As an investor, you've probably heard it all: "AI is the future!" or "Web3 is the next internet revolution!" These are the buzzwords echoing across pitch decks, conferences, and LinkedIn feeds. And while these technologies undoubtedly have transformative potential, there's a trap hidden in all the noise—a blind spot many investors fall into. It’s not about missing the boat; it’s about boarding the wrong one.
Today, we’re not here to repeat the hype. Instead, we’ll explore the hidden risks in AI and Web3 that most investors overlook. More importantly, we’ll outline a clear framework, specific to The Brutally Honest Angel, to help you separate the gold from the fool’s gold.
The Shine of AI and Web3: Why Investors Get Hooked
First, let’s address why these sectors are so attractive. AI promises to revolutionize industries—healthcare, finance, logistics, you name it. Web3, with its decentralized nature, paints a picture of a fairer, more transparent internet where users truly own their data and assets.
These ideas are compelling, and founders pitch them well. But as an investor, it’s your job to go beyond the surface. The problem isn’t the tech itself; it’s how easy it is to fall for overhyped narratives and overlook deeper structural issues.
Let’s start with the hype machine. Founders in these sectors are masters of storytelling. They weave narratives about their technologies solving world problems or democratizing access to information. But remember, a good story doesn’t always translate to a good business. Your job is to interrogate these stories, to challenge the underlying assumptions that make them sound so compelling.
Another factor driving investor enthusiasm is FOMO (fear of missing out). When everyone is pouring money into AI and Web3 startups, it can feel like sitting out means falling behind. But chasing trends without fully understanding their nuances is a surefire way to end up with investments that look promising on the surface but fail to deliver value over time.
And then there’s the role of media. Headlines focus on breakthroughs and billion-dollar valuations, but they rarely highlight the failures and hidden pitfalls. This creates a distorted view of these industries, where success stories dominate the narrative, making it harder to spot the risks.
Framework for Identifying Hidden Risks: The TBHA "3R Lens"
At The Brutally Honest Angel, we’ve developed the 3R Lens to evaluate investments in sectors prone to hype like AI and Web3. This lens focuses on three dimensions:
Reality Check – How grounded is the startup in solving real, pressing problems?
Risk Layers – What are the hidden technical, regulatory, and market risks that could derail the business?
Resilience – How prepared is the team to pivot or handle setbacks inherent to these fast-moving industries?
Using this framework, let’s break down the common pitfalls investors face in AI and Web3, along with actionable insights.
Hidden Risk #1: Overestimating Market Readiness
AI Blind Spot: Many AI startups claim their technology can disrupt industries overnight. But AI adoption in traditional sectors—like healthcare or logistics—takes time due to regulation, infrastructure, and entrenched practices. For example, AI diagnostic tools might pass technical tests but fail in real-world hospital environments due to data incompatibility or doctors’ mistrust.
Web3 Blind Spot: Web3 startups often pitch their projects as "the decentralized alternative to X," whether it's banking, social media, or gaming. However, most users are not ready—or willing—to make the leap. Concepts like private key management, which are core to Web3, remain too complicated for the average consumer.
Even when the tech works, consumer education and behavior change take time. Think about it: how long did it take for smartphones to become ubiquitous? And that was with massive consumer-facing companies like Apple and Samsung driving the shift. Web3, by comparison, lacks such unified forces, making its adoption curve potentially much steeper.
TBHA Takeaway: Ask the Hard Questions
AI: How dependent is the startup on slow-moving industries or third-party adoption? If their solution requires years of lobbying or infrastructure changes, the timeline may not align with your investment goals.
Web3: Does the startup offer an interface or user experience that makes it easy for non-technical users? A polished whitepaper is not enough.
Market readiness isn’t just about the technology; it’s about timing. An idea that’s too far ahead of its time can burn through investor money without gaining traction. Look at how many early AI startups from the 1990s failed because the market simply wasn’t ready for their solutions.
Hidden Risk #2: Dependency on Unproven Technologies
AI Blind Spot: Founders love to throw in jargon like “neural networks” and “deep learning.” But not all AI solutions are created equal. Many startups build their models on publicly available datasets or open-source algorithms, which competitors can easily replicate. Worse, some rely on experimental technologies that haven’t been proven outside of research labs.
One specific example is AI systems dependent on “edge computing.” While this technology has incredible potential, its infrastructure remains in its infancy. Startups betting heavily on such cutting-edge tech face a precarious situation if the ecosystem doesn’t mature as quickly as they expect.
