S1|Ep91 Hidden Venture Capital Trends: Redefining Fundraising & Investment Strategies in the AI Era

My amazing guest today is Jeffrey Fidelman from New York, United States. Jeffrey brings deep expertise in investment banking and startup growth, and we’re diving into The Hidden Venture Capital Trends: Redefining Fundraising & Investment Strategies in the AI Era. 

Recently Jeffrey was elected to the Board of Directors of Harvard Alumni Entrepreneurs (HAE), a global community of alumni founders, investors, and executives.

Jeffrey is also a part of the Diamond Executive Group of the AI Game Changers Club - an elite tribe of visionary leaders redefining the rules and shaping the future of human–AI synergy.

Jeffrey is the Founder and Managing Partner of Fidelman & Company, a leading advisory firm launched in 2015 to address the critical fundraising and growth strategy needs of early- to mid-stage companies. 

Jeffrey’s career began in residential real estate, which led him to the Portfolio Manager role at Morgan Stanley, followed up by the role of Vice President at HSBC, driving revenue strategy for Manhattan. 

As a seasoned entrepreneur, investor, and advisor with deep expertise in finance and fundraising strategy, Jeffrey has built and supported ventures across multiple industries, globally. 

🔑 Key topics discussed:

• Hidden venture capital trends and emerging blind spots founders and investors overlook
• How AI-driven data, platforms, and automation are transforming capital allocation
• Disruption ahead: which parts of the venture ecosystem will be reshaped first by AI
• Why traditional investor evaluation methods become liabilities in an AI-powered landscape
• Strategic advantages for founders and investors who adopt AI early
• The new economics of AI efficiency and its impact on valuations, capital needs, and startup strategy
• Global competition: how AI might rebalance or reinforce power in major venture hubs
• What investors and business owners must unlearn to thrive in AI-powered fundraising
• Essential leadership guidance for navigating the next wave of AI transformation

🔗 Connect with Jeffrey on LinkedIn: https://www.linkedin.com/in/jeffreyfidelman/
🌏 https://fidelmanco.com/

Chapters

• 0:00 Welcome And Guest Background
• 3:20 From Banking To Building A Structured Fundraise Process
• 8:30 Fundraise-As-A-Service And Analyst Model
• 11:20 Standardizing Inputs Versus Using Templates
• 18:20 2025–2027 VC Blind Spots: Energy And Infra
• 23:00 AI’s Compute, Cost, And Data Center Limits
• 26:20 Transportation Innovation And New Modal Ideas
• 31:00 AI Platforms And Capital Allocation Efficiency
• 36:20 Diligence As The First Major Area Disrupted
• 41:20 Will Traditional Methods Become Liabilities
• 45:20 How Early Movers Can Benefit And “Game” Diligence

Transcript

SPEAKER_01: 0:00

Hello, and welcome to Digital Transformation and AI for Humans with your host Amy. In this podcast, we delve into how technology intersects with leadership, innovation, and most importantly, the human spirit. Each episode features visionary leaders who understand that at the heart of success is the human touch, nurturing a winning mindset, fostering emotional intelligence, and building resiliency. My brilliant guest today, Jeffrey Fiddelman from New York, the US, joins me to explore the hidden venture capital trends shaping the coming years. I'm excited to uncover how AI is redefining fundraising and investment strategies. Jeffrey is the founder and managing partner of Fidelman and Company, a leading advisory firm launched in 2015 to address the critical fundraising and growth strategy needs of early to mid-stage companies. His firm is supporting clients across capital races, business sales, acquisitions, and strategic initiatives all over the world. Recently, Jeffrey was elected to the board of directors of Harvard Alumni Entrepreneurs, HAE, a global community of alumni founders, investors, and executives. With deep experience as an interim CFO and COO, Jeffrey has successfully guided dozens of companies through pivotal financial transactions, helping them unlock growth, secure capital, and navigate complex market dynamics. Jeffrey's career began in residential real estate, which led him to the portfolio manager role at Morgan Stanley, followed up by the role of vice president at HSBC, driving revenue strategy across banking, lending, insurance, and retail for Manhattan. As a seasoned entrepreneur, investor, and advisor with deep experience and expertise in finance and fundraising strategy, Jeffrey has built and supported ventures across multiple industries globally, helping funders secure capital, scale operations, and navigate growth. I'm honored to have Jeffrey as a part of the executive group of the AI Game Changes Club, an elite tribe of visionary leaders redefining the rules and shaping the future of human AI synergy. Welcome, Jeffrey. I'm so happy to have you here in the studio today.

