Big Tech – CB Insights Research

Nvidia’s next big bet? Physical AI

Alex Lee — Wed, 26 Mar 2025 13:59:20 +0000

This research comes from the March 25 edition of the CB Insights newsletter. You can see past newsletters and sign up for future ones here.

M&A is back.

Below, we break down what’s driving the surge in deals, then zoom in on Nvidia’s latest purchase.

Buyers on the prowl

Q1’25 has already seen 11 $1B+ deals for VC-backed companies worth a combined $54.5B — blowing past quarters out of the water.

More than half of that value comes from Google’s $33B purchase of Wiz, the biggest VC-backed M&A exit of all time.

It’s not just tech startups — consumer & retail brands are getting snapped up too, like Pepsi’s $2B acquisition of Poppi.

But tech is leading the charge.

M&A activity in the sector rebounded 5% in 2024 and we expect it to gain more steam this year thanks to several factors:

- Less regulatory pressure: Big tech players like Google are betting on a friendlier dealmaking climate with Lina Khan out as head of the FTC.
- AI boom: Incumbents are anxious to get their hands on AI assets and infrastructure (see ServiceNow’s acquisition of MoveWorks and SoftBank’s acquisition of Ampere).
- Cheaper prices: Tech M&A valuations keep falling, encouraging strategic and financial buyers to get off the sidelines.

Nvidia’s M&A playbook

Among the Mag 7, Nvidia stands out for its aggressive acquisition strategy.

All told, Nvidia has snapped up 7 AI startups since 2021, with 4 of these in just the last year.

Last week it bought Gretel — reports place the exit valuation north of $320M (Gretel’s last disclosed valuation) but less than $1B.

Per CB Insights’ ESP ranking, Gretel is a leader in the synthetic training data market.

Source: CB Insights — ESP ranking of players in tabular and text-based synthetic training data

Synthetic data offers a potential solve to 3 issues in AI development:

A diminishing pool of high-quality text data to train more advanced LLMs.
The need to preserve privacy by using anonymized data, critical to AI adoption in industries like healthcare and finance.
The absence of real-world data to train physical AI models on tasks like driving cars or piloting humanoid robots.

CBI customers can see our analysis of 50 synthetic data providers here.

By acquiring Gretel, Nvidia positions itself at the forefront of the synthetic data market and strengthens its position in emerging areas like physical AI.

Nvidia sees the physical domain as the next evolution of AI, according to CB Insights’ earnings call transcripts.

Source: CB Insights — Nvidia Q4 FY 2025 earnings transcript

Back in June 2024, we wrote about how Nvidia is investing in and partnering with companies focused on industrial applications, like digital twins and robotics, which can rely on AI for simulation and training.

See where else the $3T company is targeting growth in our Nvidia strategy map.

Related research from CB Insights:

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Global AI race heats up in India with unprecedented hiring spree

Thomas Hodson — Fri, 21 Mar 2025 19:50:48 +0000

Global tech leaders are establishing positions in India’s fast-growing AI sector.

According to CB Insights headcount data, AI firms such as Glean, Scale, and OpenAI have increased their workforce in the country by as much as 67% over the last six months. The country’s domestic AI companies have also seen 32% headcount growth over the same period.

Notably, India is now OpenAI’s second-largest market — with a user base that tripled in the past year — while global tech giants like Microsoft and Nvidia have made substantial infrastructure investments in the country.

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Google’s biggest acquisitions

emiller — Thu, 20 Mar 2025 15:00:20 +0000

Google has disclosed over 270 acquisitions in its history.

Its biggest acquisition to date came in March 2025, when it bought cloud security company Wiz for $33B — nearly 3x the value of its $12.5B acquisition of Motorola Mobility in 2012. (It sold Motorola Mobility off after just 2 years for a quarter of the acquisition price.)

Using CB Insights M&A data, we made a visual timeline of the largest acquisitions in Google’s history. See the list of top 10 below.

Please click to enlarge.

Google’s top 10 acquisitions:

Wiz ($33B, 2025) — Cybersecurity
Motorola Mobility ($12.5B, 2012) — Mobile
Mandiant ($5.4B, 2022) — Cybersecurity
Nest Labs ($3.2B, 2014) — Home automation
DoubleClick ($3.1B, 2007) — Advertising
Looker ($2.6B, 2019) — Business intelligence
Fitbit ($2.1B, 2019) — Wearables
YouTube ($1.7B, 2006) — Video sharing
Waze ($1.15B, 2013) — Mapping
HTC – Pixel Smartphone Division ($1.1B, 2017) — Mobile

KEY TAKEAWAYS

Google has spent nearly $65B on its top 10 acquisitions.
All 10 of the top deals pictured had valuations greater than $1B, including marketing solutions provider DoubleClick ($3.1B, 2007) and navigation app Waze ($1.15B, 2013). YouTube ($1.7B, 2006) was Google’s first $1B+ acquisition.
These top deals reflect Google’s strategy evolution, from adtech (AdMob, DoubleClick) in the late 2000s to mobile (Motorola Mobility) and wearables (Fitbit) in the 2010s to cloud computing and security (Mandiant, Looker, Wiz).

