Imagine: your personal AI agent notifies you that a hair dryer you’ve been eyeing is now on sale. The product page highlights benefits tailored to your curly hair, while the agent confirms it will arrive before your upcoming trip.

With your approval, the agent handles the purchase through your secure wallet. Later, it proactively suggests complementary hair care products for the summer season.

This world of autonomous commerce isn’t as far off as it seems. Tech and e-commerce leaders — including OpenAI, Nvidia, Amazon, Walmart, Google, and Apple — are already building AI systems that are steps away from conducting transactions. 

AI agents will impact each stage of the customer journey, streamlining the path to purchase and fundamentally transforming how businesses build relationships with consumers and drive loyalty.

We use CB Insights data on early-stage fundraising, public companies, and industry partnerships to analyze how generative AI — especially AI agents — is transforming the customer journey.

In the 11-page report, we cover 3 predictions that emerged from our analysis: 

1. First-party transaction data will shape the future of AI-driven personalization. As personalization becomes more sophisticated at the awareness and consideration stages, companies with direct access to first-party data will have an edge.
2. Direct-to-agent (D2A) commerce will kill traditional loyalty. With AI agents handling browsing and shopping, traditional loyalty programs will lose effectiveness as agents optimize shopping across a select group of merchants.
3. A few AI agents will own the customer relationship. Companies like Amazon, Google, and Apple — with critical distribution and financial services infrastructure — are well-positioned in commerce.

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• Generative AI — e-commerce search
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But AI and blockchain solutions are driving innovation to create new revenue streams and stoke the existing ones. Early-stage deals in those spaces helped drive a rally in funding and dealmaking in gaming in 2024. Annual funding nearly tripled to $3.2B across 317 deals, with early-stage companies securing 77% of investments — up from 70% in 2023. 

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Open-source AI is drawing unprecedented attention from developers and enterprises, driven in part by DeepSeek’s recent model releases.

Cost pressures and demands to improve the performance of generative AI applications are driving enterprise interest in the ecosystem as organizations seek more flexible and cost-effective alternatives to proprietary solutions. 

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January was a busy month for the generative AI space, headlined by DeepSeek‘s R1 model launch — matching OpenAI’s o1 model capabilities at just 5-10% of the cost, while open-sourcing the technology.

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However, global CVC deal count dropped to its lowest level since 2018 as CVCs become more selective.

Download the full report to access comprehensive data and charts on the evolving state of CVC across sectors, geographies, and more.

Key takeaways from the report include:

• CVC-backed funding grows, deal activity slows. Global CVC-backed funding increased 20% YoY to $65.9B, but deal count fell to 3,434, the lowest level since 2018. All major regions saw deal volume declines, with Europe dropping the most at 10% YoY.
• CVCs are all in on AI. AI startups captured 37% of CVC-backed funding and 21% of deals in 2024 — both record highs. Counter to the broader decline in deals, CVCs ratcheted up AI dealmaking by 13% YoY as they race to secure footholds in the space before competitors gain an insurmountable edge.
• The flight to quality continues. Among deals with CVC participation, the annual average deal size hit $27.3M in 2024, tied for the second highest ever. Amid fewer deals, CVCs are increasingly aggressive when they do decide to invest.
• Early-stage deals dominate. Early-stage rounds comprised 65% of 2024 CVC-backed deals, tied for the highest share in over a decade. Biotech startups made up half of the top 20 early-stage deals.
• CVC-backed funding plummets in Asia. In 2024, Asia’s CVC-backed funding dropped 34% YoY to $7B — the lowest level since 2016. China is leading the decline, with no quarter in 2024 exceeding $0.5B in funding. CVCs remain wary of investing in the country’s private sector.

We dive into the trends below.

CVC-backed funding grows, deal activity slows

Global CVC-backed funding reached $65.9B, a 20% YoY increase. The US was the main driver, increasing 39% YoY to $42.8B. Europe also saw CVC-backed funding grow 18% to $12.3B, while Asia declined 34% to $7B.

$100M+ mega-rounds also contributed to the rise, ticking up 21% YoY to 141 deals worth over $32B in funding.

Meanwhile, deal count continued its decline, as both annual (3,434 in 2024) and quarterly (806 in Q4’24) totals reached their lowest levels in 6 years.

Annual deal volume fell by at least 6% YoY across each major region — the US, Asia, and Europe — with Europe experiencing the largest decline at 10%.

However, Japan-based CVC deal volume remains near peak levels, suggesting a more resilient CVC culture compared to other nations. Two of the three most active CVCs in Q4’24 are based in Japan: Mitsubishi UFJ Capital (21 company investments) and SMBC Venture Capital (15).

Track recent activity from 1,000+ CVCs

CVCs are all in on AI

AI is driving CVC investment activity, much like the broader venture landscape. In 2024, AI startups captured 37% of CVC-backed funding and 21% of deals, both record highs.

In Q4’24, the biggest CVC-backed rounds went primarily to AI companies. These include:

• AI search startup Perplexity’s $500M Series C, backed by Nvidia’s NVentures 
• Coding AI copilot Poolside’s $500M Series B, backed by the venture arms for Citigroup, HSBC, Capital One, LG, eBay, and Nvidia
• AI computing processor developer Lightmatter’s $400M Series D, backed by GV

CVCs are also investing in the energy companies powering the AI boom, such as Intersect Power, which raised the largest round at $800M (backed by GV).

Expect the trend to continue into 2025, as emerging AI markets mature further, such as AI agents & copilots for enterprise and industrial use cases; AI solutions for e-commerce, finance, and defense; and the computing hardware necessary to power these technologies.

The flight to quality continues

In 2024, the annual average deal size with CVC participation reached $27.3M, a 34% YoY increase and tied for the second highest level on record, exceeded only by the low-interest-rate environment of 2021.​

Median deal size also increased, though only by 8% to $8.6M.

Even though the number of CVC-backed deals declined in 2024, the increase in average annual deal size reflects a focus on companies with strong growth prospects. CVCs are prioritizing quality and committing more funds to a select group of high-potential investments.