SPEAKER_00: 2:36

Likewise, thank you so much for having me, and what a wonderful introduction. I could not have done it better myself.

SPEAKER_01: 2:41

Thank you. Amazing. Let's start this conversation and transform not just our technologies, but our ways of thinking and leading. If you are interested in connecting or collaborating, you can find more information in the description. And don't forget to subscribe for more powerful episodes. If you are a leader, business owner, or investor ready to adapt, thrive, and lead with clarity, purpose, and wisdom in the era of AI, I would love to invite you to learn more about AI Game Changers, a global elite club for visionary trailblazers and change makers shaping the future. You can apply at AIGamechangers.club. Jeffrey, to start with, I would love to hear more about yourself, about your story, and everything you would like to share with us about your journey.

SPEAKER_00: 3:34

Well, that's that's quite a question. I don't know how much time we have on this podcast, but I background-wise, I think you covered it well. In my own words, what I would say is I graduated Harvard. I spent the better part of a decade in banking, as you mentioned, between Morgan Stanley and HSBC. And I was then asked to help run a venture fund under a family office, where the thesis was investing in early stage tech and tech enabled companies. And um in 2015, as you noted, I left that fund and started what is now an investment bank. Um, always with a thought of working with early stage founders and helping them through their fundraise journey, either fundraise preparation, which is how this firm started, although and uh all the way through fundraise execution. And what I recognized between working in more institutional banking and then working in venture was that the needs were very, very similar, but the outputs were very, very different, and as well as the inputs, I suppose. And what I mean by that is when you're in the institutional banking, you have a client who is making$50,$100,000,$300 million a year, and they're waiting to go public, or they're waiting to get bought out or absorbed by a large conglomerate or organization. So that's more a question of who's going to invest rather than if someone is going to invest, and let's find that person, which is the case when working with an earlier stage company. And the way that we worked in the venture fund was that we would anchor our investment invest arounds and then help those founders syndicate the rest of the capital. So effectively, my fundraising experience was further bolstered in working through that VC, really gaining a better understanding of first of all, what's coming in. So different types of diligence documents, the deck, the model, the valuation, all of that is very, very standardized in late-stage companies. Early stage companies, not so much. So the input that we were getting, then what we had to work with the founders or the management teams of the businesses in putting together the fundraising documentation to go out to investors was a very, very different process. So in 2015, coming kind of out of the fund and starting, starting this firm, it was clear that the funnel, so to speak, or that the process that has to has to be worked through can and should be standardized, knowing that the inputs and outputs will be different from time to time. And I always say that structure is a lot a lot more important or incredibly important versus a template, which is something that really should almost never be used. And there's a big difference between structure and template. Structure being um kind of A, B, C, D happens in this order versus a template, which is here's your table of contents, and now here's the whole model, just put inputs in and distribute that to everyone. So so again, kind of going back to going back to the story, coming from institutional, going to venture, what I recognize is that a structure can be built and applied to early and mid-stage companies, both in fundraising preparation and in fundraising execution. So that's what we set out to do. And that's how this firm ultimately grew into just under 40 individuals, fully remote across US and Canada, because we've been able to apply a structure fundamentally to almost every engagement that we have, or pretty much every engagement that we have. And that's kind of a note on growth as well for anyone listening, having a services-based businesses that wants to turn it more into kind of, let's call it managed services, so to speak. That structure is incredibly important to allow businesses to grow. So when we have a client coming in needing help with fundraising uh materials, we know the structure of, well, here's what a deck needs to look like. Let's speak with the founder and let's get the content properly in the order that it should be presented. Same thing with financial modeling. We have a structure in mind, not a template. And then every conversation and every model that we build is custom for our clients. And then that has been, and we could talk about this later kind of in the podcast, but that's been incredibly impactful to the founders because it's not just here, let me buy something off the shelf and fill it out, but having them or requiring them to actually work through those materials hand in hand with us allows them to have a more successful outcome of their fundraises because they had hands in in building the deck, they had hands in in building the model. So they fundamentally understand way more than they would if they bought something off the shelf. And same thing on the fundraising side. Structure is applicable. We I was joke, we don't do anything proprietary necessarily, but rather we call it fundraise as a service, which is our um our version of putting a structure to fundraise execution, where we took best practices that you can find in Google or GPT or whatever the case may be, of you know, find a list, have consistent outreach, rinse and repeat. And that's what we ended up doing. Instead of building a Rolodex and representing only a few clients, we implemented best practices. So went out and subscribed to all the different investor databases, aggregated them into kind of internal. We built a workflow and an analyst on top of that. So now when we represent clients and helping them fundraise, it's the analyst who becomes an extension of our clients' team. Similar to an outsource investment relations. The analyst has with him or her all of the infrastructure that we built, the access to the databases, the workflow, the intercommunication, and we drop that into our clients' businesses on a month-to-month basis. So the analysts are calling investors on behalf of ABC Company or emailing from ABC.com so that from the investor's perspective, it's the company calling, not a third-party broker or banker. So looping all of that, and you asked about story and telling you everything. I know I kind of went into the business piece a little bit more, but it's really been a passion to, you know, and I don't want to sound cheesy or corny and saying democratize fundraising, but all too often we have seen tools that have popped up over the years of a founder tool set. Or here's an open-ended CRM for all the investors that you'll meet. And I always joke that it's like giving a calculator to my five-year-old son. The calculator is not broken, my five-year-old is not broken, but you're giving a tool to someone who doesn't know how to use it. So really wanting to have and now have successfully built fundamentally the infrastructure to help founders raise capital. So I'll pin it there so I don't monologue further beyond that.

