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Since our last post looking at artificial intelligence and generative AI, we have seen an explosion of activity that can only be referred to as a “hype-cycle.”  There is a growing consumer and enterprise appetite for this technology, and more and more companies are jumping on board. And with all this buzz, there is an even greater interest on the part of investors, who are looking to position themselves ahead of each new wave of development. More regulation is also pushed to protect intellectual property and privacy.

Whenever we see a hype-cycle, we ask:  how’d we get to this point, and where are we going?

Artificial intelligence is the development of computer systems performing tasks that typically require human intelligence. For example, visual perception, speech recognition, decision-making, and translation between languages. It’s a combination of computer science as applied to data-sets to enable problem solving. Generative AI is an algorithm that can create new content, including audio, code, images, text, simulations, and video. The key to generative AI is extensive language modeling that can analyze and learn from natural language interfaces, partnered with media that can procure natural language inputs, with AI core foundation modeling and infrastructure that can analyze inputs to create generative outputs.

The phrase was coined in December 1997 when two computer scientists Sepp Hochreiter and Jurgen Schmidhuber, invented long short-term memory networks, which improve memory capacity in neural networks, allowing for pattern recognition in training data. In 2012, Alex Krizhevsky gave us AlexNet, a convolutional neural network trained on graphical processing units, breaking 75% accuracy in identifying images from a manually tagged database. While Google has led research and development in the field for over a decade, focused on a product called “BERT,” OpenAI formed and released its first generative pre-trained transformer in June 2018 called GPT, ushering in a new era of LLMs. A year later, Microsoft invested $1 billion into OpenAI, launching the modern day AI arms race, culminating in a new text-to-image model called Dall-E, and ChaptGPT, released in late 2022, which has been adopted at warp speed.

NVIDIA, Google, Microsoft, Meta, and even government agencies are leading this arms race and are locked in battle, each deploying billions of dollars to win. Microsoft announced a multiyear, multibillion-dollar investment in OpenAI and exclusive integration with its Azure system in January 2023. and Amazon have partnered on AWS. Google announced BARD and invested $300 million in Anthropic.

PitchBook recently released its report “Vertical Snapshot: Generative AI,” examining the venture capital trends, industry overview, and market landscape in this space. Below we look at the AI sub-verticals, technologies, and startups that are getting the most funding and why.

Natural-language user interface (LUI or NLUI) is a type of computer human interface where the user and the system communicate using natural, human language. So, users are communicating with a computer using their spoken language. Most of us use this daily on our phones or other devices (Siri, Alexa, etc). The largest areas of investment within this specific segment were chatbots, voicebots, and personal assistants, which captured $544.9 million in 2022 (59.6% of all dollars invested).

2D media is exactly as it sounds – any artwork that exits in two dimensions, such as paintings, drawings, or prints. AI can be used not only to create 2D media but also to convert 2D media into 3D media. In this area, investors seem to see the most possibility in avatars, video generation, and editing, grabbing 37.7% and 40.8% of total dollars invested, respectively.

AI Core and biotech also brought in impressive numbers. With PitchBook finding AI Core, which includes foundation model developers and infrastructure for model development, raised $5 billion between 2018 and 2022. Biotech startups utilizing generative techniques have also piqued the interest of venture capital investors, with $1.6 billion invested during the same period.

One fascinating finding of the PItchBook report is the predicted growth in generative AI due to the incredible enterprise applications of this technology. Natural language interfaces will likely be the primary catalyst of this growth. In fact, PitchBook “expects the market at a 32.0% CAGR to reach $98.1 billion by 2026.”

As we look forward, the costs of foundation model training are dropping. Custom hardware and accelerated software tools are making it cheaper to train new LLMs. Price points are accessible to startups. Meanwhile, chief information officers at larger corporations are pushing for digital transformation and AI adoption across the enterprise. According to a report published by MIT in September 2022, only IT, supply chain, and finance departments were gaining widespread adoption of AI, and only at the 40% level. We believe the launch of ChatGPT4 is driving adoption deeper into those functions and driving expansion into sales, marketing, product development, and human resources.

ChatGPT has gone viral, and end users and consumers are adopting it. While yesterday’s estimates of the market size and opportunity for AI software looked impossible to attain, it now looks understated.

This space has fantastic potential, and it will be interesting to see how it ultimately impacts industries across the board and becomes an even more significant part of our daily lives. One area that will be critical to watch is how lawmakers approach regulation and what kind of implications those regulations will have on this rapidly evolving technology. Although, as with most technology, regulation needs to catch up to advancement.

What are the guard rails of intellectual property? Who owns the content output if the inputs were copyrighted? Who is responsible for copyright infringement? What if the output is wrong and it drives tortious conduct? Who will bear the risk of liability? Will it be insured? How can you protect your image, likeness, speech, and images, and how can you enforce them?

It is a brave new world; we may only be in the early innings of a new technology revolution. For startups looking for funding, we believe the keys to success will lie in foundational technology combined with unique algorithms that have some demonstrated utility and traction in at least one vertical but with applications across verticals. While the banking crisis may have interrupted funding flows for a limited period, the hype-cycle in generative AI has momentum with staying power.

Author Louis Lehot

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