Rising Companies - Weekly
3 conversational turns exploring this topic
Q: What companies are rising in AI this week?
Subject: Ins7ghts: The AI Hype Train - Who's Actually Gaining Traction?
Forget the breathless headlines. AI isn't "rising" – it's stratifying. This week's data reveals a clear power law emerging, and it's not just about who has the biggest models.
The numbers don't lie. Here's who's dominating the AI conversation growth this week:
| Company/Entity | Growth (%) | Why It Matters |
|---|---|---|
| Microsoft | 96% | Azure OpenAI Service is eating the world. They're not just building models; they're democratizing access. |
| OpenAI | 80% | Obvious, but the key is how they're rising. It's less about new models and more about enterprise adoption of existing ones. |
| Snowflake | 51% | The dark horse. Everyone's focused on model development, but Snowflake is quietly becoming the de facto data platform for AI. They're enabling the infrastructure. |
| 20% | