I began discussing AI centric business strategies in 2016, when several Big Tech companies started addressing Machine Learning and AI in their shareholder reports, strategy presentations, and earnings calls. US tech giants’ outsized market caps—many in the $2–4 trillion range by 2025—directly reflect investor belief in their AI and data leadership and the future revenue potential this unlocks. In “The AI Imperative: A Practical Roadmap for Business” (Praeger, 2018), I identified several common characteristics of the AI-centric companies. All these hold until today and explain the valuation uptake of technology businesses in the US and China.
I will introduce seven critical factors enabling AI-centricity, using examples from the top five companies on the US West Coast.
7 Distinct Critical Factors enabling an AI Centric Business
1. Aggressive commitment to AI from the top leadership since the early 2010s
AI commitment at Big Tech companies is propelled by decisive action and direct engagement from top leadership: Mark Zuckerberg at Meta and Sundar Pichai at Alphabet have both restructured leadership teams and ramped up investments to ensure AI sits at the core of strategy and innovation. At Microsoft, Satya Nadella has reshuffled key roles to integrate AI across productivity tools and emphasised responsible AI development, while Apple’s executives like Craig Federighi and Kim Vorrath have been tasked to steer a product-driven AI transformation.
Alphabet (historically Google) has openly discussed AI in its annual shareholder meetings since at least 2017, with Sundar Pichai calling Google an “AI-first company.” AI has been strategically referenced in the company’s annual reports since the mid-2010s.
From its earliest days, Amazon used AI for personalised recommendations, which currently drive around 35% of its sales. By the early 2010s, Amazon intentionally reorganised itself to leverage AI and ML in every part of its organisation, developing a culture where these technologies underpinned business innovation and customer experience across all divisions. A pivotal moment came in 2014–2015 with the introduction of the Amazon Echo (powered by Alexa) and the launch of Amazon Machine Learning, AWS’s first ML service for developers.
Machine learning and artificial intelligence (AI) became central to Meta’s strategy in the early 2010s, when the company—then Facebook—identified AI as one of the key future technological platforms it wanted to control, alongside AR/VR. Early on, Meta formed its Facebook AI Research (FAIR) group in 2013, led by AI professor Yann LeCun.
Apple’s acquisition of Siri (first integrated in iPhone 4S, 2011) marked its initial public foray into AI-driven user experiences. The company built on this with machine learning for features like typing prediction, personalised recommendations, and facial recognition throughout the 2010s. Apple introduced the Core ML framework in 2017, allowing developers to integrate machine learning into their apps, and debuted its Neural Engine hardware for efficient, on-device AI tasks. This represented a strategic shift to on-device intelligence and privacy-focused machine learning. Apple’s public positioning evolved with the announcement of “Apple Intelligence” at WWDC in June 2024.
2. Proprietary data and data-driven culture
Proprietary data has been a central pillar in establishing and maintaining AI leadership for Big Tech companies. Each company draws upon unique, large-scale, and hard-to-replicate data assets that underpin their AI R&D, product development and delivery, and ecosystem advantages. As an example, Microsoft’s key AI asset is the proprietary business and productivity data within Microsoft Graph—encompassing enterprise emails, documents, meetings, chats, and workflow telemetry across Outlook, Teams, SharePoint, and more. Meta’s AI “data moat” is composed of a continuous stream of user-generated content, social interactions, and behavioural signals across its massive global platforms.
Amazon’s deep advantage lies in access to proprietary data from its retail, AWS, voice assistant (Alexa), and logistics/fulfilment operations. This includes transaction histories, product metadata, reviews, supply chain events, and user engagement data—constantly refreshed from real customers and partners.
In technology companies, teams are equipped and incentivised to collect, manage, and experiment with data to innovate and make existing businesses and products more efficient. Their technical know-how to unify, clean, and analyse data from multiple sources is unmatched by industrial incumbents like healthcare or transportation.
3. Data centres and infrastructure
All mentioned technology giants invested heavily in data centre infrastructure to provide agility and flexibility to their AI building efforts.
As an example, Meta committed massive capital expenditures ($64-72 billion in 2025) on data centres. This investment fortified AI-driven ad targeting and delivery across Facebook, Instagram, and WhatsApp. At Amazon, investments of $5 billion in Ohio and $11 billion in Georgia (2020–2025) expanded AWS infrastructure, powering AI workloads for both Amazon and third-party clients. AWS’s revenue run rate hit $110 billion in 2025, with AI as a major growth lever. In all companies, innovations in designing and building data centres include unique facility layouts, custom hardware designs, and state-of-the-art cooling and power systems.