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Zuckerberg vs. Altman: A showdown in social AI

Alex Lee — Wed, 05 Mar 2025 22:04:20 +0000

This research comes from the March 4 edition of the CB Insights newsletter. You can see past newsletters and sign up for future ones here.

AI chatbots saw a record 427M app downloads last quarter — up 42% vs. Q3.

Now Meta wants in on the action.

The tech giant reportedly plans to spin Meta AI into a standalone app to compete with ChatGPT, Google’s Gemini, Perplexity, and other chatbot apps.

ChatGPT is still king with nearly a quarter of AI chatbot app downloads in 2024 and 400M weekly active users across platforms (not just mobile).

But Meta has a massive in-built user base, including ~700M people who already interact with Meta AI features each month across apps like Facebook and Instagram.

The battle for attention is on.

Source: X

One of the differentiators for social AI applications will be emotional intelligence — an area where OpenAI’s latest model, GPT-4.5, shows promise.

While many headlines have focused on the model’s eye-watering costs, one notable advance is its reported ability to interact with greater empathy (an area where Anthropic’s Claude has so far been superior).

This matters because the more natural AI sounds, the easier it will be for consumers to see it fitting into their lives.

It also makes AI even more formidable in both personal and business settings if the AI can be both higher IQ and EQ than most of us pesky ol’ humans.

Businesses are catching on: More public cos are citing AI as a “friend” or “companion” on earnings calls in an effort to make B2C services more personal.

Mercedes-Benz, for instance, envisions drivers chatting with its MBUX voice assistant (which is built on ChatGPT) like a friend.

Source: CB Insights — Mercedes-Benz Q4’24 earnings call

Beyond emotional connection, AI developers are also making their chatbots more useful through agentic capabilities. This dual focus on both feeling and function will shape the competitive landscape moving forward.

While still constrained by reliability and access issues, web-browsing agents like OpenAI’s Operator signal a future where humans interact regularly with AI agents that take actions on their behalf.

Already nearly every tech giant, plus leading LLM developers like OpenAI and Anthropic, have developed AI agents or are building out tools for others to develop them.

In a head-to-head matchup, OpenAI and Meta would bring distinct sets of advantages.

OpenAI has already built user habits with ChatGPT and is now layering agent capabilities on top. Meta must convince users its AI offering brings something meaningfully different.

But Meta’s experience in the social realm, combined with its existing algorithms, could give it an edge in creating AI that feels like it belongs in human conversations.

Watch for both companies to rapidly iterate on their consumer agent offerings in the coming months.

Related AI research from CB Insights:

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Tech M&A Predictions for 2025

ashley.tortorelli@cbinsights.com — Mon, 24 Feb 2025 21:34:48 +0000

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The foundation model divide: Mapping the future of open vs. closed AI development

Isabelle Selles — Wed, 08 Jan 2025 20:08:44 +0000

This is part 1 of 2 in our series on the generative AI divide. In part 2, we will cover considerations for enterprise adoption of open & closed models.

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:

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.
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).
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.

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:

“I really think small language models are the future. You don’t need these huge proprietary LLMs for the vast, vast majority of use cases that you’re dealing with, especially some of the administrative burden in healthcare that we deal with.”

VP of Machine Learning,
Publicly traded multinational health insurance company

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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Big Tech in Fintech

Laura Kennedy — Thu, 19 Sep 2024 12:07:55 +0000

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Big Tech in Energy: How Amazon, Google, Microsoft, & Nvidia are advancing the global energy transition

Thomas Hodson — Wed, 04 Sep 2024 16:53:08 +0000

The energy sector presents big tech companies with opportunities to address the growing demand for clean energy solutions and meet their sustainability goals.

These tech leaders are collaborating with energy incumbents and startups alike to tap into renewable energy sources and decarbonize their operations.

While these big tech players are competing in the energy space, they are also developing unique strategies:

Amazon is working to decarbonize its transportation and fulfillment center operations, with a focus on hydrogen tech.
Google is pioneering new models for clean energy procurement as it works to boost the sustainability of its data center network.
Microsoft is focusing on renewable energy sources — like solar and fusion — and carbon capture technologies to meet the growing energy demands of its AI-driven operations.
Nvidia is enhancing data center energy efficiency and investing in the development of a green and reliable power grid.

This report uses CB Insights datasets like investments, acquisitions, business relationships, company scouting reports, earnings transcripts, and more. Learn more about our data here.

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This month in genAI: Moonshot raises $300M, Amazon acquires Perceive, Google launches Gemini Live

Adya Pandey — Fri, 30 Aug 2024 19:51:31 +0000

The content below was curated by our experts using CBI Instant Insights, a one-click AI analysis and summarization tool. Click on company profiles for more details and sourcing information.

NOTABLE DEALS

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Analyzing Apple’s AI strategy: Small models, spatial computing, and consumer-friendly AI agents

Stephanie Dalwin — Fri, 30 Aug 2024 17:11:14 +0000

Apple’s recent moves are a testament to its singular approach to AI development.