SPEAKER_01: 10:33

That sounds really impressive. And thank you so much for sharing everything around how exactly you came to the place where you are today. And it's always inspiring to see individuals with a burning heart. And uh I'm looking forward to our today's conversation because I want to learn more and share all that wisdom with our listeners and viewers. So, Jeffrey, looking at 2025 and ahead to 2026 and 27, what hidden venture capital trends or blind spots do you see that most founders and investors are not paying attention to?

SPEAKER_00: 11:15

I'm not sure that I can necessarily talk about trends. We all see that AI is a big trend right now. I think it'll likely continue to be in different forms, different types of applications. I don't mean applications as an app, but rather the application of AI to different industries. And it's something we were talking about earlier is kind of this idea of innovation not being a single point of innovation, but rather a broad spectrum when innovation happens. And this is still kind of a new thesis of mine of how to look at the market, but we have fundamentally seen how technology advances one step at a time as opposed to this gradual advancement over time. And I see that happening because fundamentally, on the back end, there isn't enough support to create ubiquitous adoption of technologies as they come out. So with AI, for example, I think we have the ability to continue progressing AI as a software, as a language, as kind of whatever users or consumers or even businesses interact with. But on the back end, what is limiting that development and scale of innovation is bandwidth or compute in terms of data centers, cost, it's still pretty expensive to run that, and likely due to energy and energy costs and the different types of energy that need to be powering the AI centers that we only see increasing over time to further quench the demand that we have. So when thinking about blind spots, and when thinking, I don't know if this is a blind spot for many, for any, for some, but when I think about investments in general, and and when I think about kind of these quiet hyperscaling businesses, it's almost always around infrastructure, internet, wireless internet, satellite connectivity, and different forms of communication ability on a wireless location, right? The infrastructure behind it is really what has been able to grow. And the growth of that infrastructure has further led to call it supply into the market of demand from a consumer usage. So, energy, for example, we've been talking with a number of companies that are finding alternative means of energy. And I say alternative because they're not mainstream now. We have spoken with companies working on uh a lot of nuclear, a lot of fission, a lot of fusion. We have spoken with a number of companies that are now going back into kind of co-generation and gasification of things like garbage. There are hydraulic companies now that are coming out where I was telling you earlier, kind of putting the hydraulics into where toll booths are. And the hydraulics themselves, as a car or truck, drives over it, generates energy. So I think a major blind spot in what we're likely going to see over the next couple of years is a lot of investment into energy, a lot of investment into infrastructure. And with that investment will likely come innovation, right? Energy has to be cleaner, energy has to be cheaper. Transportation is likely going to be the next trend, perhaps, in alternative forms of transportation, right? When was the last time we had innovation in transportation? The car, the motorcycle, plane, right? Like those modes of transportation have maybe become slightly more efficient, more effective, a faster car, more luxurious ride, but it's still a car on four wheels, or it's still a motorcycle. And I say that because we're also we've spoken with a company that's developing kind of high speed on a on almost what looks like a monorail or a tether that takes very little land, doesn't require a large amount of eminent domain, is incredibly safe, and travels at you know three plus hundred miles an hour. So I think in terms of a short answer to your question, infrastructure and energy to fuel additional AI growth and kind of compute growth. And I think transportation and infrastructure is kind of another one that we'll likely see coming back around in in terms of both innovation and investment in the coming years.

SPEAKER_01: 15:23

Exciting times. And I agree, there are so many new opportunities, and so much is going to show up on the horizon just within the coming few years. So I'm looking forward to the future, which is actually closer than we could imagine, because it's like living in a science fiction movie, truly. So we are blessed to live in this time, right?

SPEAKER_00: 15:51

Absolutely. Well, if they they used to say, if you want to see the future, just read a science fiction book.

SPEAKER_01: 15:57

So true, indeed. And so many things are coming true, and hopefully most of them are going to belong to the brighter side of those stories as well.

SPEAKER_00: 16:09

Well, there's always a balance.

SPEAKER_01: 16:12

I agree. Jeffrey, how will AI-driven data platforms and automation reshape the way capital is allocated over the next two to three years? What are your thoughts on that?

SPEAKER_00: 16:25

I think that prudent capital allocation comes down to an analysis of data. In a in a simpler way of saying something like that is before you make an investment, you need to be educated about what you're making an investment in. And I think that over the past, I don't know, decades, the ability of data processing, the availability of data, has only increased over time, the availability. Right. We talk about how we have this educated consumer now. And that's whether you're you know buying an ice cream or buying a stock, all consumers are a little bit more educated, or hopefully they're a little bit more educated because the availability of data to them through the internet, through Google, through now asking GPT around it, unless it hallucinates, you have a lot more availability of information to educate yourself before making a decision. And that's why I say kind of when you look at the overall investments community, whether it's a professional investor, a fund, or someone in between, you have the availability of all of this data. And and where it's getting to now is an overavailability of data. It's too much to process for a person. Where there's so many different points of data that you can analyze, where it becomes a race of, you know, before it was mathematicians, then it became quants, now it became algorithmic writers, right? To to, and I'm thinking about this maybe from a hedge fund or investment fund perspective. There's so much data now where AI can really be implemented to churn that data out and make not educated decisions necessarily, but provided in a summarized fashion. So if you're looking at making an investment in an infrastructure company of what I was talking about, and let's call energy in particular, if you whittle it down to, okay, I only want to invest in this type of energy technology, there's no reason why you can't use AI to assist you to make the best investment decision of where that energy needs to be, what it should be priced at, how it should be built, the architecture to make it more efficient. So I think in general, businesses will become more efficient. And in turn, the investment decisions of the people investing in those businesses also will become more efficient and effective. I see a lot of this, and people talk about this all the time, and I don't want to talk about it firsthand. We're not in we're not investors, rather an investment bank. But you see a lot of this kind of herd mentality, especially as kind of dictated by the news, of where investment dollars are going. Right? Something like 40% last year or this year of mu venture dollars went into like three or four venture funds or firms, because they have kind of uh built this community, this network, but they're all investing in each other's deals anyway. And now you kind of have this ability, I hope, that with greater connectivity, now with AI, now with kind of different decision-making parameters, you'll be able to start distributing that capital further, not only to who's in the community, but to the best actual entrepreneur business idea, what have you. Similarly to stage of companies getting investment, um, for a while it frustrated us and many other clients and many other people out there, probably that the term seed investment, the stipulation had changed so much from 10 years ago to now a seed investment or see a company. Seeking seed investment needs to have a million dollars in ARR, needs to have XYZ metrics put in place prior to raising a seed round. Whereas, you know, by definition, a seed round was to seed a company. But now, you know, especially over the past couple of months, with vibe coding platforms, Lovable as an example, you have the ability to essentially build out an MVP for a few hundred dollars and a few hundred hours of your own time and somebody with zero technical expertise or engineering talent. So when thinking about kind of how AI is changing investment thesis for a lot of funds and investors that are looking to deploy capital, I think that the term or the name of rounds is now almost irrelevant. It'll probably revert to one, two, three, four, five to be sequential rather than seed A, B, C, D, E, F. I think K, someone just raised a recent round, um, Databricks, maybe they raised the K round to really fundamentally how easy it is to start a business today, even if it's a tech product. And again, easy. Let's just say straightforward. I don't want to say anything is easy. But really, with a tool like Lovable, a few hundred dollars, and that's if you're like using all of the prompts that you're given access to, and probably a few hundred hours, you, you, anyone we're talking to can develop some sort of MVP to actually bring and start testing to market. And investors know that, and they'll be looking for later and later stage. Um, despite the investor being an early stage investor, I think we'll see continued kind of later stage investments coming out of those um out of those funds.

SPEAKER_01: 21:42

This is very interesting. And uh, I'm going to come back to this topic a little bit later. But first, I'm curious which parts of the venture ecosystem from due diligence to portfolio management are most likely to be disrupted first by these shifts.

SPEAKER_00: 22:02

Well, okay, so let's categorize those. You have origination, you have probably some sort of like underwriting, then you have the the offer, so to speak, then you have due diligence, and then you have execution, and then you have portfolio management. If I'm kind of breaking it down, and maybe that's the it's one too many. I think origination will be slightly impacted. I still think it relies mostly on kind of human touch and interactivity, but I think that investors more and more, and we've seen a lot of like forms and submissions that you have to take, will kind of use that to originate deals. But I still think it'll it'll remain on outreach, introductions, things of that nature. I think the initial underwriting will probably be AI supported more and more over time, meaning, does this investor investment fit my fund criteria? Does it check all the boxes? Does it not check all the boxes? And the more structured data that is input, the easier it will be for investors to utilize that in the uh initial underwrite process. I think um I think on the diligence piece, it is probably going to be heavily used. We have had clients and we've seen a lot of companies that are already working on this. And we've seen this actually for the past couple of years, where now it's more AI driven and they call it more AI, or it used to be LLM and just mass modeling, where you can effectively upload a variety of different documents: PDF, Word file, Excel sheet, um CSV, uh QuickBooks, whatever output, and uh platforms will start underwriting deals. This has existed for a while in real estate, and we probably have seen a lot of companies in private credit start to do the same. Um, I would say that probably the heaviest impact will be inside of diligence of where AI will start to take over and supplement kind of human underwriting uh and due diligence. Execution piece will also likely be heavily AI driven, but I only say that because if it comes down to most negotiating the agreements and ready for signature, AI can take care of that. Not actually signing, but just setting everything up and sending it out. So it'll be the next iteration of you know, doc you sign, panda doc, or whatever people use. I'm sure there, if there's not already, there's going to be a lot more AI layered into those platforms. And then the last one you mentioned is portfolio management. That really depends on the fund. You have a lot of funds that are very active in kind of what they do, and they can probably use AI to keep a live dashboard um and and um increase their efficiency of participation. Whereas the ones that are a lot less operationally intensive, meaning they cut a check and then they put their hands up and and wait for returns, AI will probably affect them a little bit less. Um because they're not they're not really doing anything in the first place. So hopefully that's not too long of an answer. And to answer short answer um is diligence. I really feel that diligence will have the heaviest impact of AI, at least initially.

SPEAKER_01: 25:15

Absolutely. And I appreciate that you went a little bit more into the details because of course everybody who is working with these kind of topics is curious about every part, and uh your help here is invaluable. Jeffrey, many investors still rely on traditional ways of evaluating opportunities. How might these established approaches become liabilities in an AI-powered venture landscape?

SPEAKER_00: 25:47

I would draw the comparison to um high frequency trading when hedge fund started doing it, um, and kind of the closeness to to NICE or NASDAQ also giving people an edge. That's all been kind of worked through and out at this point, but it's also because everyone else is using something similar. So I think that there are some methodologies that will likely always work, but many will have to be updated so that your competitor doesn't necessarily have the edge. The edge being the extra point of valuation, the edge being the extra piece of diligence that they found. Now, in venture, it's a little bit more binary, again, depending on how much is input and operationalized, but the actual investment decision is pretty binary. Because if I'm coming into the same round as somebody else, likely we're coming in at the same terms. Right? There's not, they're not getting in a day or sooner at three pennies less, where they're going to make a little bit more money because the AI told them to, it's fundamentally not how this works in terms of venture. So I think if you're looking at it from a binary perspective, meaning yes or no, I invest or I do not invest, given how the market currently operates, you have a lot of deal sharing and you have a lot of like co-investments with one another. So unless somebody was super secretive and I really don't see how this would necessarily work. But if you see one person investing, then you're likely going to invest and share information with that other person. Venture investors in companies are incentivized to work with each other. They're not incentivized to go against each other, typically speaking, right? So if Jeff and Emmy invest in a deal, my AI or your AI is not going to give you any higher return on investing in a certain deal if we're both investing in the same deal at the same terms. If anything, it's in your interest and in my interest to share information with one another so that we can gain higher in conviction and maybe invest more money in that deal, or lose conviction and invest less or not at all in that deal. But there's not, you know, yes, there's competition. I can't say there's no competition between funds in general. I'd have to think about it more, but just my initial reaction is that it if it helps everyone or it hurts everyone in terms of venture investing as it comes to kind of how AI will impact an investor's go or no-go decision.

