Smarter models, sharper founders: growth investing in the AI era
Short Briefings on Long Term Thinking - Baillie Gifford
Building an AI research team and early aims
Kyle explains forming an internal machine-learning team in 2016 to explore AI's role in long-term investing.
With developments in generative AI progressing at such a furious pace, how can investors cut through the noise to identify the companies that will really matter? Baillie Gifford’s Kyle McEnery shares his approach to meeting the entrepreneurs building the future – including his encounters with AppLovin, Anthropic, NVIDIA, Roblox and Reddit.
Background:
Kyle McEnery is an investment manager in our Long Term Global Growth Team (LTGG) and previously led Baillie Gifford’s Artificial Intelligence Research Project.
In this conversation, he tells host Leo Kelion why AI’s ever-increasing capabilities make this one of the most exciting times to be a growth investor, and how leadership and culture act as signals in the noise to help identify companies with the greatest long-term growth potential.
In addition to discussing which of the firms enabling and using today’s language-based ‘frontier’ AI models are leading the pack, he explains how efforts to understand and simulate real-world physics could unlock further progress.
Portfolio companies discussed include:
Anthropic – developer of the Claude AI models, which excel at coding, among other tasks.
NVIDIA – the semiconductors firm whose accelerator chips are powering many of the advances in generative AI.
Roblox – the video games platform whose Cube 3D technology allows creators to build objects and environments out of text-based descriptions.
AppLovin – the ad-tech company whose AI-first strategy keeps the business lean and nimble.
Reddit – the online discussion forum, whose authentic human conversations are gaining in value as a counterpoint to AI-generated output.
Resources:
AI and the future of everything: a long-term perspective
Anthropic: why we are backing the AI frontrunner
Long Term Global Growth Strategy (institutional investors only)
LTGG philosophy and process (institutional investors only)
Private companies: from Anthropic to Zetwerk
Short Briefings on Long Term Thinking hub
Companies mentioned include:
Timecodes:
00:00 Introduction – Dartmouth College’s artificial intelligence workshop
01:50 From quantum to AI via asset management
02:50 Creating and then culling a machine-learning initiative
08:05 ChatGPT’s wake-up call
10:35 Exceptional companies at the dawn of generative AI
12:10 Anthropic’s appeal to business customers
14:55 A winner-takes-all opportunity?
17:05 Dario Amodei and the scaling laws
19:10 NVIDIA’s foundational role in neural networks
22:55 Making video game items in Roblox with AI
25:00 AppLovin – a company built for the next era
26:55 Reddit’s valuable conversational communities
29:35 World models, spatial AI and the physical world
32:35 Staying open-minded and humble
33:35 Book choice
Glossary of terms (in order of mention):
Generative AI:
AI systems that create new content such as text, images or code rather than just analysing data.
Machine learning:
AI techniques where systems learn patterns from data rather than being explicitly programmed.
End-to-end, systematic (investment strategy):
Fully automated, with decisions made by predefined rules rather than human judgement.
Agentic AI:
AI systems that can plan and carry out tasks autonomously rather than just responding to prompts.
R&D:
Research and development.
GPT:
OpenAI’s models, which power its ChatGPT chatbot.
Natural language processing:
AI that enables computers to understand and generate human language.
Token:
A chunk of text, such as a word or part of a word, used by language models.
Foundation models:
Large AI models that can handle a wide variety of tasks.
Know your customer (KYC):
Financial checks used by banks to verify customers’ identities and risks.
Scaling laws:
The idea that AI performance improves predictably as models, data and computing power increase.
Compute:
The processing power required to train and run AI models.
Jevons’ paradox:
The counterintuitive idea that efficiency gains can increase, rather than reduce, overall usage.
CUDA:
NVIDIA’s software platform for programming its chips for high-performance computing.
Jensen:
Jensen Huang, NVIDIA’s co-founder and chief executive.
Metaverse:
Shared virtual worlds where people interact, create and play online.
Large language models (LLMs):
AI systems trained on vast amounts of text to understand and generate language.
Multimodal models:
AI systems that can process multiple types of data, such as text, images and video.
World models:
AI systems that learn how the physical world works in order to predict and simulate it.
Embodied AI:
AI that learns through physical interaction with the real world, such as robots or vehicles.
Imitation learning:
Training AI by having it copy actions demonstrated by humans.
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