The Artificial Investor #55: Mag7’s battle for AI dominance heats up
✍️ The prologue
My name is Aris Xenofontos and I am an investor at Seaya Ventures. This is the deepdive version of the Artificial Investor that comments on one of the top AI developments of the last few days.
This Week’s Story: Mag7’s battle for AI dominance heats up
Meta doubled down on its AI ambitions recently, making headlines with a massive 14 billion-dollar “investment” in Scale AI, through which it acquired a 49% stake and brought Scale’s CEO, Alexandr Wang, in-house to help lead its newly formed “Superintelligence Labs”. At the same time, Meta has aggressively courted top AI talent from OpenAI, reportedly offering eye‑popping packages, such as 100 million-dollar signing bonuses. These moves highlight Zuckerberg’s intent to put Meta into a leading position in the GenAI race.
Which Mag7 is best positioned to dominate in an AI world? Who is winning and who is losing currently? What are the Big Tech’s strategic goals? What are they likely to do next? What does this mean for investors and founders in the space?
Let’s dive in.
🧱 Current status across the AI stack
Nvidia is the only infrastructure pure-play and the hyperscaler that has monetised best the AI gold rush so far. Many are concerned about their dependence on Nvidia and are developing their own AI chips. The company has tilted from consumers towards businesses in the last 3 years.
Going further up the stack, Microsoft and Amazon are primarily infrastructure aggregators through their leading Cloud offerings. Microsoft has better access to businesses via Windows and Office365, whereas Amazon to consumers via Amazon.com and Alexa.
Google and Meta focus on the consumer application layer, while Google is also cutting across multiple layers of the stack. These two players have the strongest data moats and are the only ones with proprietary AI model capabilities.
Apple and Tesla are the most vertically-integrated players and are focused on consumers with premium distribution. Tesla owns an AI-native stack end-to-end, whilst Apple has hardware and software that currently feels a bit outdated.
🎯 Strategic goals and AI gaps
Before we make any predictions about the future strategic moves of the main players, let’s analyse first their core strategic goals and AI gaps.
Apple
🎯 Goal: Maintain dominance of Tech distribution to consumers
⚠️ Gaps: No proprietary AI model (weak OpenAI partnership), basic AI Cloud, outdated AI applications (iOS, Siri)
Microsoft
🎯 Goal: Remain the one-stop-shop for Enterprise AI
⚠️ Gaps: No proprietary AI model (fragile OpenAI partnership), Copilot and Copilot Studio (AI agent platform) have low utility
🎯 Goal: Protect Search and Android
⚠️ Gaps: AI Search product is behind ChatGPT, AI Search monetization model still unclear, proprietary mobile models (e.g. Gemini Nano) not widespread
Amazon
🎯 Goal: Maintain leadership in Cloud and e-commerce
⚠️ Gaps: Weak proprietary model (Nova, Anthropic dependence), poor AI device (Alexa), nascent proprietary AI compute
Meta
🎯 Goal: Maintain leadership in consumer apps
⚠️ Gaps: Weak proprietary model (Llama), no compelling AI consumer product (Meta AI)
Nvidia
🎯 Goal: Maintain its hardware moat
⚠️ Gaps: Weaker in the fastest-growing AI use case (inference), doesn’t offer a proprietary AI chip alternative to Cloud hyperscalers
Tesla
🎯 Goal: Maintain leadership through vertically-integrated products
⚠️ Gaps: Weak proprietary model (xAI), sub-scale infrastructure (Dojo)
🔮 Predictions
We expect Apple to either deepen its relationship with OpenAI or Anthropic with exclusive partnerships and investment to close the model quality gap or acquire/ invest heavily in another frontier lab, e.g. xAI, to hedge against dependency. The Cupertino company should also expand its AI-native hardware product line beyond iPhone, with smart glasses, smart home hubs and other ambient AI interfaces.