SPEAKER_01: 28:22

This is actually probably something really beneficial because then uh it creates a more fair playground and uh opens up new doors and opportunities. And speaking about the opportunities, what opportunities will open up for founders and investors who understand these shifts early, and how can both sides position themselves to capture the benefits?

SPEAKER_00: 28:53

You know, well, going off of the thread of what we were talking about, if diligence is going to be the most impacted piece of the life cycle of a dollar getting deployed and managed, then you know, founder, it would behoove founders to build into that, build into either creating a diligence platform for funds and underwriting platform for funds to use and become customers, or, and this is where it gets a little bit interesting, or learn the system so you can game the system. And what I mean by that is if you fundamentally understand how somebody else's AI due diligence algorithm works, then you can optimize your materials in providing it to that AI underwriting algorithm so that you can increase your chances of the highest score. And that's kind of the, you know, as you were asking the question, I was I had a vanilla sky moment. If you've seen the movie, it's kind of like, well, if everything is AI and everything is predictable, then everything kind of becomes monotonous in the same. And, you know, hopefully that doesn't really happen because people recognize that and and kind of break out of the mold. But at least initially, and probably over the next couple of years, as from a founder's perspective, either build that software and product for companies or at minimum really fundamentally understand how those diligence platforms work so that when you are raising capital and you are submitting your documentation, you're optimizing it, not for what you think it is, not what your accountant says it should be. Now, obviously, I you know, don't toe the line. Don't do no, I'm not I'm not saying suggesting anyone do anything illegal. Uh so I definitely want to give that disclosure. But there's definitely a way to optimize materials. Maybe it's the content, the order of the content, maybe it's the way things are presented, to increase your your likely output score from whatever AI is underwriting the deal. Um and from a fund perspective, it's just about capital efficiency. Um, so you're still going to be charging the same, you know, two and 20 or 1.5 and 20, 30, whatever the case may be. Maybe you know, some funds will will try to kind of push down management fees because they need less overhead now that AI is coming into effect. But but kind of the initial users of it will be more efficient at deploying capital. They'll just have a lower cost in the fund operations itself.

SPEAKER_01: 31:15

This is truly valuable. And I would like to take us back to the topic we've been already discussing. And you mentioned that what previously costed 100,000 is now being executed for 10,000 with AI and automation. So, how will this efficiency shift to redefine valuations, capital requirements, and the venture playbook overall?

SPEAKER_00: 31:44

No, that's a good question. Well, you know, the personal anecdote that I would share is that while we were building out kind of an internal proprietary CRM database workflow, we did go out to different development firms. And I think the best price we were coded in timeline was around 250k US in nine months. And right before pulling the trigger, I stumbled upon this really interesting website called Lovable, if anyone's heard of it. And I started playing around with, you know, building it out just one afternoon or one evening, which turned into many, many evenings. And like I said, a few hundred dollars later, I had built out an MVP for this platform. Now I have no like engineering background or technical knowledge. So I got it to a point that I could get it to, but I didn't, you know, I didn't know how to set up the database. I didn't know how to set up like the Google or Microsoft authentication and sign in, single sign and all that. Like it got me far enough, but it didn't get me all the way there. And I'm sure since then it maybe it could have today. And I ended up taking that MVP and then going to a group of developers that I knew and said, look, I don't need wireframes, I don't need you to think of the logic, I don't need you to think of the structure. It's done. I don't need you to design it, like it's done. I just need you to commercialize it, right? Like make everything work, essentially. And maybe that was an extra, let's call it 10K, plus or minus a few dollars. So effectively, I got a working product. Now, you know, this it wasn't like ideation all the way through. We effectively just productiz the process that we're using internally. So we knew how everything needed to work before building anything out. But I was able to build this out for, you know, call it four or five months, maybe 15K all in, probably closer to 12. Yes, a ton of my time, a lot of my time, especially in building out the MVP because I was doing it alone, but it's done and it's ready to go. Now, you know, then you have to start actually building the business, gaining revenues and building it from there before going to an investor. But the point from the investor's perspective now, especially now my perspective, having gone through that process myself, is that it is no longer acceptable to go and raise a hundred and fifty thousand dollar pre-seed round, generally speaking. Uh, and I know people always will come into the comments and eventually say, like, oh, I'm building a quantum computer, or I'm building a rocket engine, or I'm building a new pharmaceutical treatment. Like, yes, of course. For those types of things, you still need a significant amount of capital to build those out. But when you're talking about a product, like a like um even a widget, right? Now with globalization, tariffs being a separate conversation, but now with globalization, it's never been easier to proof out and print something, even 3D print something uh with cost now as well. And for a services-based business or a software business, with tools like AI and vibe coding types of tools, it's never been cheaper. And for that reason, a better time to start a business. So I think you know, investors are aware of that. They're becoming more and more aware of that. And unless you are kind of a tangible hardware type of company that does require significant investment just to build an MVP or a first product, you really should be, and this is from both perspectives, like an investor would probably say the same thing as I'm saying, you should be moving towards a product through a vibe coding platform or otherwise, because it's possible, at least as a proof of concept. And we've been seeing the same feedback from investors that our clients have been talking to.