Early-stage deals dominate

Early-stage rounds (seed/angel and Series A) made up 65% of CVC-backed deals in 2024, tied for the highest recorded level in more than a decade.​

In Q4’24, biotech companies were the early-stage fundraising leaders, accounting for 10 of the 20 largest early-stage deals. Biotech players City Therapeutics, Axonis, and Trace Neuroscience all raised $100M+ Series A rounds, with City Therapeutics and Axonis notably receiving investment from the venture arms of Regeneron and Merck, respectively.

Among all early-stage CVC-backed companies, the largest round went to Physical Intelligence, a startup focused on using AI to improve robots and other devices. Physical Intelligence raised a $400M Series A with investment from OpenAI Startup Fund.

Explore CVCs’ top early-stage company targets in Q4

CVC-backed funding plummets in Asia

Asia’s CVC-backed funding continued its downward trend in 2024, decreasing 34% YoY to $7B.

China was the main driver, with CVC-backed funding coming in at $0.5B or less every quarter in 2024.​ CVCs remain wary of investing in startups in the nation, which faces a variety of economic challenges, including a prolonged real estate slump, cautious consumer spending, strained government finances, and weakened private sector activity amid policy crackdowns.

In Japan, on the other hand, CVC activity remains robust. In 2024, funding with CVC participation ($1.7B) remained on par with the year prior, while deals (502) actually increased by 11%.

MORE VENTURE RESEARCH FROM CB INSIGHTS

• State of Venture 2024 Report
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Venture funding surged past the $100B mark for the first time as AI infrastructure players pulled in billion-dollar investments. A wave of M&A deals and rapidly scaling AI unicorns further underscored the tech’s momentum.

Download the full report to access comprehensive data and charts on the evolving state of AI across exits, top investors, geographies, and more.

Key takeaways include: 

• Massive deals drive AI funding boom. AI funding hit a record $100.4B in 2024, with mega-rounds accounting for the largest share of funding we’ve tracked to date (69%) — reflecting the high costs of AI development. Quarterly funding surged to $43.8B in Q4’24, driven by billion-dollar investments in model and infrastructure players. At the same time, nearly 3 in 4 AI deals (74%) remain early-stage as investors look to get in on the ground floor of the AI opportunity. 
• Industry tech sectors lose ground in AI deals. Vertical tech areas like fintech, digital health, and retail tech are securing a smaller percentage of overall AI deals (declining from a collective 38% in 2019 to 24% in 2024). The data suggests that companies focused on infrastructure and horizontal AI applications are drawing greater investor interest amid generative AI’s rise.
• Outside of the US, Europe fields high-potential AI startup regions. While the US dominated AI funding (76%) and deals (49%) in 2024, countries in Europe show strong potential in AI development based on CB Insights Mosaic startup health scores. Israel leads with the highest median Mosaic score (700) among AI companies raising funding. 
• AI M&A activity maintains momentum. The AI acquisition wave remained strong in 2024, with 384 exits nearly matching 2023’s record of 397. Europe-based startups represented over a third of M&A activity, cementing a 4-year streak of rising acquisitions among the region’s startups. 
• AI startups race to $1B+ valuations despite early market maturity. The 32 new AI unicorns in 2024 represented nearly half of all new unicorns. However, AI unicorns haven’t built as robust of a commercial network as non-AI unicorns, per CB Insights Commercial Maturity scores, indicating their valuations are based more on potential than proven business models at this stage.
• Tech leaders embed themselves deeper in the AI ecosystem. Major tech companies and chipmakers led corporate VC activity in AI during Q4’24, with Google (GV), Nvidia (NVentures), Qualcomm (Qualcomm Ventures), and Microsoft (M12) being the most active investors. This reflects the strategic importance of securing access to promising startups while providing them with essential technical infrastructure.

We dive into the trends below.

For more on key shifts in the AI landscape in 2025, check out this report on the implications of DeepSeek’s rise.

Massive deals drive AI funding boom

Globally, private AI companies raised a record $100.4B in 2024. At the quarterly level, funding soared to a record $43.8B in Q4’24, or over 2.5x the prior quarter’s total. 

The funding increase is largely explained by a wave of massive deals: mega-rounds ($100M+ deals) accounted for 80% of Q4’24 dollars and 69% of AI funding in 2024 overall.

The year featured 13 $1B+ deals, the majority of which went to AI model and infrastructure players. OpenAI, xAI, and Anthropic raised 4 out of the 5 largest rounds in 2024 as they burned through cash to fund the development of frontier models. 

Overall, the concentration of funding in mega-rounds reflects the high costs of AI development across hardware, staffing, and energy needs — and widespread investor enthusiasm around the AI opportunity. 

But that opportunity isn’t limited to the largest players: nearly 3 in 4 AI deals (74%) were early-stage in 2024. The share of early-stage AI deals has trended upward since 2021 (67%) as investors look to ride the next major wave of value creation in tech.

CBI Customers: Track 15,000+ AI companies

Industry tech sectors lose ground in AI deals

Major tech sectors — fintech, digital health, and retail tech — are making up a smaller percentage of AI deals.

While the overall annual AI deal count has stayed steady above 4,000 since 2021, dealmaking in sectors like digital health and fintech has declined to multi-year lows. So, even as AI companies make up a greater share of the deals that do happen in these industries, the gains haven’t been enough to register in the broader AI landscape.

The data suggests that, amid generative AI’s ascendancy, AI companies targeting infrastructure and horizontal applications are drawing a greater share of deals. 

With billions of dollars flowing to the model/infra layer as well, investors appear to be betting that the economic benefits of the latest AI boom will accrue to the builders.  

Outside of the US, Europe fields high-potential AI startup regions

Although US-based companies captured 76% of AI funding in 2024, deal activity was more distributed across the globe. US AI startups accounted for 49% of deals, followed by Asia (23.2%) and Europe (22.9%). 

Comparing median CB Insights Mosaic scores (a measure of private tech company health and growth potential on a 0–1,000 scale) for AI companies that raised equity funding in 2024 highlights promising regional hubs. 

Explore 500+ high-potential AI startups

European countries dominate the top 10 countries by Mosaic score (outside of the US). Israel, which has a strong technical talent pool and established startup culture, leads the pack with a median Mosaic score of 700.

Overall activity on the continent is dominated by early-stage deals, which accounted for 81% of deals to Europe-based startups in 2024, a 7-year high.

The European Union indicated in November that scaling startups is a top priority, pointing to the importance of increased late-stage private investment in remaining competitive on the global stage.

AI M&A activity maintains momentum

The AI M&A wave is in full force, with 2024’s 384 exits nearly reaching the previous year’s record-high 397.

Acquisitions of Europe-based startups accounted for over a third of AI M&A activity in 2024. Among the global regions we track, Europe is the only one that has seen annual AI acquisitions climb for 4 consecutive years. Although the US did see a bigger uptick YoY (16%) in 2024, posting 188 deals. 

In Europe, UK-based AI startups led activity in 2024, with 32 M&A deals, followed by Germany (18), France (16), and Israel (12). 

Major US tech companies, including Nvidia, Advanced Micro Devices, and Salesforce, participated in some of the largest M&A deals of the year as they embedded AI across their offerings.

Search for recently funded AI companies with high M&A probabilities

AI startups race to $1B+ valuations despite early market maturity 

AI now dominates new unicorn creation. The 32 new AI unicorns in 2024 accounted for nearly half of all companies passing the $1B+ valuation threshold during the year. 

These AI startups are hitting unicorn status with much smaller teams and at much faster rates than non-AI startups: 203 vs. 414 employees at the median, and 2 years vs. 9 years at the median. 

CBI Customers: Dive into data on every unicorn

These trends reflect the current AI hype — investors are placing big early bets on AI potential. Many of these unicorns are still proving out sustainable revenue models. We can see this clearly in CB Insights Commercial Maturity scores. More than half of the AI unicorns born in 2024 are at the validating/deploying stages of development, while non-AI new unicorns mostly had to get to at least the scaling stage before earning their unicorn status.

Tech leaders embed themselves deeper in the AI ecosystem

In Q4’24, the top corporate VCs in AI (by number of companies backed) were led by a string of notable names: Google (GV), Nvidia (NVentures), Qualcomm (Qualcomm Ventures), and Microsoft (M12). 

As enterprises rush to harness AI’s potential, big tech, chipmakers, and other enterprise tech players are building their exposure to promising companies along the AI value chain.

Meanwhile, startups are linking up with these players to not only secure funding for capital-intensive AI development but also access critical cloud infrastructure and chips.

MORE AI RESEARCH FROM CB INSIGHTS

• What DeepSeek’s model releases mean for the future of AI
• Here’s how leading strategy teams are successfully driving generative AI adoption in their organizations
• The foundation model divide: Mapping the future of open vs. closed AI development
• 15 tech trends to watch closely in 2025
• Inside the AI drug discovery arms race: Record M&A activity, a biologics funding spree, and more
• The industrial AI agents & copilots market map

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The AI company, which emerged from Liang Wenfeng’s hedge fund High-Flyer, released an open-source reasoning model (named R1) in January 2025 that rivals the performance of OpenAI’s o1 reasoning model.

DeepSeek says it trained its base model with limited chips and about $5.6M in computing power — a fraction of the $100M+ US rivals have spent training similar models — thanks to some clever techniques.  

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But only 32% of strategy leaders report active genAI deployments at their organizations.

To identify pain points and success stories for genAI adoption, we surveyed 50 senior strategy leaders working at companies across major industries.

Download the full report to understand how leading strategy teams navigate genAI adoption, their key challenges, and the tactics separating successful implementations from stalled initiatives.

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The divide between open-source and closed-source AI models is reshaping tech industry dynamics. 

Tech leaders have staked out clear positions: Meta and xAI are open-sourcing models like Llama 3.1 and Grok-1, while Google and OpenAI have largely walled off their systems. Investment flows are also split between both approaches. Since 2020, private open-source AI model developers have attracted $14.9B in venture funding, while closed-source developers have secured $37.5B — reflecting different bets on how AI innovation will unfold.

The core difference lies in access: closed-source approaches keep model details and weights proprietary, while open-source development makes these elements available so models can be more freely studied, run, and adapted.

Companies building generative AI applications must understand this evolving landscape as it has crucial implications for the infrastructure they adopt. Based on current trends, we expect:

1. Consolidation around frontier models: Closed-source models from players like OpenAI, Anthropic, and Google will dominate the market. Only tech giants like Meta, Nvidia, and Alibaba are likely to sustain the costs of developing open-source models that can compete on performance with proprietary ones. Frontier model training costs are growing 2.4x annually, driven by hardware, staffing, and energy needs, according to Epoch AI.
2. Revenue and investment gaps threaten open-source model developers’ viability: While burning cash, closed-source leaders like Anthropic and OpenAI lead the private market in funding, revenue, and commercial traction. Open-source developers face similar costs but struggle to generate revenue or attract capital investment ($14.9B vs. closed-source’s $37.5B since 2020). This suggests they will move to commercialize their closed models (e.g., Mistral AI) and/or pivot to smaller, specialized offerings (e.g., Aleph Alpha).   
3. Smaller models drive open-source adoption: Industry leaders, alongside a range of smaller players, are releasing smaller, specialized open-source models, as evidenced by Microsoft‘s Phi, Google’s Gemma, and Apple‘s OpenELM. This suggests a two-tier market for enterprises evaluating the landscape: closed-source frontier models for the most sophisticated applications and open-source smaller models for edge and specialized use cases.

Below, we use CB Insights data to map out the open-source and closed-source AI landscape. Our analysis focuses on foundation models — the powerful, general-purpose AI systems that form a critical infrastructure layer.

CB Insights customers can track every company mentioned in this analysis using this search. We used the Generative AI — large language model (LLM) developers and Generative AI — image generation market profiles to establish the private market landscape, focusing on companies that have received funding and are developing foundation models. 

Table of contents

Consolidation around frontier models

• Industry leaders are divided in their approaches
• Closed-source developers lead the private market in equity funding
• Performance gaps converge, with largest companies’ models topping leaderboard

Revenue and investment gaps threaten open-source model developers’ viability

• OpenAI dominates LLM adoption and revenue, followed by Anthropic
• Open-source’s path to revenue remains unclear
• Investors hedge their bets

Smaller models drive open-source adoption

• A wave of smaller foundation model players will move away from frontier model development
• Market bifurcation accelerates

Consolidation around frontier models

Industry leaders are divided in their approaches 

Many big tech companies — like Google and Apple — are releasing a combination of open and closed models, typically keeping their flagship models proprietary while releasing lighter-weight open models as an extension of their research efforts.