Given the turmoil in its relationship with OpenAI, Microsoft will likely accelerate funding to Mistral or xAI. On the Enterprise side, the company’s co-pilot and agentic offering are underwhelming; we expect an acquisition either in the AI Orchestration (e.g. LangChain or Orby) or the Agent Low-/ No-code Dev (e.g. Adept) space. The Redmond company should also do something with Bing AI - either kill it or relaunch it with integration into its strong consumer offerings, Xbox and LinkedIn.
Google’s priority is to protect its multi-billion-dollar cash cow and crown jewel: search. Watch it rebuild its advertising around AI with new monetisation formats for Overviews, recommendation flows (e.g. sponsored suggestions in AI) and “Google Analytics for Conversation”. Also, we wouldn’t be surprised if we saw the Mountain View company acquire a vertical AI search player (e.g. Perplexity). Finally, Google will need to avoid its Google Workspace becoming obsolete and lose its data feedback loop by launching a true AI agent product.
Amazon needs to strengthen its AI model position and the easiest way is to strengthen its Anthropic partnership with further equity and joint infrastructure. But, this won’t be enough to cover for Alexa’s shortfall. Further AI hardware bets are expected in areas like wearables and ambient AI. Given the Seattle company’s limited strategic AI assets, there could be a case for expanding its AWS offering by acquiring AI Inference infrastructure (e.g. Modular).
Llama needs further investment and the good news is that Zuckerberg looks ready to accept his mistakes and strengthen his AI organisation with more external top-notch talent. The Menlo Park company will likely also invest heavily in multi-modal user experiences to combine video, text and voice. The end game would be to turn WhatsApp and Instagram into full AI interface layers.
Nvidia’s natural move is to lock in governments and global sovereign clouds (India, EU, UAE) via state-scale GPU leasing deals, capitalising on local AI sovereignty strategies. The Santa Clara company’s bold move could be to acquire inference-native chip startups (e.g. Groq) to plug performance gaps and defend its edge. A safer bet would be the launch of a full-stack AI agent infrastructure hosted in DGX Cloud.
Tesla sooner or later will likely formally integrate xAI into its product stack. We wouldn’t be surprised to see an acquisition of a planning/agent startup (e.g. Rebellions) that will help build reasoning layers into its robotics capabilities. Having these two infrastructure gaps sorted out, the Austin company would be well positioned to expand further its AI hardware products (e.g. Optimus robots for factories or logistics) and, thus, strengthen its data flywheel for embodied AI.
💵 Show me the money
Naturally, some interesting opportunities arise for investors. Below are our Buy/Sell recommendations.
📈 Buy
Model providers (e.g. Mistral, xAI, Cohere)
AI agent infrastructure (e.g. Modular, LangChain, AutoGen, Orby)
Specialised AI inference infrastructure (e.g. d-Matrix, Groq, Cerebras)
Enterprise agent dev tools (e.g. Adept, Crew AI, Thread AI)
Edge chip players (e.g. Rain AI, Mythic)
Vertical AI search startups (e.g. Perplexity, You.com)
Wearable and ambient AI manufacturers (e.g. Bee AI)
📉 Sell
Productivity or creativity apps that rely solely on OpenAI APIs (e.g., Jasper, Copy.ai).
Model labs without Cloud backing (e.g. AI21, Aleph Alpha, Writer)
General-purpose chatbots without unfair distribution advantages
Legacy voice assistant players (e.g. SoundHound, Snips, Mycroft AI)
API companies providing prompt injection detection, evals, simple RAG pipelines
🧭 Strategic Guidance
Before we close, we also wanted to include some basic tips for entrepreneurs building in AI:
Align with Mag7 gaps. Whether it’s inference, UX or vertical specialisation, find the blind spots they must close.
Own distribution or data. If you don’t have infrastructure or models, having attention or proprietary data is essential.
Don’t just use models; rather, integrate them into defensible workflows. That’s where moats come from.
See you next time for more AI insights.