SPEAKER_01: 35:39

This is very interesting. And uh speaking about the worldwide trends and uh what we are seeing on the market today, I wonder: will these trends create more fair competition for startups worldwide, or will they mainly benefit the established venture hubs like Silicon Valley, New York, Stockholm, London, or Singapore, for example? What is your take on that?

SPEAKER_00: 36:07

Well, you you know, it's your answer is your actual answer is in the question. Because if we spoke 20 years ago, New York was not a tech hub at all. Like at all, it was not a tech hub. That was the joke, again, a long time ago, where you go to LA with no revenue and raise a ton of money, you come to New York, try to do the same thing, and you know, they kick you to the side. Now all of a sudden, New York is a tech hub. Same thing with Miami. Miami, I mean, only up until really COVID and after COVID has really kind of become more and more a tech hub, even though it's it's still trying to work itself out. London, Stockholm, all of these places, Singapore especially. I mean, Singapore itself is less than 100 years old. I'm not sure exactly when it was started and grew, but Singapore is relatively new as a city in and of itself. And it being a central hub both financially and from a technology perspective, is is the answer I suppose that that we're looking for. I do think that there's going to be a distribution of technology hubs across the world. I still think that US will continue to be the golden child of innovation because of capital markets and because of consumer demand and sentiment, right? Like no one has a bigger uptake than the US. You want to sell an app for a dollar a day or a dollar a month, you come to the US with the expectation of you're you're going to get revenue here. And it's from a cultural perspective too. US is very open to innovation and very open to adoption and mass adoption. I think something like 70% of um a billion or unicorn companies are consumer-facing companies when you think about it. So I think it remains in the US. Even with clients who are working with internationally, Europe, both East and West Europe, a lot of clients in the Middle East that we have. We occasionally have clients from Singapore as well, although time difference makes it makes it difficult for us to work together. But common denominator across all of those clients, they're always looking for US capital.

SPEAKER_01: 38:13

This is great to know. And um yes, I am sure this is going to be very valuable for so many of our listeners and viewers. So I'm grateful that you are sharing this view with us. What is one thing investors and business owners must unlearn to succeed in AI-powered fundraising and business growth? The unlearning part, because it's not often that we're thinking about it's about time to truly address this area.