Meta and Nvidia, meanwhile, are also open-sourcing flagship models. 

Note: When developers “open-source” AI models, they do so on a spectrum, publicly disclosing some combination or element of the: model weights (the learned parameters of a neural network, crucial for the model’s performance and capabilities as they encapsulate the knowledge acquired during training), underlying source code, and original training data. Open-sourcing may also involve licensing the model for free commercial use.

Open-source proponents are preparing for an open-source future

Meta CEO Mark Zuckerberg wrote in July that “Meta is committed to open source AI,” with the belief that an open ecosystem will eventually become the standard. On earnings calls, Meta is the most active big tech company in terms of open-source mentions. 

At the same time, Zuckerberg acknowledged in April on Dwarkesh Patel’s podcast that the company will only continue open-sourcing “as long as it’s helping us.” 

In July 2024, Meta released the model weights for its latest Llama model family so developers can fine-tune the model (train it on custom data). However, the source code and model architecture remain unavailable, limiting full modification or analysis. Meanwhile, Nvidia released both the model weights and training code for its NVLM 1.0 family of large multimodal language models in September 2024. 

Closed-source proponents view revenue as crucial for top resources and talent

For example, Baidu CEO Robin Li said in an internal memo that open-source models “make little sense.” From a business perspective, he noted, “Being closed source allows us to make money, and only by making money can we attract computational resources and talent.”

Safety remains central to the debate

Critics of open-source AI models fear they will be misused by malicious actors to access harmful information (like how to build a bomb or write code for a cyber attack). They also raise national security concerns, with critics suggesting foreign actors’ ability to use open-source models to advance military applications (like weapons systems and intelligence tech) will undermine strategic advantages held by countries that currently lead in AI development. 

Closed models use techniques like Reinforcement Learning by Human Feedback (RLHF) during fine-tuning to limit the harmful content the model can produce. Open models, meanwhile, are more likely to be deployed without these safeguards. 

On the other hand, open-source AI proponents argue, as highlighted in Mozilla’s Joint Statement on AI Safety and Openness with 1,800+ signatories, that increasing access to foundation models will ultimately make them safer, thanks to increased transparency, scrutiny, and knowledge sharing. 

Closed-source developers lead the private market in equity funding

The private market is also split, with closed developers leading in equity funding. 

While both Mistral AI and xAI are proponents of open-source, both of their flagship models are currently closed. 

The cost to develop frontier models — taking into account hardware, staffing, and energy consumption costs — is growing 2.4x per year. This is driving the fundraising race. 

Performance gaps converge, with largest companies’ models topping leaderboard

Leading open-source models, like Meta’s largest Llama model, are making their way onto the MMLU leaderboard — a test that evaluates a language model’s knowledge and reasoning skills. The expanded version, MMLU-Pro, includes more challenging questions to assess advanced reasoning capabilities in AI models.

At the same time, proprietary models continue to outpace open-source ones by several months in terms of release dates. 

The leaderboard itself is dominated by the largest companies in both big tech and the private market, indicating market consolidation at the frontier level. 

At this stage, a16z partner Marc Andreessen has posited we could be approaching a “race to the bottom” — a future point where there are no moats for foundation models, and open-source performance is on par with closed-source. This has come into focus in recent months as frontier labs like OpenAI and Google have focused on smaller model development and other products (like agents) as performance gains slow and as release dates for the largest models (such as a potential GPT-5) get pushed back.

Below we look at how revenue and adoption gaps in the private market also point to increasing consolidation.

Revenue and investment gaps threaten open-source model developers’ viability in the private market

OpenAI dominates LLM adoption and revenue, followed by Anthropic

As LLM developers burn through cash, the focus has shifted to customer adoption — and revenue. 

Based on CB Insights business relationship data, OpenAI is far ahead of its peers in terms of its disclosed partnerships and client relationships. 

This business relationship analysis is limited to publicly disclosed partnership, client, and licensing agreements for pure-play model developers to highlight adoption trends. Relationships are not exhaustive and are directionally representative of trends across model developers’ partner and client relationships.

In terms of revenue, OpenAI leads, with projections of $3.7B in annual revenues for 2024 and $11.6B for 2025. However, it’s also been burning cash: the company projected midway through the year that it would lose $5B in 2024.

Open-source’s path to revenue remains unclear

While revenues for open-source model developers are not publicly available in most cases, reports suggest revenue generation is more limited — especially given the competition from Meta’s Llama.

The embattled Stability AI reportedly generated $8M in 2022 and less than $5M in the first quarter of 2024 (while losing over $30M). In June 2024, it secured an $80M funding deal that included the forgiveness of $100M in debts owed to cloud providers and other suppliers. 

Meanwhile, Mistral AI has an unclear path to revenue, per The Information reporting — it sells access to its API, and under 10% of its users pay for Mistral’s larger commercial models through partners. Most of its smaller, open-source models are free. 

Source: CB Insights — Mistral funding insight

Following the traditional approach to monetizing open-source businesses — building paid support offerings or tools (plugins, security, migration, apps on top) around the open-source core — some model developers are now building more enterprise capabilities into their platforms. 

For example, Databricks offers security and other paid support services around its open-source LLM, DBRX. Similarly, Aleph Alpha launched in August 2024 a “sovereign AI” platform designed to help corporations and governments deploy LLMs (not necessarily its own) with added control and transparency features to serve the European market. 

Investors hedge their bets

Most leading investors in private foundation model developers have backed companies developing both closed and open models.

Corporate investors figure heavily — Nvidia, Alibaba, and Microsoft, for example, have offered computing power and funds for development. These investments are aimed at feeding their core business focuses, such as AI chips and cloud computing. AWS, Azure, and Google Cloud all host both open and closed models.

