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.

Most small drones from U.S. startups have failed to perform in combat, dashing companies’ hopes that a badge of being battle-tested would bring the startups sales and attention. It is also bad news for the Pentagon, which needs a reliable supply of thousands of small, unmanned aircraft.

In the first war to feature small drones prominently, American companies still have no meaningful presence. Made-in-America drones tend to be expensive, glitchy and hard to repair, said drone company executives, Ukrainians on the front lines, Ukrainian government officials and former U.S. defense officials.

Absent solutions from the West, Ukraine has turned to cheaper Chinese products to fill its drone arsenal.

Ukrainian officials have found U.S.-made drones fragile and unable to overcome Russian jamming and GPS blackout technology. At times, they couldn’t take off, complete missions or return home. American drones often fail to fly at the distances advertised or carry substantial payloads.

American drone company executives say they didn’t anticipate the electronic warfare in Ukraine. In Skydio’s case, its drone was designed in 2019 to meet communications standards set by the U.S. military.

Ukrainian forces are burning through about 10,000 drones a month, which they couldn’t afford if they had to buy expensive U.S. drones. Many American commercial drones cost tens of thousands of dollars more each than a Chinese model.

«Це рубікон для цієї угоди – найбільшої за останні 10 років M&A в Україні», – каже Forbes Михайло Шелемба, CEO провайдера домашнього інтернету «Датагруп-Volia», якого також купує Ньєль.

Після завершення угоди Шелемба очолить обʼєднану lifecell і «Датагруп-Volia». «Без позитивного рішення щодо цієї справи угода не відбудеться», – говорить Шелемба.

16 квітня вирішується доля $1,5 млрд інвестицій, до яких входять сума угоди та інвестиційні зобов’язання на п’ять років.

Візія об’єднання «Датагруп», «Воля» та lifecell – це наша спільна з Horizon Capital стратегія ще з 2018 року.

Яка вартість «Датагруп-Volia»? За різними оцінками йдеться про $120-140 млн.

Ви дуже скромно оцінюєте нас. «Датагруп-Volia» належить приватному фонду, що управляється Horizon Capital, тому оцінка була оголошена інвесторам фонду, а в них немає вимог щодо оголошення цієї інформації.

У 2019-му Vodafone придбали за понад $734 млн. Якщо угода з Ньєлєм – це угода десятиріччя, то сума більша.

Можемо сміливо називати це угодою десятиріччя.

На ринку фіксованого інтернету десь 4500 провайдерів. Яка у вас частка ринку?

У нас близько 9–10% ринку за різними підрахунками. 40% ринку контролюють 10 найбільших компаній. Найбільша має десь 15%. Тобто склалися гарні умови для консолідації ринку. Маючи мобільний зв’язок, ми будемо більш конкурентні. У планах – купувати менших провайдерів фіксованого інтернету. Ми завжди ведемо з кимось переговори, бо наша мета – зростати і органічно, і не органічно.

Що ця угода означає для України?

Ця угода зараз в усіх у світі на радарі. І її значення для економіки України складно переоцінити. Ньєль не просто інвестор. Його знають усі. Він публічна особа, тому резонанс досить великий. Я впевнений, що це стане дуже потужним сигналом для міжнародних інвесторів. У нас немає жодного права проґавити цей шанс. Якщо угода зірветься, то ми не просто не отримаємо позитивного сигналу. Це буде негативний сигнал з точки зору інвестиційної привабливості.

1. Displace services with software
Software may be eating the world, but not as fast as you’d think. Nearly 80% of the economy is service-based, largely untouched by technology due to the abundance of unstructured data and complex reasoning requirements. Generative AI is now making inroads into these previously underserved sectors, turning SaaS on its head with “Services-as-Software.”

2. Target work that is high-value, high-volume, or facing labor shortages
The roles where AI can have the largest impact are high-value (e.g., software engineers and lawyers), high-volume (e.g., customer service and business development representatives), or in areas with worker shortages (e.g., nurses).

3. Seek pattern-based workflows with high engagement and usage
Most AI apps are copilots that share work with an employee, while some are agents that can automate tasks entirely. Either way, a great place to seek a moat for AI apps is pattern-based workflows with regular usage and habitual engagement, rather than those that rely on data or network effects alone.

4. Unlock proprietary data
Over 80% of enterprise data is inaccessible because it’s trapped in unstructured file formats or document stores like your email inbox. By leveraging LLMs to make this data usable, AI-native apps can build proprietary data moats over time.

5. Embrace zero marginal cost creation
Generative AI enables infinite scaling of written content, images, and potentially even software and products, unleashing creation possibilities across the enterprise—from cold sales outbounds to personalized marketing to compelling visual assets.

6. Build where incumbents aren’t, can’t, or won’t
Identify areas where GenAI can unlock net-new capabilities, incumbents are slow-moving, or the dominant player fails to imagine what is possible with AI.

7. Win with compound AI systems rather than models
The most successful AI apps focus on adding value at the data and infrastructure level; they win through novel architectural approaches, including chain-of-thought, tool use, agents, and appropriate model choices for the function at hand (versus relying on a single monolithic model).