What Founders Should Know About AI Trends

A Thought Leadership article we liked from The Generalist:

What to Watch in AI

If you only have a few minutes to spare, here's what investors, operators, and founders should know about the most exciting AI trends. 

• Copilot for everything. AI is already streamlining illustration, writing, and coding. It may soon become an assistant for all knowledge workers. In the future, we may have versions of GitHub’s “Copilot” feature for lawyers, financial analysts, architects, and beyond.
• Tracking value accrual. As AI startups often rely on publicly available models like GPT-3 or Codex, some question their defensibility. The fundamental question centers around value accrual. Will applications that leverage GPT-3 successfully capture value? Or will it accrue to the infrastructural layer?
• Beyond words and images. GPT-3 and DALLE-2 have attracted deserved attention for their ability to automate text and image creation. The most impactful uses of AI may come from the life sciences, though. AI can be used to design better pharmaceuticals or run more efficient clinical trials.
• Improving interfaces. Interactions with AI typically take the form of a basic text box in which a user enters a “prompt.” While simple to use, greater control may be needed to unlock the technology’s power. The challenge will be to enable this potential without introducing needless complexity. Applications will need smooth, creative interfaces to thrive.‍
• Addressing the labor shortage. Skilled laborers are in short supply as society’s need increases. For example, while demand for skilled welders increases by 4% per year, supply declines by 7%. AI-powered robots may be part of the solution, automating welding, construction, and other manual tasks.

“This time is different.”

Sir John Templeton, the man named “stockpicker of the century” by Money magazine in 1999, referred to those as the “four most dangerous words in investing.”

It’s a good quip and a fair point. Markets are full of mirages, and circumstances that appear exceptional may show themselves to be mundane – one movement in a familiar, repetitive cycle.

Sometimes, though, things really are different. Sometimes, a tiny, promising glimmer produces a lasting flame. Sometimes, the world is genuinely changed.

The sentiment in venture capital is that we may be in the midst of such a moment when it comes to artificial intelligence (AI). The past year has seen a blossoming of new models and startups, along with increased public interest. While venture investing in the sector has slowed in line with the broader market pull-back, talk to VCs today about what they’re most excited by, and generative AI is often mentioned.

As ever, there’s a chance we look back on this period as a false dawn – the result of capital searching for heat amidst a cooldown. But that feels unlikely. My first venture job was in 2016 when every other pitch deck purported to have some AI advantage, and chatbots were seen as a UX evolution. Playing with DALLE-2, GPT-3, and Stable Diffusion feels decidedly different than that era, the equivalent of jumping from a pull-to-speak doll to a precocious toddler. AI is unlocking real creativity and real commercial value, producing novel images, plausible writing, and usable code. The sheer volume of innovation and experimentation often feels difficult to follow as improved models supersede predecessors, and startups identify new ways of leveraging them. The horizon of possibility looks distant one day, then jarringly close a few weeks later.

To better understand the state of the industry, I’ve asked ten thoughtful AI investors to share the trend they believe is worth watching. My hope is that it helps us (myself included) better identify areas of opportunity and topics worthy of further research.

A note on how these collaborations come together.

While investors know what other contributors are writing about and are encouraged to pick different subjects, I’ve found that some overlap is often interesting. Two investors might analyze a similar topic very differently, and there is value in their distinctions.

Additionally, I intentionally do not preclude investors from mentioning companies in which they have invested. Everything is a matter of trade-offs, and I believe the benefits outweigh the perceived costs. The downside of this approach is that investors may be seen as “talking their book.” Firstly, we select contributors that I consider thoughtful and reliable. Secondly, it’s more interesting to allow investors to pick the companies they know best and have studied most deeply. It also requires them to choose among favorites. Lastly, it demonstrates they have skin in the game, capitalizing their convictions.

With that outlined, let’s tumble down the AI rabbit hole together and learn how new technologies are impacting our minds, bodies, and machines.

Read on for short essays from top AI thinkers who highlight the AI trends to keep an eye on, including Reid Hoffman, Saam Motamedi, Sarah Guo, Lan Xuezhao, Matt Turck, Leigh Marie Braswell, Nathan Benaich, Rob Toews, Cat Wu, and Michael Dempsey.

Trend: The elevation of human work

Is there any profession as quintessentially right-brained as an “artist?” Or one as left-brained as a “programmer?”

What's been so remarkable to us about the rapid evolution that has characterized the last year, especially in the large language models, is how they’re now powering assistive tools that radically increase productivity, impact, and value across a wide range of professions.

For artists, we've got AI image-generation tools like OpenAI’s DALL-E, Midjourney, and many others. For programmers, we've got Microsoft's GitHub Copilot, which helps software developers write, test, and refine code in many of the most currently popular computer languages.

While some AI skeptics characterize large language models as brute-force prediction machines that won’t ever imbue computers with anything like human intelligence or consciousness, what we see, in mind-blowing practice, is how profoundly these kinds of AI tools are already beginning to enhance human flourishing.

What Copilot does for developers and DALL-E does for visual creatives of all kinds is reduce or eliminate rote, time-consuming, but still crucial aspects of their jobs. Of course, this dynamic is hardly unique to software developers and artists. Large language models are trained on massive quantities of text data, then incorporate what they “learn” to generate statistically probable (contextually sensible) output to user-supplied prompts. So while Github Copilot was trained by...

Click here to read more and also see the AI briefings from Saam Motamedi, Sarah Guo, Lan Xuezhao, Matt Turck, Leigh Marie Braswell, Nathan Benaich, Rob Toews, Cat Wu, and Michael Dempsey.

Thanks for this article excerpt and its graphics to The Generalist.

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