Venture investors are taking sides:

• Coatue, the leading VC by unique companies backed, has called open source “the heartbeat of AI.” It’s taking a complementary approach: “We see open-source models as firmly having a place alongside proprietary ones.”
• a16z’s founders are proponents of open-source models, arguing that their transparency and accessibility will help ensure that AI is developed securely and ethically. In 2024, the two largest a16z-backed AI deals went to open-source LLM developers xAI and Mistral AI.
• Meanwhile, Founders Fund partner John Luttig has argued that the future of foundation models is closed-source. Khosla Ventures’ Vinod Khosla (a backer of OpenAI) also argues in favor of closed-source AI for safety reasons. 

The investor split reflects uncertainty over which ecosystem will dominate and where the greatest value creation will occur. The relative difference in funding totals ($14.9B in equity funding to open-source model developers vs. $37.5B to closed-source), as well as the data available on revenue, suggests that a closed approach for private developers appears poised to win out, especially given the most performant open models at this point are from big tech leaders.

Smaller models drive open-source adoption

A wave of smaller foundation model players will move away from frontier model development 

The conditions of a) high compute costs, b) limited moats, and c) competition from big tech have created a market ripe for a shake-up.

We’re seeing a wave of smaller foundation model players:

• Collapse into big tech: Adept, Inflection, and Character.AI have all essentially been “acqui-hired” by big tech companies, with founders and large portions of teams joining the acquirers. These deals reflect the high costs of model development, with licensing payments often directed to investors. 
• Paywall frontier models: Some open-source AI developers now sell access to premium models while keeping basic versions free — similar to strategies used by big tech. For example, Mistral AI’s flagship model Mistral Large is built for commercial use (not open-source) and is available on Azure in partnership with Microsoft.
• Focus on smaller, open-source models: Developers like Germany-based Aleph Alpha and Israel-based AI21 Labs have shifted in 2024 from competing on general-purpose LLMs to building lighter-weight, optimized models and related AI tools. These models are open-source, with paid services layered on top.

Market bifurcation accelerates

Based on these trends, the AI model market is splitting into two tiers:

• Frontier models are largely dominated by closed-source offerings from well-funded players (OpenAI, Anthropic, Google), which can sustain growing compute costs. Meta’s Llama remains the most notable open-source alternative.
• Smaller models, optimized for specific use cases or edge deployment, are supported by a growing open-source ecosystem. These small language models (SLMs) have fewer parameters than LLMs, making them cheaper to train and easier to run.

Industry leaders are releasing smaller, open-source models to advance research efforts and to promote edge applications: Google with Gemma, Microsoft with Phi, and Apple with OpenELM. 

For example, Microsoft highlighted in a recent earnings call: 

“We have also built the world’s most popular SLMs, which offer performance comparable to larger models but are small enough to run on a laptop or mobile device. Anker, Ashley, AT&T, EY, and Thomson Reuters, for example, are all already exploring how to use our SLM Phi for their applications.” — Satya Nadella, CEO of Microsoft, Q2’24 Earnings Call  

Meanwhile, of the 11 private SLM development platforms we identified, roughly half are already in the process of deploying their products.

Smaller, open models are also gaining traction in sectors like financial services and healthcare, where keeping sensitive data on-premises can be a need.

For example, a VP of machine learning at a health insurance company needed a solution for training healthcare models and looked to Hugging Face’s open-source library. In our May 2024 conversation, the buyer highlighted the opportunity of SLMs for their use case:

For now, it’s clear a hybrid approach is winning with enterprises: they will look to closed-source frontier models for the most sophisticated applications and open-source smaller models for edge and specialized use cases.

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But beyond the momentum building in AI, global deal activity plunged 19% YoY to its lowest level since 2016, creating both challenges and opportunities for investors and corporate strategists.

Download the full report to access comprehensive data and charts on the evolving state of venture across sectors, geographies, and more.

Key takeaways from the report include:

AI is eating VC. In 2024, AI represented 37% of venture funding and 17% of deals — both all-time highs. AI infrastructure players raised all of the top 5 venture deals of the year, with 4 closing in Q4’24 alone — driving a 2-year high in quarterly funding. With nearly 3 in 4 (74%) AI deals being early-stage in 2024, investors are staking out early claims to reap the rewards of the tech’s potential.

Aside from AI, venture dealmaking is in a drought. Globally, deal activity fell 19% YoY to 27K in 2024 — its lowest annual level since 2016. The drop was most pronounced in countries like China (-33% YoY), Canada (-27%), and Germany (-23%). However, several countries in Asia — Japan, India, and South Korea — have bucked the downward trend. Their resilience suggests attractive investment conditions.

AI and industrial automation are common themes among the fastest-growing tech markets. Out of 1,400+ tech markets that CB Insights tracks, those with the highest rate of YoY deal growth include enterprise AI agents, genAI for customer support, industrial humanoid robots, and autonomous driving systems. Expect these technologies to continue maturing in 2025, increasing their disruptive potential.

Despite market uncertainty, early-stage valuations hit a record-high median of $25M in 2024. Investors are packing into early-stage rounds to ride the next major wave of value creation in tech, likely drawn by startups’ ability to now build products with less capital and fewer people thanks to AI tools and infrastructure. However, early-stage startups could face a reality check when they try to raise later-stage rounds if they have yet to prove they can sustain growth. Although mid- and late-stage deal valuations rebounded slightly vs. 2023, they remain muted compared to 2021 and 2022.

IPO timelines get delayed. From first funding to IPO, VC-backed companies that went public in 2024 waited a median of 7.5 years — 2 years longer than in 2022. Amid unfavorable market conditions, some late-stage players like Stripe and Databricks have resorted to raising additional equity funding or selling private shares in lieu of going public. This allows them to create liquidity for early investors and employees when the path to a public debut is rocky.

We dive into each trend below.

AI is eating VC

The 5 largest deals of the year all went to AI model and infrastructure players (led by Databricks’ $10B Series J, followed by a $6.6B round for OpenAI, two $6B rounds for xAI, and a $4B round for Anthropic). But the activity isn’t limited to the largest, most well-resourced AI players. 

Across the board, AI companies are capturing a higher share of deal volume — nearly one in 5 deals (17%) now go to AI companies, almost triple the share from 2015 (6%). AI deal volume remained above 4,000 for the fourth year in a row. 

CBI customers: Track 15,000+ AI companies

The boom is providing tailwinds for every stage of the startup lifecycle, from early-stage companies — which take 3 out of 4 deals in AI — to startup exits. The AI M&A wave is in full force, with 2024’s 384 exits nearly rivaling the previous year’s record-high 397.

