Artificial intelligence (AI) remains one of the strongest drivers of venture capital investment, proving that the hype cycle isn’t even close to finished. According to a recent EY report, 37% of fundraising in the third quarter of 2024 was for AI-related companies, similar to second-quarter volume. Startups using AI are getting noticed for their ability to tackle big problems in robotics, automation, healthcare, logistics, and more. But the reality is that investors hear, “We’re using AI” all day. The degree to which entrepreneurs actually use it varies substantially. There is even backlash from investors, including a 31-page report by Goldman Sachs that questions how worthy AI is of investment.
The Federal Trade Commission (FTC) recently announced a crackdown on companies making deceptive AI claims. This “AI washing” — lobbing AI into marketing without backing it up — might grab attention, but it’s a fast track to losing credibility. Founders need to communicate clearly and honestly about how AI fits into their business. The focus has to be on actual innovation, not just chasing buzzwords.
It is critical to avoid situations like Theranos, where bold claims were made without substance, leading to severe consequences. The stakes are even higher with AI, as the technical complexity makes it harder to verify claims of how it’s used and easier for misuse to slip through. According to insurer Allianz, 38 AI-related securities class action lawsuits were filed between March 2020 and October 2024 — 13 of them came in 2024 alone.
AI’s appeal to investors isn’t just about technical sophistication. It’s about solving problems that matter and creating a real business. Founders who take shortcuts or exaggerate their AI capabilities risk alienating the very backers they’re trying to attract. With regulators sharpening their scrutiny and the market growing more discerning, delivering substance is essential.
AI’s broad reach
Artificial intelligence encompasses far more than the conversational AI tools that dominate headlines. Patrick Winston, the late computer scientist and professor at MIT, outlined the foundational elements of AI more than 30 years ago in his seminal textbook, “Artificial Intelligence.” Long before large language models captured the public’s imagination, AI was driving advancements in problem solving, quantitative reasoning, and algorithmic control. These roots highlight the diverse applications of AI beyond chatbots and natural language processing.
Consider the role of AI in robotics and computer vision. Simultaneous localization and mapping (SLAM), for example, is a groundbreaking technique enabling machines to navigate and interpret environments. It underpins critical autonomous systems and exemplifies AI’s capability to address complex technical challenges. While not as widely recognized as large language models, these advancements are just as transformative.
Fields such as speech recognition and computer vision, once considered AI innovations, have since matured into distinct disciplines, transforming industries in the process and, in many cases, losing the ‘AI’ label. Speech recognition has revolutionized accessibility and voice-driven interfaces, while computer vision powers advancements in areas like autonomous vehicles, medical imaging, face recognition, and retail analytics. For founders, this underscores the importance of articulating how their innovations fit within AI’s broader landscape. Demonstrating a nuanced understanding of AI’s scope enables startups to stand out in an increasingly competitive funding ecosystem for early-stage companies.
For instance, machine learning models can optimize supply chain logistics, predict equipment failures, or enable dynamic pricing strategies. These applications may not command the same attention as chatbots, but they offer immense value to industries focused on efficiency and innovation.
Speaking investors’ language
When communicating to founders how they use AI, founders should focus on measurable impacts, such as improved efficiency, better user outcomes, or unique technical advantages. Many investors are not deeply technical, so it’s essential to present AI capabilities in simple, accessible language. Explaining what the AI does, how it works, and why it matters builds trust and credibility.
Investors are growing weary of hearing the term “AI,” concerned that entrepreneurs are over-branding their ventures with the technology instead of how it helps them solve problems. AI has become table stakes in many industries, and its role should not be overstated in a company’s strategy.
Equally important is transparency. With the FTC cracking down on exaggerated AI claims, being truthful about what your technology can and cannot do is a necessity. Overstating capabilities might generate initial interest but can quickly backfire, leading to reputational damage or regulatory scrutiny.
Founders should also highlight how their use of AI aligns with broader market opportunities. For example, leveraging AI for predictive analytics, optimization, or decision-making systems can demonstrate foresight and innovation. These applications may not dominate headlines like chatbots, but they address real-world needs that resonate with investors.
Ultimately, it’s about presenting AI as a tool that drives value and solves pressing problems. By focusing on clear communication, honesty, and alignment with investor priorities, founders can position themselves as credible and forward-thinking leaders in the AI space.
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