Web3 Blind Spot: Blockchain scalability is still a massive issue. Many Web3 startups promise transactions per second that rival Visa or Mastercard, but the underlying infrastructure simply can’t handle it. Ethereum gas fees, for example, have made many Web3 applications impractical for everyday use.
And then there’s the interoperability problem. Web3 ecosystems often operate in silos. A decentralized application built on one blockchain might struggle to integrate with another. This fragmentation limits the network effects that are crucial for any technology to achieve mainstream success.
TBHA Takeaway: Dig Into the Tech
AI: Does the startup have proprietary datasets or algorithms that give it a defensible edge? If not, it risks being outpaced by competitors with better resources.
Web3: Has the startup addressed core issues like scalability and interoperability? Solutions reliant on “future upgrades” to blockchain protocols are risky bets.
Hidden Risk #3: Regulatory Uncertainty
AI Blind Spot: AI startups often operate in areas with unclear regulations. For example, facial recognition has incredible potential in security but faces bans and restrictions worldwide due to privacy concerns. Similarly, autonomous vehicles are stuck in legislative limbo in many regions.
Web3 Blind Spot: Web3 faces an even murkier regulatory landscape. Governments worldwide are grappling with how to regulate cryptocurrencies, DeFi platforms, and NFTs. A startup that thrives in today’s regulatory environment could crumble under tomorrow’s rules.
In some countries, regulations could outright ban certain Web3 applications. China’s crackdown on cryptocurrencies is a cautionary tale for anyone investing in blockchain-based startups. Meanwhile, the European Union’s AI Act is set to impose strict rules on how AI technologies can be developed and deployed.
TBHA Takeaway: Stay Ahead of Regulation
AI: Does the startup have a legal and compliance strategy? Teams that ignore this are setting themselves up for trouble.
Web3: Does the business model rely on loopholes or regulatory arbitrage? These are short-term solutions that rarely last.
As an investor, you don’t need to be a regulatory expert, but you do need to ask the right questions. Understanding how regulations might evolve—and how startups plan to adapt—is crucial.
Hidden Risk #4: Founder Overconfidence
AI Blind Spot: AI founders often come from technical backgrounds and underestimate the complexities of commercializing their solutions. Building a great algorithm is one thing; convincing a client to pay for it is another.
Web3 Blind Spot: In Web3, founders are sometimes ideologically driven, prioritizing decentralization over practicality. This can lead to solutions that are technically impressive but lack real-world utility.
Even worse, some founders in these fields believe their technology is so revolutionary that they don’t need to bother with basic business principles. This overconfidence can manifest in everything from unrealistic revenue projections to resistance against feedback.
TBHA Takeaway: Assess the Founders
AI: Does the team have a balance of technical and business expertise? A lone technical genius without a savvy business partner is a red flag.
Web3: Are the founders flexible enough to adjust their ideals to market demands? Rigidity is a recipe for failure.
Hidden Risk #5: Misaligned Incentives in Web3
One of the biggest risks in Web3 is the prevalence of token-based models. Founders often launch tokens to raise funds quickly, but these tokens create misaligned incentives. For example:
Early token holders may push for short-term price gains rather than long-term value creation.
Founders may exit the project once they’ve cashed out their tokens, leaving investors holding the bag.
TBHA Takeaway: Look Beyond the Token
Is the startup’s business model viable without the token? If not, you’re investing in a speculative asset, not a company.
How is the token supply structured? Pay close attention to vesting schedules and founder allocations.
Hidden Risk #6: The Overlooked Cost of Scaling AI
AI startups often underestimate the cost of scaling their models. Training AI models requires massive computational resources, which can quickly eat into budgets. Additionally, scaling often reveals hidden biases in the model, requiring costly retraining and fine-tuning.
For instance, an AI model designed for natural language processing might perform well in testing but struggle with diverse accents or dialects when deployed globally. Addressing such issues can demand significant resources, both financially and in terms of time.
Another overlooked factor is the reliance on third-party providers. Many AI startups depend on cloud services for computational power, which makes them vulnerable to fluctuating costs or service disruptions. Without a clear roadmap for transitioning to more cost-effective infrastructure, these dependencies can erode profitability as the startup grows.
TBHA Takeaway: Evaluate Scalability
Does the startup have a clear plan for managing the high costs of scaling AI models? A robust partnership with cloud providers like AWS or Google can be a good sign.