This trend will continue in 2025 as incumbents look to grab AI tech and talent and build end-to-end AI offerings. Get the full breakdown of what AI M&A means for corporate strategy in our Tech Trends 2025 report.

In Q4’24, the AI boom helped fuel a substantial rebound in global funding. The quarter’s funding tally reached $86.2B — a 2-year high, and an increase of 53% quarter-over-quarter (QoQ).

60% of that quarterly total, or $52B, came from mega-rounds (deals worth $100M+) — nearly tying Q1’21 (61%) for the highest share ever across venture. 

At the same time, quarterly deal volume steadily declined throughout 2024, including slipping below 6,000 in Q4’24 for the first time since 2016.

Aside from AI, venture dealmaking is in a drought

Despite AI’s surge, most venture sectors face their worst dealmaking drought in nearly a decade, forcing investors to adjust their strategies. Many investors are taking a more selective and risk-off approach right now as they wait out macroeconomic volatility and geopolitical tensions.

Among major dealmaking countries and regions (those seeing 500+ deals per year), the slump was most pronounced in China (-33% YoY drop in deals), Canada (-27%), and Germany (-23%). 

However, several countries in Asia bucked the trend and notched slim YoY gains: Japan (+2%), India (+1%), and South Korea (+1%). These countries have invested heavily in developing their startup ecosystems and may be benefiting indirectly from investors diverting funds away from China.

AI and industrial automation are common themes among the fastest-growing tech markets

AI and industrial automation are at the center of some of the fastest-growing markets in tech.

We filtered CB Insights’ 1,400+ tech markets for those with at least 20 equity deals over the last 2 years, then singled out those with the strongest deal growth YoY in 2024.

The enterprise tech and industrials sectors dominate, comprising 9 of the top 10 tech markets. Advancements in generative AI are fueling much of the activity in areas like humanoid robots and autonomous driving systems. Investors are also backing tech companies improving industrial processes like water treatment and purification, with deals to the market more than doubling YoY.

The enterprise tech and industrials sectors are also seeing a wave of hiring, as they lead in YoY headcount growth among all sectors. Industrials markets saw an average of 11% headcount growth last year, followed by enterprise tech markets with 10%. 

Financial services and the consumer & retail industries are noticeably absent from the top 10 fastest-growing markets. Given the tough venture landscape, emerging technologies in these areas face an uphill battle.

CBI Market Index: Compare 1,400+ markets

Early-stage deals are showing strength

Globally, early-stage dealmaking represents one of the most vibrant areas of venture right now, with median deal size and valuation reaching all-time highs in 2024.

The seed/angel and Series A stages remain resilient despite the broader downturn, in part because investors view them as a safe haven to ride out late-stage challenges like constricted exit opportunities and capital constraints. Deal sizes and valuations for the mid- and late stages rebounded slightly vs. 2023 but were muted when compared to the boom times of 2021 and 2022.

Corporate strategy and development teams seeking out early-stage opportunities can see 900+ high-potential startups here. To identify these players, we looked at the nearly 11,000 VC-backed startups that raised seed or Series A rounds in 2024, then filtered for those with the healthiest businesses (600+ Mosaic score) and strongest management teams (600+ Management Mosaic score).

Explore 900+ high-potential early-stage startups

IPO timelines get delayed

Most tech firms continue to shirk the IPO market. Some are still waiting for macroeconomic conditions to stabilize, while others prefer to focus on topline growth without having to deal with the financial scrutiny that comes with being a public company.

This is pushing back the timelines for IPO-ready companies even further. 

From first funding to IPO, VC-backed companies that went public in 2024 waited a median of 7.5 years — 2 years longer than in 2022.

While Q4’24 saw an uptick in global IPOs, activity remains down vs. historical levels. In the current climate, many late-stage startups will likely opt instead to raise more private funding to sustain operations and pay out employees or early investors.

Search for recently funded companies with high IPO probabilities

Related resources from CB Insights:

• Live briefing on venture trends for 2025
• All AI research from CBI
• 15 tech trends to watch closely in 2025
• Game Changers 2025: 9 technologies that will change the world
• $1B+ Market Map: The world’s 1,249 unicorn companies in one infographic

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For pharma companies, it’s critical to maximize efficiency in preclinical development — the stage after drug discovery and before clinical trials — as poor processes can mask early warning signs of drug candidates that are likely to fail.

The stakes are high: Studies show that 90% of drugs fail in clinical trials. In Phase 1 trials alone — which have a median cost of $3.4M per trial — 40% of drug candidates fail.

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While AI copilots — which work alongside humans to speed up their workflows — currently comprise 90% of company activity, the tech will serve as a stepping stone to more autonomous solutions in the coming years. Eventually, AI agents could manage entire industrial processes, shifting human roles from operational tasks to strategic oversight.

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Bringing a drug to market traditionally involves staggering costs — averaging $1.3B when accounting for all failed compounds — and costs continue to rise as productivity in the drug R&D process declines. 

AI has the potential to break this pattern by dramatically accelerating drug discovery and reducing costs.

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NOTABLE DEALS

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The AV space has seen equity funding triple this year to $7.5B, and robotaxi services are notching notable milestones — Waymo, for instance, recently hit 150K paid rides per week, 3x the volume from just 5 months prior.

Among key growth drivers, generative AI is helping remove hurdles to widespread adoption, while the potential for regulatory pullback may attract more investors in the year to come.

Source: CB Insights — advanced search for autonomous driving company funding as of 11/13/2024.

In this brief, we highlight potential approaches to tap into the autonomous driving opportunity — including partnering with self-driving stack developers, acquiring or investing in AV assets at attractive valuations, and supplying AV makers with the necessary components to scale.

