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October 06, 2023

Anna Ruth Williams



In the first half of 2023, Pitchbook calculates that private equity and venture capital investors bet $40 billion on AI startups. (Granted, $10 billion of that total is attributed to Microsoft’s investment in OpenAI.) 

As we race through the second half of the year, AI investments haven’t slowed down. Most notably, Amazon’s $4 billion investment in Anthropic and Visa Venture’s pledge to invest $100 million in generative AI applications fueling “the future of commerce and payments.”

So, what types of AI are investors betting on? First, let’s step back and look at the three stages of AI: Narrow AI, General AI, and Super AI. 

  • Narrow AI, also known as Weak AI, refers to systems that are designed to perform limited tasks within a specific sphere (e.g., ChatGPT). While these systems are great at performing a particular function, they lack the intelligence and versatility of human cognition. 

  • General AI, also known as Artificial General Intelligence (AGI), is still a hypothetical form of AI. It promises to perform any intellectual task that you or I could.

  • Super AI, also known as Artificial Superintelligence (ASI), is an AI system that will be able to surpass human intelligence in virtually all areas. When Super AI is realized, it will be capable of outperforming you and me in virtually every cognitive task.

types of artificial intelligence

So, we spoke with VC investors to get their take on what AI technologies they’re betting on today – and if they expect to be investing in General or Super AI anytime soon.

Mark Buffington, Managing Partner at BIP Ventures

I still think investors tend to get hyped up by big themes – blockchain, Big Data, crypto, etc. That’s why today, there’s no shortage of AI-focused VC funds popping up. But most of these funds are solutions out there looking for problems. In reality, firms who invest in ‘themed funds’ end up divorcing themselves from real business problems, and ultimately end up losing a lot of money.

That’s why I’m more interested in marrying real world problems and problems that are worth solving with the application of AI tools that can do just that. 

At BIP Ventures, our AI investing criteria is that the product must 1) solve real world problems, 2) be highly differentiated and, 3) have such market demand that it flies off the shelf. 

A great example of this is our portfolio company Kythera, which uses ML, data science and AI to drive predictions in healthcare. For example, the efficacy of pharmaceuticals and treatment protocols. Today, a doctor is augmented by this technology, making it Narrow AI. But the technology is inching towards Super AI in that the machine will eventually know more than the doctor about a patient’s health.

Michael Maziar, Vice President, Emerging Practice at Silicon Valley Bank

The AI solutions I’m seeing investors hone their thesis around goes beyond the noise of a nice wraparound Chat GPT without a unique use case, say legal or accounting which have dominated narratives so far. Hallucinations and miscalculations included. But the use of AI in legal proceedings is a topic of increasing debate and scrutiny (See: the former Fugee rapper Pras’ case highlighting poor AI writing which may lead to a retrial).

While important, I get more excited with emerging managers who are leading the way with a unique thesis and deep industry understanding. For example, a firm recently established invests in AI advancing the bioeconomy. Their portfolio companies will radically accelerate personalized medicine, drug discovery, and therapeutic delivery at a price and pace never seen before.

Superintelligence is a natural progression and machine consciousness has permeated the mainstream over from science fiction for decades already. However, its widespread adoption still faces barriers and steep hills to climb. It reminds me of the work that Dr. Ayana Howard, formerly of NASA, Georgia Tech and now Dean at the Ohio State College of Engineering. She’s been working on human-robotic interaction for years, combining emotional intelligence of humans and applying it to robot acceptance beyond pop culture fascination. The corollary for AI is the chatbot actually sounding human, not because of its tone or cadence, but rather its ability to build trust through an emotional connection. I’m confident we can give AI machine consciousness, but believe the timeline for an emotionally aware sentient in everyday life is still far off. Not because we don’t have the technology, but rather we lack collective behavioral change. Shifting a paradigm takes time and patience.

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