How does the startup plan to address potential biases or errors that arise during scaling?
Hidden Risk #7: Lack of Real-World Integration
AI Blind Spot: AI startups often focus on technical brilliance while neglecting practical integration. For example, a predictive analytics tool might work seamlessly in a controlled environment but fail when integrated into an organization’s existing software stack. This gap between innovation and implementation can result in delays, increased costs, or outright project failures.
Web3 Blind Spot: Integration challenges in Web3 are often tied to interoperability. A blockchain-based supply chain startup, for example, might struggle to gain adoption if it cannot integrate with the legacy systems of major industry players. Without seamless interoperability, the promise of decentralization loses its appeal to businesses operating in traditional industries.
TBHA Takeaway: Prioritize Integration
AI: Does the startup have partnerships or pilots with key industry players to validate its integration capabilities?
Web3: Is the solution designed to work alongside existing systems, or does it require users to overhaul their infrastructure entirely?
Hidden Risk #8: Unrealistic Growth Assumptions
Many startups in AI and Web3 are overly optimistic about their growth trajectories. They assume rapid adoption without accounting for potential roadblocks such as customer education, competitive pressures, or economic downturns.
For example, a Web3 startup targeting decentralized finance (DeFi) might underestimate the time it takes to build trust in financial ecosystems. Similarly, an AI startup developing automated customer service tools might face resistance from businesses wary of alienating their customer base with overly robotic interactions.
TBHA Takeaway: Challenge Growth Projections
What assumptions underpin the startup’s growth projections? Are they realistic given the current market conditions?
Has the team accounted for the time and resources required to educate customers or build trust in their solution?
Hidden Risk #9: The Talent Trap
AI and Web3 startups often compete for the same pool of specialized talent. Hiring and retaining skilled engineers, data scientists, or blockchain developers is not only expensive but also highly competitive. Startups without a compelling mission, strong leadership, or attractive compensation packages may struggle to build and maintain the teams they need to execute their vision.
In Web3, the talent challenge is exacerbated by the nascency of the field. With fewer experienced professionals available, many startups rely on self-taught developers who may lack the expertise required to build robust systems.
TBHA Takeaway: Evaluate the Team
Does the startup have a clear strategy for attracting and retaining top talent?
How experienced is the team in navigating challenges specific to AI or Web3?
Hidden Risk #10: Ignoring Ethical Considerations
In both AI and Web3, ethical concerns can quickly spiral into public relations disasters. AI startups deploying facial recognition or predictive policing technologies may face backlash over bias or privacy violations. Web3 startups can come under fire for enabling illicit activities or environmental harm due to high energy consumption in blockchain mining.
These issues aren’t just moral dilemmas—they’re business risks. Public scrutiny, legal action, or consumer boycotts can derail even the most promising ventures.
TBHA Takeaway: Assess Ethical Risks
AI: How does the startup address concerns around bias, privacy, and misuse of its technology?
Web3: Is the startup actively working to minimize its environmental impact or address concerns about enabling illicit activities?
Hidden Risk #11: Overreliance on Trends
Finally, one of the most significant risks is investing in startups that chase trends without a solid foundation. In AI, this might mean a startup pivoting to focus on generative models like ChatGPT simply because they’re trending. In Web3, it could involve launching an NFT marketplace without a clear value proposition, banking on the buzz around digital collectibles.
Trendy startups often burn through cash quickly, prioritizing speed to market over building sustainable business models. When the hype fades, they’re left without a strong core to fall back on.
TBHA Takeaway: Look for Substance
Does the startup have a clear long-term vision, or is it riding the coattails of a trend?
How defensible is the startup’s business model in a post-hype environment?
Building Smarter Investment Strategies
As AI and Web3 continue to evolve, they will undoubtedly create immense opportunities for investors. But the key is to approach these sectors with a critical eye. By applying The Brutally Honest Angel’s 3R Lens, you can identify hidden risks early and make smarter investment decisions.
The next time you’re presented with a pitch promising to “revolutionize” an industry, take a step back. Ask the tough questions, dig into the details, and resist the allure of hype. Great investments aren’t about chasing buzzwords—they’re about finding teams and ideas that can weather the challenges ahead and deliver real value.
In a world dominated by AI and Web3 hype, staying grounded is your biggest competitive advantage. Let others chase the shiny object while you focus on what truly matters: sustainable, impactful, and resilient investments.
