Artificial intelligence has received the lion’s share of venture investors’ attention and capital since OpenAI launched ChatGPT in November 2022. Considering the following data points:
75% of the startups in YCombinator’s Summer 2024 cohort (156 out of 208) were working on AI-related products.
In Q2 2024, 49% of all venture capital went to artificial intelligence and machine learning startups, up from 29% in Q2 2022.
In 2020, the median pre-money valuation for early-stage AI, SaaS, and fintech companies was $25 million, $27 million, and $28 million, respectively. In 2024, those figures are $70 million, $46 million, and $50 million.
OpenAI, which was unprofitable and on pace to generate $4 billion in annual revenue, was able to raise new capital at a $157 billion valuation in October 2024 (implying a 39x revenue multiple).
However, even though the spotlight currently rests on model companies like OpenAI and Anthropic, and AI-native companies like search engine Perplexity and transcription tool Descript, the number of “non-AI” companies that will be impacted by artificial intelligence far outnumbers companies whose core business is AI-focused. We collectively refer to the impact of artificial intelligence on these other companies as “the long tail of AI.”
The ways that companies within this long tail have used AI are as diverse as the companies themselves. For example, did you know that Walmart has developed its own AI models to improve the customer shopping experience? Or that Boston Consulting Group gave all of its employees access to ChatGPT after seeing that the chatbot gave consultants a 40% performance boost on creative tasks?
To explore how non-AI companies are integrating AI, we put together a deep dive that we published this week on The Long Tail of AI. Because AI is developing and changing so quickly, we created a four-piece framework that categorizes different AI integration strategies based on their resource intensity:
Building an independent, proprietary model: this is the most resource-intensive way to leverage artificial intelligence and is generally reserved for companies that have large, novel data sources from which they can derive unique insights and the human and financial capital needed to train a new model from scratch.
Leveraging proprietary closed-source models: building on closed-source models such as OpenAI’s GPT models or Anthropic’s Claude, which are easy to access via API, have been trained on billions of parameters, and can generate accurate, detailed outputs across a variety of fields, from coding to customer service.
Open-source models: Models like Mistral or Meta’s Llama, are also powerful tools, with Llama 3.1 being trained on 405 billion parameters. Unlike closed-source LLMs, however, open-source models provide companies with increased transparency and flexibility, as model weights can be adjusted to meet specific customer needs.
Third-party AI tools, such as ChatGPT, are the easiest to integrate as customers can simply pay to use a fully developed tool instead of investing in building or adjusting models internally.
Today’s deep dive uses lessons and examples from 14 companies, from retail giant Walmart to private browser startup Brave, to explore how different businesses are thinking about their AI integration strategies today. For more on this, read our full breakdown of how non-AI companies are using AI.