Unlike big tech peers like Google and Meta, it’s largely kept its in-house model development activity out of the public eye. With a focus on the on-device user experience — and smaller models as a result — Apple is relying on external large language models (LLMs) from partners like OpenAI to round out its generative AI suite.

Using CB Insights data, we uncovered the 3 biggest strategic priorities in Apple’s AI strategy:

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Big Tech in Healthcare

Amrit Panjabi — Thu, 22 Aug 2024 14:00:48 +0000

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Big Tech in Fintech: How Amazon and Google are battling to own transactions

Laura Kennedy — Thu, 08 Aug 2024 20:35:09 +0000

Big tech won’t be your next bank — but they’ll play a part in many of your transactions.

After nearly a decade of big tech companies venturing into launching their own financial products, the major players have now pulled back. Most have shifted to roles as tech providers, broadly supporting advances in financial infrastructure.

Amazon and Google stand out in this area:

Amazon is embedding itself in more financial transactions via partnerships, investments, and acquisitions. It’s using these relationships to reach customers across more geographies and a wider range of services.
Google has shifted away from providing financial services and instead is connecting its existing platforms to others’ financial offerings. The company is also investing and partnering to enable digital-first financial tools.

We mined CB Insights data on Amazon’s and Google’s investments, acquisitions, and partnerships, as well as patents and earnings transcripts, from January 2021 to July 2024 to explore how the companies are reengineering their fintech strategies.

Download the full report to see where they are making moves.

This report uses CB Insights datasets like investments, acquisitions, business relationships, earnings call insights, patents, and more. Learn more about our data here.

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Big Tech in Healthcare: How Amazon, Google, Microsoft, & Nvidia are looking to transform drug R&D, primary care, and more

Jacqueline Tubbs — Wed, 12 Jun 2024 18:49:45 +0000

The $11T+ healthcare industry presents a host of opportunities and challenges for big tech players, from the chance to capture an abundance of consumer data to the pressure to address digitization and connectivity.

These leaders are harnessing their existing offerings — in areas like cloud computing, AI, and hardware — to service healthcare providers and pharmaceutical companies.

While big tech players are competing with each other in this landscape, they are also carving out distinct strategies:

Amazon is going deeper into primary and specialized care.
Google is amassing troves of health data, which could play a role in its biotech bets.
Microsoft is equipping healthcare organizations with AI tools to improve clinical research, drug R&D, and care delivery.
Nvidia’s long-standing hardware dominance positions it to play a major role in the future of smart hospitals.

This report uses CB Insights datasets like investments, acquisitions, business relationships, patents, buyer interviews, company scouting reports, and more. Learn more about our data here.

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Analyzing Google’s healthcare growth strategy: Can the tech giant become the sector’s go-to AI provider?

Anjalika Komatireddy — Thu, 25 Apr 2024 21:39:07 +0000

Google has recently sharpened its focus in healthcare, where it’s looking to deploy AI throughout the fragmented ecosystem.

It’s tackling the issue on all fronts, from investing in AI-enabled care models via Google Ventures, to forging partnerships through its Google Cloud division, to launching new products that tailor generative AI to healthcare use cases.

These moves seek not only to address the challenges facing the sector, but also to strategically differentiate Google from big tech peers like Nvidia, Microsoft, and Amazon — all of whom are making concerted efforts to integrate AI into healthcare.

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Analyzing Microsoft’s healthcare growth strategy: How the software giant is betting generative AI will transform the sector

Alex Lennox-Miller — Fri, 19 Apr 2024 15:57:51 +0000

Microsoft is building on its momentum in generative AI to move deeper into healthcare.

The tech giant found a major entry point into healthcare workflows when it acquired Nuance for $19.7B in 2022. It followed up on this by expanding its existing relationship with leading EHR vendor Epic to introduce generative AI across clinical workflows, with the aim of improving provider efficiency and mitigating staffing shortages.

Microsoft’s recent relationships also point to an increasing focus on developing and deploying generative AI in the pharmaceutical industry. The tech behemoth is partnering with and investing in startups in drug development, where early applications of generative AI have seen especially strong investor and commercial traction.

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The big tech AI arms race: 75+ AI startups backed by Amazon, Google, Microsoft, and Nvidia

Isabelle Selles — Tue, 12 Mar 2024 16:47:53 +0000

Big tech companies — Alphabet (Google), Amazon, Apple, Meta, Microsoft, and Nvidia — are betting big on AI.

Microsoft’s market cap is hovering at all-time highs as investors have gotten behind its embrace of the technology. Meanwhile, Nvidia joined the club after cruising past the $1T and $2T market cap benchmarks in less than a year on the back of demand for its high-end AI chips.

Where big tech is putting its money — including investments and acquisitions — provides a window into each player’s strategy. The number of AI deals backed by the group increased 57% in 2023 compared to 2022. Notably, Meta and Apple didn’t invest in any AI startups in 2023, though Meta has been active in developing its own open-source AI models and Apple acquired an AI video compression startup last year.

Below, we map out every AI company backed by big tech and their strategic venture arms in 2023 with key takeaways below.

CB Insights customers can use this search to stay updated on every big tech AI investment, including 2024 deals.

Key takeaways

1) Big tech continues to pile in on AI infrastructure, nabbing stakes in the next generation of AI startups.

As companies rush to harness AI’s potential, big tech wants to put itself right at the heart of AI value chains.

Some of the largest AI rounds backed by the group in 2023 went to leading foundation models and other development platforms: OpenAI ($10B corporate minority from Microsoft), Anthropic ($2.6B in funding over multiple rounds backed by Google and Amazon), and Databricks ($500M Series I backed by Nvidia’s NVentures with follow-on participation from AWS and Microsoft).

2) Rivalries in cloud computing drive activity, while some startups bring in multiple big tech backers to remain neutral.

Due to the capital-intensive nature of AI development, startups are linking up with big tech companies like Google, Microsoft, Amazon, and Nvidia to access their cloud infrastructure, chips, and dollars.

In turn, big tech companies are fueling their rival cloud computing and chip businesses.

3) Healthcare and industrials lead big tech’s vertical AI investments.

Nvidia backed 8 AI startups in healthcare & life sciences in 2023, with 7 of these focusing on AI drug discovery, as the chipmaker doubles down on the sector.

Applications in materials development, manufacturing, and warehousing are also drawing the attention of big tech as the sector races to automate operations.

For example, the Amazon Industrial Innovation Fund invested in AI-powered robotics safety startup Veo Robotics in April 2023. Meanwhile, just last month, Microsoft’s M12 and NVentures backed humanoid robotics startup Figure’s $675M Series B.

4) An emerging focus area is AI companions and agents.

Companies in this category like Inflection are working to make interacting with computers as conversational as it is to talk to people.

A number of companies backed by big tech in 2023, like Adept and Imbue, are building “autonomous agents” — LLM-powered bots that can independently reason and execute tasks.

In this vein, in January 2024, the Amazon Alexa Fund backed a round to AI agent startup MultiOn.

5) Nvidia bursts onto the scene.

Overall, Nvidia has ramped up its activity more significantly — from backing 5 AI startups in 2022 to 32 in 2023 — than any other tech giant as it embeds itself deeper in the generative AI ecosystem.

Apple and Meta, which historically have made fewer investments compared with other giants and do not have venture arms, did not publicly disclose any AI startup investments in 2023.

Looking ahead

Large acquisitions have been key for big tech to bring in new revenue sources as well as launch new product lines.

But with regulatory pressure hobbling big tech’s acquisition activity, expect these players to lean more heavily on AI partnerships and investments, as well as product launches, in the coming months.

The post The big tech AI arms race: 75+ AI startups backed by Amazon, Google, Microsoft, and Nvidia appeared first on CB Insights Research.

AI-powered humanoid robots are coming and big tech wants in

Benjamin Lawrence — Fri, 23 Feb 2024 14:00:49 +0000

Humanoid robots are gaining momentum as companies across industries race to automate operations for a competitive edge.

These robots resemble the human form and are designed to be multi-talented, making it possible to swap them in for complex tasks normally handled by flesh-and-bone employees. Popular robot forms like automated guided vehicles (AGVs) & autonomous mobile robots (AMRs) and collaborative robotic arms are much less flexible.

However, humanoid robots are complicated to build and deploy. They require substantial sensor processing, advanced control, and complex skill execution. They also need multimodal artificial intelligence (AI) to understand the environment around them.

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The Future of Big Tech in AI

Isabelle Selles — Fri, 26 Jan 2024 21:27:44 +0000

The post The Future of Big Tech in AI appeared first on CB Insights Research.

Inside the Google Mafia: The most prolific ex-Google founders

Adya Pandey — Tue, 19 Dec 2023 21:42:26 +0000

Former Google employees — its alumni network, or “mafia” — have founded 1,200+ companies across a wide range of industries over the past 5 years.

These businesses, the majority of which have been backed by VCs like a16z and Sequoia Capital, have collectively raised $20B+ in equity funding.

Many members of the Google Mafia have founded just 1 business in their post-Google era. However, others have launched multiple, with the most prolific establishing 6 or more.

We analyzed CB Insights Key People data to identify the ex-Googlers who founded the most businesses after leaving the tech giant.

Ricky Jacob

Ricky Jacob was a team lead at Google, focusing on geo content operations for 2 years.

After leaving Google, Jacob launched location-based service provider GeoSpice (products included runner tracking platform Odikyo and event tech developer Eventsito). He also established CommonTrip, which functioned as a sustainability-focused ridesharing platform.

Jacob pivoted to fintech when he established open banking platform Kred (formerly Huepay), banking API platform Paysack, and robo-investing platform TRDR.

Most recently, the ex-Googler founded Certify Social, a platform that enables organizations to issue non-fungible token (NFT) rewards.

Robert Spiro

Robert Spiro co-founded social search engine Aardvark, which was acquired by Google in 2010. Following the acquisition, Spiro started as a product manager for Google’s social network: Google+.

