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Generative AI

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Introduction to Generative AI (GenAI)

AI is nothing new. For decades it has been in use in data-driven and analytical workflows, with increasing sophistication. But creativity and ideation were considered human skills, far from the capabilities of artificial intelligence.

The emergence of Generative AI (GenAI) and programs such as StableDiffusion and ChatGPT has turned this assumption on its head.

GenAI is an emerging frontier of AI, which uses Large Language Models (LLMs) trained on large data sets of content media (text, images, audio, video) to create new text, audio, images and more.

During a rapid emergence, Generative AI startups have attracted huge funding from investors, with over $25B in funding in 2023 alone. And 2024 is set to reach new heights with already $18.8B raised in just 5 months.

The US is the clear leader in Generative AI VC funding, even when looking beyond OpenAI.

GenAI immediately sent shockwaves in adjacent markets.

NVIDIA has surged to a $2.7 trillion market cap, a 750% increase since the start of 2023 and becoming the 3rd most valuable company globally. While companies such as Chegg (education tutoring) lost over 2/3 due to their business model being disrupted by GenAI.

Median round sizes

Generative AI startups are showing a significant premium at every stage, from Seed to Series C+.

For instance, in 2024 their Seed and Series A rounds are more than 80% larger than the average for tech startups. Notably instead, AI only shows a small premium versus the average tech startup.

VC by stage

Below we break venture capital into three distinct stages:

  • Startup stage ($0-15M rounds)
  • Breakout stage ($15-100M rounds)
  • Scaleup ($100M+ rounds).

This provides more consistent and timeless segmentation of the startup & venture capital landscape (more so than self-reported round labeling, which are applied inconsistently, especially between business cycles).

Early stage

Explore early-stage funding rounds for Gen AI startups globally here on the platform.

Breakout stage

Explore breakout stage funding rounds for Gen AI startups globally here on the platform.

Scaleup stage

Explore late stage funding rounds in to Gen AI startups globally here on the platform.

Key segments

Model makers

Model makers have raised over 50% of GenAI funding, followed by applications.

Explore on the interactive landscape below our own mapping of Generative AI startups on the platform.

While OpenAI leads the pack in terms of funding for large language models (LLMs), other players like Anthropic,, Inflection, Moonshot AI, Mistral AI, Aleph Alpha, Adept, and Cohere are also well-funded.

This significant investment highlights the high training and deployment costs associated with these complex general models.

Verticalized model makers are starting to emerge, such as, which came out of stealth with a $50M seed round for its health-focused LLM. Industries poised for specifically developed LLMs include health, fintech, and legal tech.

Explore 70+ foundational generative AI models makers on the platform.


Applications are the second most funded segment of Generative AI after model makers. Use cases range across all media types (text, image, video, speech/audio/music, code, and 3d assets).

Most applications have been built around text, such as copywriting, customer relations assistants/chatbots and knowledge & search. Other notable segments include code generation, image generation, speech generation and game design.

Applications are split between those built on proprietary models and applications built on third-party models.

Most of the applications are built on third-party models, such as Jasper and Typeface. However, several startups are building applications based on their proprietary GenAI models. Examples include, Runaway and Descript.

Building on proprietary GenAI models can provide a hedge against competition as applications will likely take advantage of gathered data and user interaction to fine-tune proprietary models. Others may build layers of model fine-tuning on top of third-party models.

Explore 800+ GenAI application startups on the platform.

Operation layer

The huge increase in GenAI usage across multiple use cases, both in consumer and enterprise adoption, has created the need for dedicated infrastructure from prompt engineering to MLops (training, deployment, optimization and monitoring) to data and embedding.

Some of these solutions are being added as add-ons on previous MLops offerings, such as in the case of Scale AI.

Others respond to new GenAI needs, such as Vector databases, which already raised an all-time-high $210M in 2023, led by Pinecone and Weaviate.

Explore 90+ GenAI operations startups on the platform.


Computing layer

The rise in computing demand for generative AI is bringing huge attention to dedicated chips, Language processing units (LPUs) and tensor processor units (TPUs), such as Groq, as well as GPU data centre clusters (such as CoreWeave, Voltage Park and Lambda).

Explore 10+ GenAI operations startups, as well as 100+ AI chips startups.

Top Investors

Many top-tier investors have been building up their Generative AI portfolios though notably, Andreessen Horowitz and Sequoia have made nearly 50% more Generative AI investments than anyone else so far.

Ycombinator is by a large margin the most active accelerator for GenAI startups, with over 100 startups supported, including GenAI startups such as OpenAI, Jasper and Replit.

Top countries and cities

Since 2019, the US has been the top country globally for Generative AI funding, with a large lead, followed by China and the UK.
Israel, Canada, France and Germany have also all raised over $1B in GenAI VC funding.

Top cities

The Bay Area has been the main hub for Generative AI, attracting over $42B in just over 5 years.

Even without OpenAI's $12.3B funding, the Bay Area still attracted 7x the funding of the next hub - New York. London is the leading global hub outside the US.

Beijing, Paris and Tel Aviv have also attracted over $1B in GenAI VC funding.

AI chips: the pillars of GenAI

The GenAI wave is increasing demand for AI chips and processors for training and deploying LLMs at scale. NVIDIA has surged to a $2.7 trillion market cap, a 750% increase since the start of 2023, and has become the third most valuable company globally (NVIDIA is the leader in AI chips)

However, even Nvidia is two to three months behind on new order fulfilment for cloud server chips. Training costs and computing power availability are becoming a constraint for startups and companies wanting to train and deploy LLMs.

Globally, AI chips funding started ramping up in 2017-2018 and peaked in 2021.

However, 2022 was the most active year ever by number of rounds, showing some new early-stage innovation coming up.

China has been by far the leading geography for AI Chips investments.

However, despite the clear unmet and growing demand for AI chips and the limitations in semiconductor computation capacity, AI chip startups have, in some cases, not yet lived up to their promise.

Once heralded to great excitement, UK AI chip startup Graphcore had their valuation written down to zero by Sequoia Capital in April 2023, after having lost a major deal with Microsoft in late 2022. Graphcore has raised $682M in total VC funding and was valued at $2.8B in Dec 2020.

Also, most Big Tech (Tesla, Microsoft, Meta, Alphabet and Apple) are trying to develop their own AI hardware to reduce their dependence on NVIDIA and improve AI profitability. For more on Big Tech (Magnificent 7) strategies and CV activity see our full report.






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