Here are 4 key takeaways from our analysis:

1. Self-driving stack developers are best positioned for partnership in autonomous driving’s second wave: Waymo and Wayve have led this year’s funding rebound (combined ~90% of equity funding this year) as they make commercial gains and inch toward profitability. Both players are leveraging genAI to improve their self-driving systems and are opportunistically targeting multiple autonomous driving use cases.
2. OEMs are keeping their loss-making self-driving units afloat with fresh capital injections: Despite facing safety issues and commercialization delays, some OEMs view autonomous driving capabilities as strategically non-negotiable, with GM and Hyundai injecting a combined $1.4B in their self-driving units this year. At the same time, they may consider welcoming new financial backers to reduce their risk — creating opportunities for other OEMs to gain exposure to the space.
3. Ride-hailing players can partner with robotaxi companies to mitigate the impact to their business model: Ride-hailing companies are pursuing multiple autonomous driving partnerships at once — Lyft and Uber, for example, have formed a combined 6 partnerships this year. In turn, robotaxi companies should target a similar multi-platform strategy to maximize market reach.
4. In China, autonomous driving players have been pushed to go public at reduced valuations: Companies like WeRide and Horizon Robotics are debuting at prices discounted by 20% or more. The pressure these companies face to demonstrate near-term results represents an opportunity for OEMs and logistics companies to negotiate advantageous terms for strategic partnerships, investments, or even acquisitions.

We dive into each point below.

Self-driving stack developers are best positioned for partnership in autonomous driving’s second wave

After a 2-year funding winter, the autonomous driving space has attracted $7.5B in equity funding so far this year, a 3x YoY increase, driven by massive rounds to self-driving stack developers like Waymo ($5.6B Series C) and Wayve ($1.1B Series C).

Source: CB Insights — advanced search for autonomous driving mega-rounds in 2024 as of 11/13/2024.

Waymo hits key commercial milestones, leading US robotaxi rollout

Waymo has been hitting significant milestones this year, tripling its number of weekly paid rides from 50K in May to 150K in October. It has emerged as a competitor to ride-hailing giants in a few US cities, although its volume pales in comparison to the ~5M weekly rides offered by the likes of Uber and Lyft in NYC alone.

Waymo has also made progress toward profitability, with Sundar Pichai, CEO of Waymo parent Alphabet, highlighting significant cost reduction during Alphabet’s Q3’24 earnings call.

This has helped give investors confidence that Waymo is well-positioned to lead the commercial rollout of robotaxis in the US. The company was valued at $45B in its latest round, up from $30B.

Source: CB Insights — Alphabet’s Q3’24 earnings call

Compare robotaxi companies to identify potential partners

GenAI increases hopes of full autonomy breakthrough

Advancements in genAI are also acting as a tailwind in the autonomous driving space, with hopes that this technology can accelerate the timeline for full autonomous driving by removing remaining hurdles — such as cost, explainability, and vehicle-passenger communication.

Both Waymo and Wayve are investing heavily in the use of genAI to improve their existing autonomous driving systems.

Wayve specifically is developing a self-learning end-to-end AI driving system similar to Tesla’s that could be used by any automaker and is financially backed by some of the biggest AI players such as Microsoft and Nvidia.

Report: How generative AI is impacting autonomous driving

Self-driving stack developers target multiple autonomous driving use cases

The ability to target multiple autonomous driving use cases is another key strength of self-driving stack developers, allowing them to opportunistically pivot to focus on the most commercially promising ones. For example:

• Waymo reined in its investments in trucking use cases back in July 2023 to instead focus on robotaxis, where it saw more near-term commercial momentum. The company is now considering expanding into the personal car use case by licensing its technology.
• Wayve formed early partnerships with UK grocery retailers ASDA and Ocado, focused on home delivery of groceries. The company is now pushing deeper into robotaxis, partnering with Uber to roll out self-driving vehicles on the ride-hailing giant’s platform in the future.

Traction in the robotaxi space is driving other players to reconsider their AV strategy. For example, Elon Musk has increasingly framed Tesla as a robotaxi company — although the timing of the Tesla Cybercab launch remains uncertain.

Demonstrating a path to profitability will be key for robotaxi companies — including Waymo — to justify their outsized funding rounds, creating opportunities for OEMs and mobility players to help them scale or monetize their technology through licensing.

Their success also hinges on more municipalities and countries authorizing their operations, something that’s likely to take time unless regulations become less restrictive.

OEMs are keeping their loss-making self-driving units afloat with fresh capital injections

Despite mounting challenges such as safety issues and delayed commercialization, major OEMs such as General Motors (GM) and Hyundai have recalibrated their autonomous driving strategies while continuing to finance their subsidiary operations.

The sustained funding from OEMs amid setbacks reflects several strategic imperatives. For one, they’re still banking on the potential for returns on their significant existing investments, while also looking to position themselves competitively against tech-native OEMs such as Tesla and BYD.

Perhaps more importantly, it allows them to not miss out on the growth of the robotaxi industry by serving as suppliers of choice for vehicles and AV hardware — without bearing the full cost of software development. For example, Hyundai partnered with Waymo earlier this year to provide the robotaxi company with a fleet of vehicles equipped with autonomous tech.

Source: CB Insights — advanced search for corporate majority rounds in autonomous driving companies as of 11/13/2024.

GM provided its robotaxi unit Cruise an $850M lifeline despite safety incidents that forced the company to pause its service back in October 2023. The funding aims to bridge Cruise as it relaunches its service in select US cities, with the intent to charge for rides at the beginning of 2025.

Hyundai invested nearly $1B — including a $475M fresh capital injection — to gain 85% control of Motional, an autonomous driving JV between Hyundai and Aptiv. Aptiv’s stake reduction in the JV followed a strategic decision to cease further investment due to delayed commercialization.

Given the market pressures and challenges these units have faced, other OEMs may have an opportunity to gain exposure to the autonomous driving space by partnering with or investing in these units on favorable terms rather than trying to build the technology themselves. 

Ride-hailing players can partner with robotaxi companies to mitigate the impact to their business model

The ride-hailing industry is undergoing a major shift as platforms rush to integrate AVs into their networks.

While these partnerships offer compelling near-term advantages, they also highlight the existential challenges facing traditional ride-hail business models in an autonomous future.

Source: CB Insights — business relationship data for Uber and Lyft as of 11/13/2024

Leading platforms such as Uber and Lyft — which sold their autonomous driving units in 2020 and 2021, respectively — are pursuing multiple parallel relationships with autonomous driving developers, suggesting both urgency and hedging strategies.