After departing, Spiro’s founder journey picked up again with Good Eggs, an ethical food delivery startup where he also served as CEO for 4 years.

Following his time at Good Eggs, Spiro maintained a strong focus on the broader impact of his endeavors. For example, in 2017, he co-founded Imagination Machine, which helps create and support companies that have a positive social or environmental impact. That same year, he launched JHO, which serves as a subscription platform for women’s hygiene products.

In the years that followed, he co-founded educational magazine publisher and eco-friendly toy maker Les Mini Mondes and home solar kit provider Beem Energy. In the enterprise tech space, Spiro co-founded a professional training and coaching platform known as UpTogether.

Most recently, he helped create corporate social responsibility startup Good Steps.

The post Inside the Google Mafia: The most prolific ex-Google founders appeared first on CB Insights Research.

The future of big tech in 10 charts

Isabelle Selles — Wed, 06 Dec 2023 19:20:57 +0000

Big tech companies — Alphabet (Google), Amazon, Apple, Meta, Microsoft, and Nvidia — are on track to earn more than $1.65T in aggregate revenue this year. The group has already notched over $200B in profits in 2023.

Nvidia’s entrance into the “big tech” ranks comes on the back of the AI revolution: the AI chipmaker saw its revenue soar 206% year-over-year (YoY) in its fiscal quarter ended October 29.

However, for big tech companies to continue their frenetic pace of growth, they must add billions to their balance sheets each quarter.

Heightened competition across these companies’ core business lines (cloud computing, advertising, etc.), plus the generative AI shift, is putting pressure on big tech to cut costs and tap into new markets.

We dive in below across 10 charts.

Big tech’s dominance

Big tech’s prominence in the tech sector is evident in its sheer size compared to the most valuable tech startups.

The 6 tech giants hold an aggregate valuation of over $11T — nearly 3 times that of the entire billion-dollar unicorn club.

Even the largest unicorns are unlikely to be big tech contenders any time soon.

The club GAINS A NEW MEMBER

Nvidia’s dominance as an AI chipmaker has lifted it into the big tech ranks. The company hit a $1T market cap in Q2’23.

Its rise signals the next platform shift as companies rush to harness AI’s potential.

Revenue growth slows

Over the years, big tech has grown revenue at an astounding rate.

But broadly, these companies’ growth is slowing as they get bigger.

To fuel growth moving forward, they’ll be looking to reach into new markets, while staying at the cutting edge of emerging technologies like generative AI, quantum, and AR/VR.

A return to efficiency

Even big tech players over-expanded in 2021 and have subsequently moved to cut costs.

Though they still employ far more people now than they did in 2020, the pace of hiring will likely remain slower, with a focus on keeping expenses in check.

Blockbuster acquisitions

Large acquisitions have historically been key for big tech to bring in new revenue sources as well as launch new product lines.

WhatsApp, acquired by Facebook in 2014, now has more than 2B users globally and is among Meta’s fastest-growing services in the US.

But regulatory pressure has put a damper on this activity, as we highlight below.

Microsoft’s $69B acquisition of Activision, which finalized in October 2023, is a recent notable exception. It will bring $7.5B of revenue to Microsoft’s top line.

Regulatory pressure puts damper on M&A

Big tech M&A volume flatlined in Q3’23.

Though tech giants haven’t gone completely quiet — Microsoft’s Activision deal closed in Q4 after a nearly 2-year fight with regulators — a healthy tech ecosystem requires that they get back in the game. This is likely challenging with the FTC’s current posture.

In the meantime, expect smaller deals from these players going after key tech capabilities or talent.

For example, in Q4’22, Apple CEO Tim Cook said, “We’re constantly looking in the market and…[at] which things would provide either intellectual property or talent or preferably both that we would need. And so we’re constantly looking at acquisitions of all sizes.”

BIG TECH R&D spending FAR EXCEEDS US VC

In the US, big tech companies’ R&D investment outpaces that of overall US venture funding by a wide margin. This underscores the scale these companies play in driving tech innovation.

All in on AI

Tech giants are throwing their weight behind promising AI startups, offering computing power and funds for development, especially in the realm of generative AI.

These investments in turn could feed into their core business focuses, such as cloud computing (Google, Amazon, Microsoft) and AI chips (Nvidia).

Converging on each other’S territory

Big tech’s revenue sources are increasingly overlapping.

Cloud computing revenue growth, for example, has slowed amid heightened competition and as enterprise customers optimize their workloads.

Instead, Amazon, Microsoft, and Google are looking to compute-hungry AI to fuel their cloud computing businesses. Microsoft attributed 3 percentage points of Azure’s growth in the most recent quarter to AI.

Meanwhile, Amazon is continuing to gain share from leaders Google and Meta in the digital advertising realm.

As big tech advances on each other’s core businesses, players are also looking for growth in new markets.

Tech giants are bringing disruption to healthcare, fintech, telecom, and other verticals — but not without hiccups.

Dive in further with all our big tech research here.

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Generative AI Bible: The ultimate guide to genAI disruption

Isabelle Selles — Tue, 07 Nov 2023 22:29:45 +0000

For countless companies around the globe, one question is looming larger than any other: How can we win in the era of generative AI?