SPEAKER_00: 38:45

To unlearn. It depends. Uh and I hate to answer questions that way. It depends on what the business is and who the business owner, founder, or management team are. I would say that with AI, it's important, it's two important pieces of AI. And I don't know if this is unlearning or if this is learning or however you want to think about it. But with AI implementation, and I would probably say this about many other things, but it's really, really important to look into and to build some sort of AI into your business practices. Across the board, no matter who is in what business, it is important to do this. On the other side, though, of the same coin, it is very important to be hyper-attuned to what is the implication of implementing the AI. And what I mean by that is almost like scientific method, right? You have your control, you have your variable, your control is current business processes, your variable is AI led business processes, and you have this experiment. And you define the experiment by number of days and data points collected. I would say that 30 is too short, 60 to 90 is probably around the right amount of time to implement something like that. Meaning you let your experiment Run for 60 to 90 days with both control and variable. And at the end of that time period, assuming that your roof is not on fire and the business isn't burning down, but you know, you go through that, and no matter what, unless it's an absolute disaster, I would say no matter what, let it run. And then collect the data points and come to your conclusion. And the conclusion is, or you know, what it should be, is did the AI-led process, did that variable create more efficiency and benefit our business versus the control or the older way of doing things? And I'm not sure if this is something to unlearn. I know some people are very open to innovation and changing things. We certainly are at our firm. And I know others that you know it's my way or the highway. We've always been doing it this way, we'll never do it a different way. So I think it's important as it applies to AI to yes, to do it. Whatever it is, do it. We recently worked, we have a client, happy to introduce him to whoever's listening. We have a client who had a client who essentially built a business of going in as a consultant, and he spent decades as a consultant, going into every business as a consultant, and you can hire him, and he'll figure out a way to implement AI. And I'm sure there are many, many others that are doing stuff like that as well. And then he'll actually build AI out for the businesses. But it is important, whether it's a consulting route, whether it's sitting on a computer behind Lovable and doing it yourself, or something in between. There's something called relay, I think.app, where you can create it's almost like uh if this then that, but for the AI generation. But it's really, really important to try to implement those things. And like I said, you know, just to, I don't know, repeat myself, I suppose. But as much as important to try those things, it's equally as important to measure the data and really understand the impact that you've had. Because unless you're doing that, then it's a it is a waste of time and money. And then it's incredibly more difficult to revert back because after 60 or 90 days, whatever process that you implement, you start relying on it. And that's what you should avoid, not to rely on any one process that that you implement too soon.

SPEAKER_01: 42:07

This is a great point, and I would say that it actually applies to so many more areas. So I absolutely love it. Brilliant. Jeffrey, if you could offer one piece of advice on top of that to leaders to ensure growth and success in the coming years of EI transformation, what would that be?

SPEAKER_00: 42:30

It would be measured efficiency. Measured efficiency. So going off of what I just mentioned, control variable, time of experiment, you have a hypothesis going into it, conclusion coming out. It's, you know, just to further that, you continue doing that, right? Like business, life in general, it's one big scientific experiment by definition of you always try to have do something new and you figure out later on was it a good thing or a bad thing that I did that new thing. What I mean by measured efficiency is that what I have found, both in our business and in working with other businesses, if you break them down into business units or practices or processes, there is kind of a curve of efficiency that you'll ever be able to gain in a business unit over any amount of time that is consecutive. So you start a business day one, you know, you run your experiment, you start making it better and making more efficient. And maybe on your first iteration, you make it 100% more efficient. Totally different story. You do a 180, it's so much better now. You do it again, and next time you get 50% efficiency, 30%, 20%, 5%, 2%, 1%, fractions of a point more efficient. And there's a certain point where you know that you can continue making it more efficient, but the amount of time and resources that are used to making this process more efficient is actually negative at this point. Yes, you're making it one tenth of 1% more efficient, but the amount of time and effort and energy and stress that it took out from you, you could have rather gained 20% more efficiency in some other business process or initiative. So make sure that whatever you are trying to make more effective or more efficient is measured. Not to say that good enough is good enough, but perfect is the enemy of done. So you may as well get it done, get it out there, gain efficiencies where you can, and then keep moving through the units so that at the very least, you leave this process alone, you go on to others, and a year later you can come back to it, and maybe now you can gain another 5 or 10% efficiency. But not to get bogged down in any one initiative or any one process, good enough is might might not be good enough, and you go one or two steps beyond that. But at that point, you need to limit the amount of attention that you're spending on this process and just move on to something else.

SPEAKER_01: 44:52

Thank you so much. I absolutely love it. And this is quite different from so many other advice we've got through the episodes of this podcast, and that's what makes it even more valuable and more unique. I truly appreciate you, Jeffrey, and I'm very grateful for our today's conversation. Thank you so much for all your wisdom and experience shared with everybody today.

SPEAKER_00: 45:20

Thank you so much for having me, Amy. It was a pleasure.

SPEAKER_01: 45:23

Thank you for joining us on digital transformation and AI for humans. I am Amy, and it was enriching to share this time with you. Remember, the core of any transformation lies in our human nature, how we think, feel, and connect with others. It is about enhancing our emotional intelligence, embracing the winning mindset, and leading with empathy and insight. Subscribe and stay tuned for more episodes where we uncover the latest trends in digital business and explore the human side of technology and leadership. If this conversation resonated with you and you are a visionary leader, business owner, or investor ready to shape what is next, consider joining the AI Game Changers Club. You will find more information in the description. Until next time, keep nurturing your mind, fostering your connections and leading with heart.