I kind of want to go in on the product itself. Walk us through the product surface. Like, what is the experience like when I’m a user, I’m a founder, when I go on Tanka what should I expect?

Bei (28:16)

Yeah, we put in so much sense into the product, but if we, let’s say, we simplify, as a founder, you go into the Tanka, first of all, there is a place you can work with Tanka agent one by one basis. So on this cases, it’s essentially not too crazy different compared to the other AI agents out there, right? You still interact with the agent, you still ask all the questions, right?

Further develop your initial idea into a very buttoned up plan and further refining and fine tuning on that. Again, the main differentiation is ⁓ our window never closes. Our window stays always on and never worry about missing any information. So that’s the one. And then let’s say you as a founder, you get an idea from ⁓ a raw impression into something more tangible, you need to work with your team, right? And if today, whether the team is your co-founder, or whether it is your friend or your son-in-law, right, advisor, there needs to be a joint effort because often the wisdom come up in the conversations, right? So that’s why we have a second portion of the tanker to be a chat, right? Whereas we, whoever you invite into the tank to discuss the ideas, to hear the feedbacks, whether positive ones or constructive ones, and whether you both share or you all share any external references. All those conversations are precisely memorized and processed to be the high definition by the AI agent. then when

That’s essentially where ideally your business plan will evolve from your own work. And with the other AI agents, you would have to reprocess the information. You will have to bring all this conversation into a prompting and making sure, let’s say, that GBT understands what you have talked about. But it was tank up because the AI is sitting there. The AI is sitting there with you in the conversation. After you finish the conversation, after you are aligned,

You and your partner or your mentor are aligned on certain solution. Well, you can simply tell the tech to say, go make the next version. In that case, there is no transfer of information. And then there is no loss of communication in between. So that’s the next step, because we see the collaboration being a very core part of the founder. Very few people can pull off the one person team, even for one person, assume, right? You as a super individual, you probably collaborated with many, right? To develop your own business, right? And the last but not least is we are building Tankard to be a very open platform, right? Because this is where we fully acknowledge that everyone will probably use some other tools, whether it’s Slack, right? Whether it is Minos, right? My favorite tool. Again, we’re not trying to compete, right? We’re trying to say, if those critical contacts happen in other platforms, we want to make sure there is a way to bring those contacts into the Tanka so Tanka agent can sync with more deeper memory in mind and thus generate more high quality contents, right? And then the other direction is also true because we actually keep the memory well organized. If at some point the organization or the startup outgrow the Tanka capability, and we are building the MCP to make sure all the memories are exportable to the next tools you’re trying to use. So we are here for the specific purpose. And then there is a beginning point and there is an end point. We’re not trying to do everything. Again, we try to do the best in the part of the problem we’re trying to solve.

Grace Shao (31:59)

That’s super interesting. I was just going to ask you, where do you think founders can outgrow Tanka? Because you’ve been really focused on saying, helping them out in the very early stages. it’s interesting that you’re quite mindful that eventually, if a company grows to certain size, there is potential that the company or the founder himself might outgrow your app and they will move on to the next agent, next tool. I guess on that note, I kind of want to end on a big picture question, which is,

Bei (32:05)

So yeah.

Grace Shao (32:24)

What do you think is the future of work for knowledge workers, especially startup founders, what you’re witnessing in Silicon Valley? I think you alluded to this a little bit, that there are more and more of these called super power or super one-person bands, whatever. But what should we expect? Are we still going to see the kind of startups of couple of people with different technical skills kind of coming together, founding a company, to scaling it?

Bei (32:38)

Yeah.

Grace Shao (32:50)

and then becoming a big corporation or are we going to see complete that mode, complete transition revolve.

Bei (32:56)