4. Designing and implementing customised chipsets
Meta, Amazon, Apple, and Google have all invested heavily in developing their own custom AI chipsets to address the growing demands and costs of AI. Their motivations and approaches share some common ground but also reflect unique strategic requirements and ambitions. Custom chips allow them to optimise for their algorithms and processing requirements, yielding significant improvements in efficiency and throughput.
Owning the chip stack gives these companies more control and secrecy over their AI development, which can translate into faster innovation cycles and unique features that competitors using off-the-shelf hardware may find difficult to replicate.
5. Proprietary models and designs
The companies possess proprietary technologies for AI modelling that are built to complement, enlarge, and even partly commoditise their core business. As an example, Llama, and initiatives like the Meta AI app have made AI central to user experiences and advertising efficiency, driving impressive revenue gains and stock surges—Meta’s shares spiked 10% in July 2025 after doubling AI investment.
6. Engaging top university professors to lead AI efforts
Big Tech companies attract, afford and retain the best researchers, continually refreshing their technology edge. Several prominent AI scientists have held positions at Meta, Amazon, Apple, and Alphabet while retaining professorships at major universities. Here are well-documented examples:
Meta (Facebook AI Research)
- Yann LeCun: Chief AI Scientist at Meta (formerly Facebook), and Silver Professor at New York University (NYU). He has maintained his faculty position while leading Meta’s AI research.
- Adriana Romero Soriano: Research scientist at Meta AI and adjunct professor at McGill University.
Amazon
- Ying Nian Wu: Professor of Statistics at UCLA, joined Amazon as a scholar.
- Yizhou Sun: Associate professor of computer science at UCLA, also joined Amazon as a scholar.
- Cho-Jui Hsieh, Kai-Wei Chang, Violet Peng: Assistant professors at UCLA, have held Amazon visiting academic roles.
Apple
- Carlos Guestrin: Named part of a University of Washington professorship endowed by Apple after his AI startup Turi (founded while he was a UW professor) was acquired; he continued to serve as a UW professor and director of machine learning at Apple.
- Ruslan Salakhutdinov: Hired as Apple’s Director of AI Research while retaining his professorship at Carnegie Mellon University.
Alphabet
- Geoffrey Hinton: A renowned expert in neural networks, Hinton was splitting his time between Google and his faculty position at the University of Toronto, significantly shaping advancements in deep learning as part of the Google Brain project.
- Andrew Ng: While on sabbatical from his academic position at Stanford, Andrew Ng spent a period at Google and collaborated with Google researchers, helping to establish the foundations of the Google Brain team.
- John Canny (UC Berkeley): Canny, a professor at UC Berkeley, spent part of his sabbatical at Google working on specialised chips (TPUs) for AI algorithms, returning to his university post after his stint.
- Katya Scheinberg (Lehigh University): A professor focusing on optimisation problems, Scheinberg spent a sabbatical at Google, after which she returned to her university, noting how the experience influenced her subsequent research focus.
- Andrew Moore (Carnegie Mellon University): Moore, a former Dean at CMU, took a leave of absence to set up Google’s Research Centre in Pittsburgh, highlighting another example of a university leader deeply involved with Google while maintaining academic ties.
- Fei-Fei Li did work at Google, where she served as Vice President and Chief Scientist of AI/ML at Google Cloud from January 2017 to September 2018. During this period, she was on sabbatical from her academic post at Stanford University.
7. Captive Venture Capital investments in AI
Every Big Tech company possesses a captive VC arm to scout, acquire, and manage machine learning and AI companies.
As an example, Apple acquired 32 AI startups in 2023. Their M&A strategy focuses on smaller, early-stage firms and technology acquisitions. Alphabet invested billions in AI—examples include multi-billion-dollar rounds in Anthropic, and an early acquisition of DeepMind. Alphabet/Google is usually among the top 3 or 4 corporate AI investors annually. Meta’s corporate venture arm made its largest-ever investment with its ~$14 billion acquisition of a 49% stake in Scale AI in 2025.
The OpenAI -Microsoft deal is known as one of the largest in history (a $10 billion investment). Microsoft also participated in a $1.3 billion round for Inflexion AI and invested in other startups.
Interestingly, the same seven characteristics can be found in Chinese AI centric business giants Tencent, Baidu, and Alibaba. Add to these an incredibly vast ecosystem of partners, suppliers, and political lobbyists, and you can speculate about the sustainability of Big Tech companies.