While the tech has been in development for years, it was the launch of OpenAI’s ChatGPT that suddenly brought generative AI — artificial intelligence that generates new content (text, code, images, audio, etc.) — to the masses.

Corporate attention to generative AI has since skyrocketed, with big tech and incumbents scrambling to harness its potential to improve productivity, scale automation efforts, and accelerate digital transformation. Many fear that failing to adapt will mean being outcompeted by the companies that do.

Meanwhile, hundreds of generative AI startups have emerged, followed by billions of dollars in investment. Investors are keen to ride the wave as startups take aim at disrupting entire industries.

In this report, we use CB Insights datasets — including tech company financings, valuations, revenues, business relationships, public earnings calls, and customer perspectives — to help you understand what’s going on in the market and the players and trends to watch.

The 122-page report covers:

The generative AI boom a decade in the making
The current genAI landscape and the players competing in each market
The latest moves from big tech firms like Microsoft, Google, Nvidia, Meta, and Apple
The race to dominate genAI infrastructure, plus the latest on closed vs. open-source development
GenAI opportunities for healthcare, financial services, and retail
The 50 most promising generative AI startups to watch
The emerging trends that will shape the future of generative AI

The post Generative AI Bible: The ultimate guide to genAI disruption appeared first on CB Insights Research.

The most active startup accelerators and where they’re investing

Robin Sherk — Tue, 10 Oct 2023 17:35:40 +0000

Accelerators have been busy.

In 2022, deal count in the broader venture market declined by 4% year-over-year (YoY). However, that same year, the 50 most active accelerators together backed 7,794 deals, a 14% jump from 2021.

This reflects a broader industry shift toward investing in companies earlier in the startup lifecycle. In 2022, early-stage companies captured two-thirds of all deals, the highest share in years.

With tech valuations under pressure — especially at later funding stages — accelerators are attracting attention as a way for investors and incumbents to develop high-potential opportunities.

Exclusive accelerator programs help startups hit the ground running with the resources they need, including initial capital, expert or peer mentorship, office space, networking, publicity, and more. Notable tech names like Dropbox, Rappi, Canva, and PayPal all received accelerator support in their early days.

Using CB Insights data, we analyzed these programs to uncover which accelerators are the most active and where they’re focusing their efforts. Below, we highlight 6 key takeaways from our analysis:

The 50 most active accelerators collectively backed 7,794 accelerator deals in 2022 — up 14% YoY and 34% compared to 2020.
Top accelerators’ median annual deal count jumped 41% YoY to hit 83 in 2022. This count was lifted by a surge of accelerator investments from tech giants such as Amazon, Microsoft, and Google.
Plug & Play, Y Combinator (YC), and Techstars top the list of most active accelerators since 2020. Of the 3, however, only Techstars increased its dealmaking from 2021 to 2022.
Among top accelerators that disclose funding amounts, most (63%) typically gave $100K or less in each accelerator deal from 2020 to 2022.
The US is the most active region for accelerators, attracting nearly a third (32%) of deals from top accelerators in 2022. However, this share has fallen since 2020, when US startups accounted for 39% of deals.
Among sectors, digital health captured the most deals (3,380) from top accelerators from 2020 to 2022. However, fintech took the top spot in 2022 with 1,261 deals, while energy & clean tech saw the largest increase in deals YoY (+30%).

Let’s dive in.

How active are the 50 biggest accelerators?

The 50 most active accelerators are getting busier with each passing year.

Among these 50 accelerators, deal activity — which includes initial-stage equity (seed/pre-seed and convertible note) and non-equity (e.g., grant) funding rounds — increased 14% YoY to reach 7,794 deals in 2022.

The growth in 2022 was distributed across a variety of accelerators, with the cohort’s median accelerator deal count jumping by 41% YoY to 83 deals.

Notably, several leading tech giants ramped up their programs significantly in 2022.

Amazon’s accelerator deal count grew 247% YoY last year, lifted by the 2022 launch of its Impact Accelerator to support underrepresented founders. Amazon Web Services (AWS) also expanded other programs, including its Healthcare Accelerator, with a focus on technology to train, retain, and deploy healthcare workers.

Meanwhile, Microsoft’s accelerator activity more than doubled in 2022 (up 118% YoY) after a slowdown in 2021. Google parent Alphabet’s accelerator deal count increased 84% over the same period, reflecting mounting activity in its existing programs. Overall, corporate accelerators’ share of deals is growing: Among the 50 most active accelerators, corporate programs grabbed 17% of deals in 2022 vs. 13% in 2021.

Who are the most active accelerators?

Two accelerators lead by a large margin in terms of deal volume: Plug and Play and Y Combinator. Each did over 2,000 accelerator deals from 2020 to 2022. Techstars takes the third spot with over 1,600 deals.

These 3 players are among the oldest accelerators, with YC pioneering the model back in 2005.

