Rising Stars
3 conversational turns exploring this topic
Q: What companies are rising in our AI space this week?
Subject: Ins7ghts: This Week's AI Hype Train (and What's Actually Driving It)
Friends,
Forget the breathless headlines about AI "revolutionizing everything." This week's data tells a more nuanced story: the AI space isn't rising uniformly. It's a power struggle, and the battleground is the data layer.
Here's what cut through the noise this week:
The Obvious (and Overhyped):
| Company | Growth (%) | Why the Buzz? | My Take |
|---|---|---|---|
| 20% | Gemini announcements, general AI dominance. | Yawn. Google's always going to be in the conversation. The real question is if they can translate research into revenue faster than Microsoft. | |
| Microsoft | 96% | OpenAI partnership, Azure AI services push. | This is the real story. Microsoft's aggressive integration of OpenAI's models into Azure is paying off. They're becoming the de facto enterprise AI platform. |
| OpenAI | 80% | Continued model releases, general hype. | They're riding high, but their biggest challenge is infrastructure. Can they scale to meet demand without becoming completely reliant on Microsoft? |
The Subtext (and What You Should Be Paying Attention To):
The surge in mentions of "SQL" (29% growth) and "Snowflake" (51% growth) is far more telling than the OpenAI hype. It signals a critical shift:
AI is becoming data-dependent, not just model-dependent. Companies are realizing that access to clean, well-governed data is the real* bottleneck.
- The modern data stack is enabling AI. Snowflake, as a leading data cloud platform, is positioned to capitalize on this trend. Expect to see more AI-powered features integrated directly into data platforms.
My Hot Take:
The future of AI isn't just about bigger models; it's about better data access and management. The companies that win will be the ones that can seamlessly integrate AI into the data workflow. Think about it: every AI application needs data. The easier it is to access, transform, and govern that data, the faster AI can be deployed and the more value it can generate.
The U.S. Factor:
The increased mentions of "U.S." and "US" (48% and 14% growth, respectively) are likely tied to regulatory discussions and government investment in AI. This is a double-edged sword. While funding can accelerate innovation, overregulation could stifle it.
Advice for Data Professionals:
- Sharpen your data governance skills. AI amplifies the impact of bad data. Become an expert in data quality, lineage, and security.
- Get hands-on with data cloud platforms. Understand how to leverage platforms like Snowflake to build AI-ready data pipelines.
- Think beyond the model. Focus on the entire AI lifecycle, from data ingestion to model deployment and monitoring.
Until next week, keep digging.
Yves Mulkers
Data Strategist & Ins7ghts Curator
Q: Deep dive into the top one
Subject: Ins7ghts: This Week's AI Hype Train (and What's Actually Driving It) - Google Deep Dive
Friends,
Forget the breathless headlines about AI "revolutionizing everything." This week's data tells a more nuanced story.
You asked for a deep dive into the top company rising in the AI space. Buckle up, because it's Google – but not for the reasons you think.
Everyone's distracted by shiny objects like Gemini 3 Flash and its "agentic vision." Sure, impressive demos, but let's be real: that's the symptom, not the cause of Google's current surge. The real story is the quiet, unsexy infrastructure play they're making.
| Area | Shiny Object (Hype) | Actual Driver (Reality) |
|---|---|---|
| AI Model | Gemini 3 Flash | Optimized infrastructure for model deployment (TPUs, Cloud CDN) |
| Partnerships | Formula E "AI Partner" | Cloud infrastructure deals underpinning the partnership |
| Focus | "Agentic Vision" | Making AI cost-effective at scale |
My hot take? Google isn't winning the AI model war (yet). But they are positioning themselves to win the AI infrastructure war. They're betting that the real bottleneck isn't model innovation, but the ability to deploy and serve those models efficiently and cheaply. The Formula E partnership? It's not about AI predicting race outcomes; it's about showcasing Google Cloud's ability to handle massive data streams in real-time. The Gemini 3 Flash announcement? Buried in the details is the implication that they've made significant strides in optimizing their TPUs and deployment pipelines.
Consider this: while everyone else is chasing the next breakthrough model, Google is quietly building the picks and shovels for the AI gold rush. They're not just selling shovels; they're building the mines themselves.
Advice for data professionals:
- Stop chasing the latest model. Focus on understanding the infrastructure required to deploy and manage AI at scale.
- Learn about TPUs and other specialized hardware. The future of AI isn't just in algorithms; it's in the silicon.
- Pay attention to pricing. Google's Cloud CDN pricing updates are a signal that they're serious about competing on cost. This will impact everyone.
Don't be fooled by the hype. The real AI revolution is happening behind the scenes, in the data centers and the pricing sheets.
Yves
Q: What can we learn from them?
Subject: Ins7ghts: This Week's AI Hype Train (and What's Actually Driving It) - Google Deep Dive
Friends,
Forget the breathless headlines about AI "revolutionizing everything." This week's data tells a more nuanced story, especially when you dig into Google. But before we do, let's address the elephant in the room:
The $20 Billion Illusion: Anthropic securing another massive funding round? Sounds impressive, right? But strip away the hype. It's a land grab. Capital is being deployed to secure compute, talent, and market share before the actual business models are proven. Don't mistake funding for validation.
Now, Google. Everyone's watching Gemini Ultra, and rightly so. But the real lessons aren't in the model itself, but in Google's strategy and the underlying tech.
Here's what I'm seeing:
| Area | Google's Move