Yeah, it’s a loaded question. again, we’ve been very actively thinking along the lines of that too. So here are a few things we believe the future will evolve to. Well, there definitely will be big organizations. That’s just the case. Some businesses are better to be at a bigger scale. Let’s say if you build a robot company, you better be. You need a scale. However, we do see that with all the tools empowering people to go from ideas to the apps very quickly, we definitely see there will be exponentially more super individuals. And when we say individuals, it means either one person or either three to five person. Because eventually, everyone, we do see the traditional roles being very blurred, right? There will no longer be like a PM or front end or back end or marketer, right? Essentially one person likely that’s gonna pick up multiple roles, right? I assume, Grace, you probably were many, many roles at the same time as the owner yourself. I think that’s incredible. And then there will definitely, many of the businesses don’t have to be that big. We do see many companies will probably stay it’s pretty small, right, 5 % or 10%. For instance, Gamma achieved a $2 billion valuation at 50%. That’s incredible. And I think there will be more and more companies like that. So that’s what we are inspired to solve for them. And also, adding one more thing is we do think the future collaboration will be multi to multi, right? That meaning is no longer going to be one person being employed by one company for a long time. Because hey, when everyone can do so many things, if that person has a capacity, why couldn’t he work on multiple projects with multiple teams? That’s also where we are creating the tank to be not constrained by an entity. You don’t have to be the same entity because we fully expect anyone can work with anyone. And we want to embrace that and empower that too. And last but not least, again, there are many good thoughts. I think it’s probably a book worthy if we had more time. So I do think this is where, for the first time, in AI can, in the past, in order to value whether the workforce or organization, whether it’s effective, you kind of have to wait until the quarter end or year end to see the outcome, to see that. Because many of the information are not really recorded. But now, because everyone used so many tools, and also AI has a memory, and AI can understand how things work, I think the efficiency will be exploding. Because the AI is able to catch where the inefficiency, where the blocker is happening. That’s also, again, that’s why we built AI to be not just one-on-one, but to be in the team, just so AI can discover, right? It can observe what’s working, what’s not working, and making sure that the team always work. Whether your own team or whether the cross-functional team is always in optimal status before too late to essentially the performance review happening every second. So that’s also back to the super individuals, right? The super individuals can measure their own success in real time and furthermore be more successful.

Grace Shao (36:21)

All right, Bei, we’ve had a wonderful conversation. I have one last question for you, which is a question I ask every single guest that comes on my show. What is one differentiated view you have or something unique you believe in about the industry, about the future of tech and AI, or even something just in general in life?

Bei (36:37)

Let’s see, I have a couple, but I’ll pick one. even as an agent, we’re probably, I think it ties back to our conversation today, right? We are building, I am actively building the AI product, right? So we want to build a co-founder or even a super powered AI solutions. But I do want to acknowledge that the penetration and adoption of the AI in the real world is so, so low. And chasing after a technical advantage, going after, I think ⁓ sometimes it’s just almost a wrong direction for builders to say, let’s make this PowerPoint generation even more smoother or nicer. And while ignoring that, there are massive amount of human workforce are not even closely in leveraging even basic AI to do things. They are struggling with the basic data scraping. They are suffering with the basic information gathering and to be truly embedded in their workflow. This is actually tied back to our chat in the whole fundraising journey. I we’re talking about the most capable, the smartest, the bravest, the most ambitious founder who can build everything. But I mean, why are they still struggling in knowing where to find all the investors? Who is the right investor to work with? Am I ready for the investor conversation? What else do I need to prepare? How good is good enough? And what to anticipate?

Why there are so many basic questions that are not solved. Sometimes I think it’s a, I don’t know whether it’s a differentiator. I just want to, we are doing practicing ourselves. Sometimes we try not to be ⁓ bad at in how we can build this tool to be better than the other competitors. But we go back to the basis on what problem are we solving? How is the problem, how people are tackling the problem today and how we can leverage the technology to best solve the problem. Because I definitely observe when we go chase after the technology advancement, we’re going after like 0.1 % improvements. But when we go back to the basic problem resolution, when we look at how the real world is being operated, we’re looking at like 90 % of the problem are not even remotely empowered. So that’s where I’d like to put out here.

There is still a long way to go. And there are so many things to be built. So I’m very excited about the journey and all the possibility and all the value we can bring to the community.

Grace Shao (39:11)

Thank you, Bei. That’s really thoughtful. And I think that you do highlight a point where I think when we’re all so embedded in the tech and AI scene, we assume people are all adapting and adopting it. But to your point, actually, the general mass is really not up to speed with it. And there’s so much work that needs to be done in terms of educating them and actually working together and actually a lot of issues are not solvable by technology, but it still requires that human expertise. So really appreciate that. Thank you so much for your time today. Is there anything else you would like to share with us before we hop off?

Bei (39:44)

Well, first of all, thank you for the time. I love all the very thoughtful questions. It’s been a pleasure chatting with you and I’m grateful for the opportunity to organize the mind and the share with you and your audience as well. The last thing will be just any recommendation, any suggestions is welcome from you. I I hope this is a...

This is the start of the conversation, more so than the end of the conversation. And again, you’ve been a super individual. I want this product to be helpful for you. And also, I would love this product to be helpful for your audience, whether they are investors or they are founders. So I’m just putting, I’m definitely very, very open. We’re a sponge. We’re a sponge. We’re here to take on any suggestions or feedbacks and that’s the only way we can get better and really focus on the right problem to solve and we need everyone’s help. So thank you, thank you, Grace and thank everyone in advance for all the nice thoughts.

Get full access to AI Proem at aiproem.substack.com/subscribe

Unlocking the Future of Startups and Super Individuals with Bei Zhang

AI Proem Podcast

Unlocking the Future of Startups and Super Individuals with Bei Zhang