Notably, the top 2 are pulling back on their accelerator investment activity. For example, YC cut the size of its 2022 summer cohort by 40% in an effort to concentrate its resources more effectively, leading to a small dip in its deal activity for the year. Plug and Play is also concentrating its activity, evidenced by a 4% slip in accelerator investment count from 2021 to 2022.

In contrast, Techstars’ activity jumped 18% YoY in 2022. It saw a boost from the launch of its Techstars Powered by J.P. Morgan program, an initiative designed to support diverse entrepreneurs across the US.

Six out of the top 10 most active accelerators are US-based. However, accelerator activity is progressively spreading beyond the US investor community to include both global investors and a range of corporations, trade associations, non-profits, and government entities.

At the same time, accelerator programs are targeting increasingly niche areas and specific objectives. Examples include SAP’s “Future of Shopping” program for B2B startups and Startupbootcamp’s “Inclusive FinTech & DeFi” program launched last year.

How much cash do the most active accelerators invest?

Among the various ways accelerators offer value, providing capital tends not to be the main focus.

Almost half (46%) of these top accelerators either did not give any cash or did not disclose funding for at least 8 select deals between 2020 and 2022.

Among those that did disclose funding information, roughly two-thirds (63%) generally gave $100K or less in each deal from 2020 to 2022.

This suggests that accelerators provide a significant amount of non-monetary value, which comes in the form of mentorship, networking, validation, and exposure.

Where do the most active accelerators invest?

An accelerator’s headquarters is generally (though not always) a good indication of where it will focus its efforts.

From 2020 to 2022, roughly 3 in 5 accelerator deals from YC (58%) and Techstars (63%) went to US-based companies vs. 41% for Plug and Play. This was driven in part by Plug and Play’s global efforts, including subsidiary activity in Japan and Indonesia.

A similar trend played out among big tech companies. At Amazon, 54% of its accelerator deals went to US-based startups from 2020 to 2022, and 22% went to Asia-based companies. This aligns with Amazon’s limited footprint in Asia, relative to North America.

Microsoft’s accelerator activity reflects the opposite approach. Sixty-two percent of its deals went to Asia-based startups during that period, while just 11% went to US-based companies. Microsoft offers sizable programs outside of the US, including the Microsoft for Startups Middle East GrowthX Accelerator, launched in partnership with the Abu Dhabi Investment Office.

While the US has historically attracted a significant amount of deals from the top 50 accelerator cohort, its dominance is waning. The country’s share of deals hit 32% in 2022, down 7 percentage points from 2020.

At the same time, top accelerator deal share has grown in Asia, Africa, Latin America, and Europe. In 2022, Europe followed the US closely with 31% of accelerator deals from these investors.

Which industries do the most active accelerators invest in?

Digital health attracted the most deals from 2020 to 2022 when looking at the 50 most active accelerators. This was largely due to the fact that the Covid-19 pandemic steered more of the tech world’s efforts toward addressing gaps in healthcare, especially in 2020 and 2021.

In 2022, however, attention to digital health dipped, whereas fintech and consumer & retail saw double-digit percent growth in top accelerator deal volume YoY.

Fintech deal activity grew 18% YoY in 2022. While top accelerator interest in payments tech and crypto companies rose during this time, interest in insurtech and wealth tech solutions flattened.

Consumer & retail also saw an 18% lift over the same period. This was fueled by rising top accelerator activity in consumer tech, across areas like wellness, pet care, and beauty. Dealmaking to food tech startups, on the other hand, leveled out in 2022.

Energy & clean tech stands out on this list, as its top accelerator deal volume grew 30% YoY to rank fourth overall in 2022. This industry includes companies focused on energy production, distribution, and management. It also comprises companies focused on carbon monitoring, sustainability, and pollution or waste reduction.

For more accelerator coverage:

Report method

We define accelerators as offering direct guidance and resources to a startup through either a fixed-schedule cohort or rolling-acceptance program. Typically, startups apply for admission, but some accelerators are invitation-only.

Accelerators vary in whether they offer funding, take equity, hold challenges as part of the program, or charge fees for participation. Accordingly, the following disclosed early-stage and non-equity rounds are included in the analysis: seed, pre-seed, convertible note, accelerator/incubator, grant, and business plan competition. Given the seasonality of accelerator investments, we limit our analysis to annual comparisons.

While charts show parent organizations, our analysis only includes a parent organization’s subsidiaries that operate accelerators. Joint partnerships and incubators or early-stage investment arms that also operate accelerators are included.

Does your accelerator deserve to be in our next top investors report? Contact us here to set up a review of CB Insights’ coverage of your investments. As always, it’s 100% free and will improve our ability to rank your accelerator in upcoming research.

Top 10 most active startup accelerators

Accelerator	HQ country	Average deals per year (2020-2022)
Plug and Play	United States	929
Y Combinator	United States	820
Techstars	United States	542
TIPS Program	South Korea	419
Alphabet	United States	408
MassChallenge	United States	336
European Institute of Innovation and Technology	Hungary	232
SOSV	United States	205
Founders Forum	United Kingdom	195
Creative Destruction Lab	Canada	193

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The CB Insights Book of Strategy Maps, Part 2

Isabelle Selles — Wed, 04 Oct 2023 13:00:51 +0000

Famous management guru Peter Drucker once said, “Tell me what you value, and I might believe you. But show me your calendar and your bank statement, and I’ll show you what you really value.”

The same is true of understanding corporate strategy.

Understanding a company or its competitive strategy doesn’t come from listening to senior executives rattle off carefully coached talking points.

Instead, as Drucker points out, corporate strategy is visible in where companies allocate their time and money.

It is found by mining resource allocation and relationship data. Who are a company’s partners, investments, and M&A targets? These are the non-BS predictors of strategy. And CB Insights tracks them all.

Our 58-page coffee table book of strategy maps uses CB Insights data to shine a light on the strategies behind some of the world’s most important companies — from Amazon and Nvidia to PepsiCo and UnitedHealth Group.

If you’re a data and strategy nerd like us, we think you’ll enjoy this one.

For more strategy maps, check out our first edition of the Book of Strategy Maps here.

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The big tech company leading in AI acquisitions

Isabelle Selles — Wed, 27 Sep 2023 14:53:35 +0000

In the race for AI, Apple leads big tech in acquisitions.

Apple has acquired 30+ AI startups over the last decade. Google comes in second place with 21 acquisitions.

In 2023 so far, these companies have been relatively quiet on the AI M&A front. Only Apple and Google have made publicly disclosed AI acquisitions this year, purchasing AI video compression startup WaveOne and AI-powered math app Photomath, respectively.

Below, we highlight 4 key takeaways about big tech’s activity in AI.

Dive into each company’s acquisition history on its company profile: Apple, Google, Meta, Microsoft, Amazon.

KEY TAKEAWAYS

AI acquisitions have bolstered big tech’s prominent products and services. Apple’s AI acquisition spree over the years has been essential to the development of iPhone features like Siri, while Google’s acquisition of DeepMind (in 2014) underpins its current AI research efforts.
Big tech acquisition activity has trended downward overall. While historically big tech players have snapped up startups to acquire tech talent and expand into new markets and product lines, big tech acquisitions reached an 18-quarter low in Q2’23. A more challenging regulatory climate — especially in the US and Europe, where anti-trust pressure has been mounting — combined with the prevailing risk-off mentality of strategic acquirers, has led to a drastic decline in big tech acquisitions.
While tech giants pull back on acquisitions, some are stepping up their AI investment activity. Microsoft, for example, has invested in 9 AI companies in 2023 so far, including Inflection AI and OpenAI (compared to 3 in 2022). Its M12 venture arm — which announced in January that its investments would be more closely tied to Microsoft’s broader strategy — has also done 5 AI deals this year.
Amid the AI computing boom, expect continued AI investment from these companies. To keep pace with rivals Google and Microsoft, Amazon recently announced its intent to invest up to $4B in AI chatbot developer Anthropic. Other big tech players like Nvidia are also supercharging activity — Nividia has participated in some of the largest AI rounds of this year.

The post The big tech company leading in AI acquisitions appeared first on CB Insights Research.

Big tech isn’t shelling out for acquisitions like it used to

Alex Lee — Fri, 08 Sep 2023 13:15:03 +0000

Big tech mostly shied away from M&A in Q2’23.

Historically, big tech players have snapped up startups to acquire tech talent and expand into new markets and product lines. However, they face a more challenging regulatory climate — especially in the US and Europe, where anti-trust pressure has been mounting.

Combined with the prevailing risk-off mentality of strategic acquirers, this has led to a drastic decline in big tech acquisitions. Between Amazon, Apple, Google, Meta, Microsoft, and NVIDIA, only Apple disclosed an acquisition in Q2, according to the CB Insights Tech M&A Q2’23 Report.

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Big Tech – CB Insights Research

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Table of contents

Consolidation around frontier models

Industry leaders are divided in their approaches

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

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

Safety remains central to the debate

Closed-source developers lead the private market in equity funding

Performance gaps converge, with largest companies’ models topping leaderboard

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

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

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Key takeaways

1) Big tech continues to pile in on AI infrastructure, nabbing stakes in the next generation of AI startups.

2) Rivalries in cloud computing drive activity, while some startups bring in multiple big tech backers to remain neutral.

3) Healthcare and industrials lead big tech’s vertical AI investments.

4) An emerging focus area is AI companions and agents.

5) Nvidia bursts onto the scene.

Looking ahead

AI-powered humanoid robots are coming and big tech wants in

The Future of Big Tech in AI

Inside the Google Mafia: The most prolific ex-Google founders

Ricky Jacob

Robert Spiro

The future of big tech in 10 charts

Get the big tech investment data book

Big tech’s dominance

The club GAINS A NEW MEMBER

Revenue growth slows

A return to efficiency

Blockbuster acquisitions

Get the big tech investment data book

Regulatory pressure puts damper on M&A

BIG TECH R&D spending FAR EXCEEDS US VC

All in on AI

Converging on each other’S territory

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Generative AI Bible: The ultimate guide to genAI disruption

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The most active startup accelerators and where they’re investing

The world’s most active accelerators

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The world’s most active accelerators

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