Abstract: AI Startup

[Disclaimer ‘These Views Are My Own’]

Since March 2023, with the rise of ChatGPT, I have participated in discussions about AI startups with some of my friends. I had the opportunity to offer some opinions of my own. Unfortunately, from September 2023 to January 2024, I didn’t have the bandwidth to work on this, as I was too busy with my work to take on any side projects.

Beginning this February, I restarted discussing startup ideas with others as a way to refresh my mind. After talking with many people who shared their ideas and experiences of starting a startup, I gained more insights into what an AI startup is like, the different startups out there, and their interests.

1. My understanding of different types of AI startup

My understanding of the different types of startups is as follows: (1) Foundation model-related, (2) Ecosystem-related, (3) Infrastructure-related, (4) Application-focused.

(1) Regarding foundation models, startups such as OpenAI, Anthropic, Cohere, Together AI, Eleven Labs, Mistral, and Voyage AI are examples of companies in this category. They are building foundational pre-trained models, including (multimodal) LLM architecture, embedding models, and reranking models. Additionally, they provide or will offer customers the flexibility to fine-tune their models using customer-specific data. Competing in this space requires significant domain expertise and funding since training pre-trained models demands extensive GPU resources.

(2) Companies like Hugging Face, Langchain, and LambdaIndex represent open-source community ecosystem startups. They are creating a paradigm for developers to develop their applications more efficiently. If we consider LLMs as operating systems and AI-powered applications as software, these ecosystem companies could position themselves as programming languages like Python or programming frameworks like Django.

(3) As for infrastructure-related companies, there are numerous firms that build platforms for vector database, efficient training, model deployment, and data labeling. Examples include Pinecone, Chroma, Databricks, Anyscale, Scale.ai, Snorkel, and many other fine-tuning platforms and ML infrastructure/MLOps automation companies.

(4) Speaking of application-focused startups, there is a vast array of AI startups in this space, many of which are consumer products. For instance, many are developing chatbots, voice assistants, and various types of AI copilots and agents. These AI agents are used to enhance user productivity, automate selected workflows by users or the agents themselves, and assist users in complex workflows. (I am working on a post dedicated to AI agents and autonomous systems, so stay tuned.)

A personal AI agent is one of the most straightforward ideas when thinking about AI agents. However, I feel this space will likely be dominated by operating system providers like Apple (iPhone, iPad, MacBook), Microsoft, and Google (Android). They can integrate AI agents into the operating system, leveraging direct access to all data. Apple has a unique position here; if they continue optimizing inference on M3 chips or optimizing the M3 chips themselves, they could potentially develop the first such AI agent integrated seamlessly on phones or laptops.

Many startups in this category aim to build specific AI agent for a specific domain. Entrepreneurs in this space are more diverse than in others. For example, many attorneys want to build AI attorney-facing customers and attorney copilots/chatbots to assist attorneys. They believe they have unique positions in this space due to their domain expertise and the apparent barriers that prevent software engineers without industry experience from building such tools. However, they typically define the workflows and such domain-specific AI agents themselves, using LLM for function calls or to implement a series of function calls. At the same time, I would say there are still many more startups focusing on building AI agents as developer tools, since it’s straightforward for developers to think about those ideas to facilitate their day-to-day work. For example, automatic code debuggers, automatic code reviewers, coding copilots (there are already many out there like Github Copilot and Codewhisper), and automatic cluster management, etc.

(5) Another type of application is B2B products, which enable companies in category (4) to better build customer-facing products.

[To Be Continued]