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Who’s making money with GenAI?

🌈 Abstract

The article discusses the growing opportunities and trends in the generative AI (GenAI) market, highlighting the revenue and growth potential for various stakeholders, including enterprises, AI startups, and AI infrastructure providers.

🙋 Q&A

[01] Who's making money with GenAI?

1. What are the key highlights regarding the revenue and growth potential in the GenAI market?

  • Accenture booked over $600M this quarter ($2.4B annualized) in Generative AI, which is more than the full-year 2023 revenue of OpenAI ($1.6B).
  • Most enterprises see promising early results from GenAI experiments and plan to increase their spend anywhere from 2x to 5x in 2024 to support deploying more workloads to production.
  • This is a massive opportunity for founders building AI startups who:
    • Build for enterprises' AI-centric strategy while anticipating their pain points
    • Build Productized services which capture this new wave of investment

2. What are the key factors that enterprises care about when it comes to GenAI?

  • Control and Customizability are why enterprises care about open-source and self-hosted large language models (LLMs).
  • Hugging Face has consolidated its place in this ecosystem, and Llama became a de facto standard for LLMs, as Meta was the first to make a strong LLM source available.
  • Competitors are following suit, with Google, Apple, Mistral, Stability, Databricks and others open-sourcing their models.

3. What are the primary customer-facing use cases that enterprises are looking at for GenAI?

  • Customer Support Chatbots
  • Recommendation systems (with chat interface refining recommendations based on user input)

4. What are the key benefits that enterprises are seeing from GenAI?

  • The biggest impact has been productivity gains - "A Dollar Saved is A Dollar Earned"

5. How are enterprises building GenAI solutions?

  • Internal teams building solutions
  • Consultancies leveraging productized services
  • Product companies selling one-fit solutions

6. How will the role of workflows change with the advent of AI agents?

  • Most AI apps today are workflow apps, which execute a sequence of actions to get to the final state.
  • Now, with AI agents that can execute workflows (and soon will be able to execute fairly complex workflows flawlessly), there will be no need for SaaS apps to design workflows, as AI agents will deliver the desired end results.

[02] What are the key competencies and opportunities for startups in the GenAI market?

1. What are the three core competencies needed for a strong moat in the GenAI market?

  • Product innovation
  • Operational Excellence
  • Customer Intimacy

2. What are the key opportunities for startups in the GenAI market?

  • LLM infrastructure companies like Langchain, Pinecone or LlamaIndex have a good chance, as enterprises spent $1.1B+ on the LLM infra stack in 2023, making it the largest new market in generative AI.
  • Companies with unique data that is hard to replicate, like Character AI, can also do well.
  • Startups building GPT wrapper products, like FormulaBot and SiteGPT, are good early examples.
  • In the mobile app segment, apps that provide companionship like Character AI are doing phenomenally.
  • Overall, ChatGPT takes the win as the all-rounder tool.

3. What are the recommendations for startups based on their resources and experience?

  • If you have limited funds, you should bet on AI services, then Productized AI Services and Consultancy.
  • If you have funds and experience, validate your idea and build a strong product on a specific use case with the end customer in mind.
  • If money and talent aren't your problems, then invest heavily in R&D, get hold of very high-quality data, hoard compute, and explore exotic and new architectures both in software and hardware.
Shared by Daniel Chen ·
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