Between the two, Uber is currently leading the way with 5 partnerships since 2023, 4 of which have been signed since August this year. In September, the company announced an expansion of its partnership with Waymo, giving access to Waymo’s robotaxi through the Uber app in Austin and Atlanta (starting in 2025) in addition to Phoenix, where Uber users have been able to order a Waymo since October 2023.

These partnerships offer immediate operational benefits: reduced driver costs, improved service reliability, and the ability to better manage surge pricing.

However, they also expose how the current ride-hailing platform model faces disruption — that is, by helping autonomous driving companies build direct relationships with consumers and gain real-world miles to improve their autonomous operations. This creates a paradox where ride-hail companies are essentially helping to incubate their potential future competitors.

Looking forward, ride-hail companies face 3 distinct strategic paths:

• Partnership strategy: betting on becoming the dominant platform layer atop multiple autonomous driving systems providers
• Acquisition strategy: buying autonomous driving capabilities to maintain control of the full stack
• Potential acquisition target: positioning themselves to be acquired by autonomous driving companies seeking customer relationships and operational expertise

The choice between these paths will likely determine which companies survive the transition to autonomous mobility. 

Report: How Uber is redefining its core businesses in the age of autonomy

In China, autonomous driving players have been pushed to go public at reduced valuations

China is also seeing heightened activity in the autonomous driving space this year, including growing adoption of robotaxis. Baidu‘s Apollo Go service, for instance, averaged 75K fully driverless rides per week in Q2’24, up 26% YoY.

Chinese autonomous driving companies are also leading an exit wave through public listings, with Horizon Robotics and WeRide going public in October and Pony.ai, Momenta, and Minieye all recently filing to do the same. But they’re doing so at a discount to their last private valuations, reflecting limited access to private capital at a time of accelerating commercialization. 

Source: CB Insights — advanced search for autonomous driving exits over time as of 11/13/2024 (excludes corporate majority deals)

Both Horizon Robotics and WeRide completed their IPOs at a more than 20% discount to their last private valuations, while Pony.ai is reportedly seeking a $4.6B IPO valuation, down from $8.6B just a year ago.

Private funding for China-based autonomous driving companies has dropped 90% since 2021, from $4B to less than $400M in 2024 YTD. The funding drought is pushing many of these companies to secure public funding or risk falling behind in the capital-intensive race to autonomy.

This funding crunch comes at a particularly critical time, as many players are accelerating their commercialization efforts and require capital to scale operations. As newly public companies face greater scrutiny over quarterly performance and profitability, they’re likely to prioritize near-term revenue generation over long-term technological development. 

For transportation and mobility companies looking to expand in China, this creates opportunities to partner on advantageous terms with local players that need to show commercial progress.

This shift will also create an opening for well-funded private players like Waymo, which can maintain their focus on achieving full autonomy without the pressures of public market expectations. 

Finally, the need for operational efficiency is likely to drive consolidation within the industry, as public companies seek cost synergies and combined market power to improve their financial metrics.

Identify likely autonomous driving acquisition targets

Looking ahead

Robotaxi adoption and genAI integration are already driving the next phase of the autonomous driving market’s evolution, pushing mobility players and OEMs to reassess their strategies after some had reduced their exposure to the space.

Although the success of robotaxi operations hinges on gaining regulatory approval in many more cities — which, in the US, may be accelerated under the incoming administration — expect to see a flurry of partnerships and strategic investments as automakers, autonomous driving hardware suppliers, and mobility platforms all vie to help robotaxi companies scale and get a share of the market.

As leaders such as Waymo race ahead, emerging autonomous driving companies will have to choose between accelerating the commercialization of their existing solutions or trying to outcompete winners on technological advancements (e.g., full driving autonomy, significantly cheaper autonomous driving systems, etc.).

Either strategy will likely require pooling resources, leading to consolidation in the space.

MORE AUTONOMOUS VEHICLE RESEARCH FROM CB INSIGHTS:

• Generative AI is accelerating the timeline for fully autonomous driving
• Autonomous driving systems market report
• Uber’s AV strategy: How the rideshare giant is redefining its core businesses in the age of autonomy
• Robotaxi market report

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Our 2025 Tech Trends report provides a concrete roadmap for corporate leaders to navigate some of the most important technology shifts in the year ahead.

We include specific recommendations for action so that business leaders can get ahead of the next wave of value creation.

Here is a selection of key findings from the report:

• AI agents are given money to spend: AI agents’ utility is limited until they can make transactions seamlessly. A small group of tech players is building new infrastructure to make that happen.
• The future data center arrives: With data center power usage expected to more than double by 2026, big tech companies are morphing into energy innovators to support AI workloads. There’s a huge opportunity in improving data centers’ energy efficiency.
• Investment floodgates open for RNA therapeutics: RNA therapeutics developers are pioneering new ways to treat traditionally “undruggable” diseases, with a growing focus on neurodegenerative disorders like Alzheimer’s and Huntington’s diseases.
• AI M&A fuels the next wave of corporate strategy: AI’s share of corporate tech M&A has doubled since 2020. Tech incumbents like Nvidia, Salesforce, and Snowflake, as well as consultancies like Accenture, are rapidly acquiring AI startups to tap into enterprise demand. 
• Disease management enters a new phase with AI: AI is improving care delivery across 3 key areas of disease management: precise symptom evaluation; testing/screening for earlier disease detection (including before symptoms even appear); and finding at-risk individuals in datasets of entire patient populations. 
• Retail’s personalization imperative: Generative AI is unlocking 1:1 experiences across commerce touchpoints, with leaders like Target seeing a corresponding 3x boost in conversation rates. Personalization will become omnipresent in retailers’ offerings.
• And much more

Methodology

Our analysis relies on a wide range of CB Insights datasets, including financing and acquisition data, valuations, founding team and key people data, earnings transcripts, and more. We also leverage CB Insights’ proprietary scoring algorithms to measure business health (Mosaic) and maturity (Commercial Maturity), as well as the likelihood of acquisition (M&A Probability score). Throughout the report, we provide CB Insights customers with jumping-off points to dig deeper into the data behind the report.

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In response, robotics companies are seeing an opportunity to deploy their solutions across the healthcare industry — from automated medication dispensing to robotics-assisted physical therapy. Broader interest in the space is also rising, with healthcare robotics news mentions reaching a new high in October.

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