ChatGPT prompt:
I may be missing something, but in my mind there's a distinct difference between "cloud services" and "AI." "Cloud services" is all-encompassing, whereas AI is simply a very, very sophisticated tool that is critical to "cloud services." So, for example, it doesn't matter to a great extent who provides the AI component; what is critical is that AWS, META, Apple, Microsoft, etc, have superior "cloud services." Does that make sense?
ChatGPT reply:
Yes — that makes perfect sense, and you’ve put your finger on a distinction a lot of people blur right now. Let’s break it out clearly:
1.
Cloud Services = The Platform Layer
-
Broad, foundational infrastructure: compute, storage, networking, databases, developer tools, orchestration, compliance.
-
Goal: Let enterprises and developers run anything — from websites to supply-chain software to AI models — without worrying about servers.
-
Example: AWS S3 (storage), Azure SQL Database, Google Cloud Pub/Sub.
-
Characteristics: Sticky, high-margin, mission-critical. Customers often stay for decades once workloads migrate.
2.
AI = A Workload (though a huge one now)
-
AI is one category of service running on top of cloud infrastructure.
-
Generative AI (LLMs, multimodal models, copilots) is today’s hot workload, but it’s just one among many.
-
Example: Training GPT on Azure, or serving Meta’s LLaMA on its in-house MTIA chips inside Meta’s data centers.
-
Characteristics: Capital-intensive, fast-moving, can become commoditized if models converge in quality.
3.
Where They Intersect
-
AI drives demand for cloud. Training LLMs eats thousands of GPUs; inference at scale is also compute-hungry.
-
Cloud monetizes AI. AWS Bedrock, Azure OpenAI Service, Google Vertex AI are essentially AI wrappers around cloud primitives.
-
Control Point: Owning the cloud lets you “tax” all AI activity, no matter which model wins.
4.
Why Cloud Services Are the Real Moat
-
Switching Costs: Migrating your ERP or warehouse management system from AWS to Azure is a nightmare. Switching AI APIs (say, OpenAI to Anthropic) is much easier.
-
Breadth of Revenue: Cloud gets paid for every type of workload — AI, web apps, analytics, streaming, IoT, gaming.
-
Durability: If AI hits a hype winter or becomes commoditized, cloud demand still grows from other workloads.
So your instinct is right:
AI is a massive, strategic workload but ultimately just one component of the much broader “cloud services” ecosystem. What really matters for AWS, Microsoft, Google, Meta, Apple, etc., is whether they can deliver the most reliable, integrated, cost-effective cloud platform.
AI may crown a few short-term winners, but the long-term power is in the cloud rails themselves.
Would you like me to sketch out what happens if AI does get commoditized — and how AWS/Azure/Google Cloud could still thrive (while pure AI labs might struggle)?
Yes, that would be interesting:
Great — let’s play out the “AI becomes commoditized” scenario and see who survives best:
What We Mean by “Commoditized AI”
-
LLMs and multimodal models converge in quality (like search engines in the 2000s, or cloud storage today).
-
Switching between providers becomes easy: “drop-in replacement APIs.”
-
Price per token/query collapses as competition + open source undercut margins.
-
Differentiation shifts from “whose model is smartest” → “who integrates it best into workflows.”
Who Wins, Who Struggles
✅
Cloud Titans (AWS, Microsoft, Google Cloud, partially Meta)
-
Why they win:
-
AI is just another workload running on their cloud.
-
Even if models become cheap, training & serving them still burns massive compute, storage, and networking — all of which clouds monetize.
-
They control the rails (compute + data pipelines), not just the train (the model).
-
-
Analogy: Cloud is like the electric grid; AI is just one type of appliance. If toasters get cheap, the grid still wins.
⚠️
Pure AI Model Companies (OpenAI, Anthropic, Mistral, Cohere)
-
Why they struggle:
-
If models converge and prices collapse, they risk becoming the “commodity layer.”
-
Cloud giants can undercut or bundle their own open-source models for free (e.g., LLaMA or Phi models).
-
Survival depends on building sticky applications (Copilots, vertical AI assistants) or licensing deals with enterprises.
-
-
Analogy: Like search engines that weren’t Google in the early 2000s — most got crushed once results quality converged.
⚠️
Chipmakers (Nvidia, AMD, startups)
-
Why mixed:
-
If AI demand grows despite commoditization, chip volumes stay strong.
-
But if margins collapse and hyperscalers double down on custom silicon (TPUs, AWS Trainium, Meta MTIA, Apple NPUs), Nvidia & others lose pricing power.
-
-
Analogy: Like when Apple ditched Intel for in-house silicon — great for Apple margins, bad for Intel.
✅
Device/Edge Players (Apple, Qualcomm)
-
Why they win:
-
If cloud AI APIs are interchangeable, differentiation moves to the experience layer (devices + apps).
-
Running small/efficient models locally means lower cost, lower latency, better privacy — all sticky advantages.
-
Apple’s ecosystem makes it easier to tie AI into everyday life.
-
-
Analogy: Think iOS vs. Android — both use many “commodity” components, but the ecosystem + UX create moat.
🔹 Strategic Pivots if AI Commoditizes
-
AWS / Azure / GCP: Will bundle AI APIs into cloud suites (like Office 365, CRM, ERP) and price them close to zero — making money from compute/storage/networking behind the scenes.
-
Meta: Will keep open-sourcing (LLaMA), betting on ecosystem growth to drive engagement, ads, and maybe device tie-ins.
-
Apple / Qualcomm: Push on-device AI to make hardware upgrades irresistible (new iPhone/PCs with NPUs that can run your personal assistant offline).
-
OpenAI / Anthropic / Mistral: Must pivot from selling “raw intelligence” to selling domain-specific copilots, compliance layers, or enterprise integrations.
🔹 Key Takeaway
If AI gets commoditized, the long-term winners are the cloud providers and device/edge ecosystems, because they own the distribution rails.
Pure AI labs may find themselves in the same place as early ISPs or web browsers: critical to the revolution, but not where the enduring profit pools are.
👉 So in short: commoditized AI helps the rails, hurts the riders.
Tagged: clouds and AI rails.
No comments:
Post a Comment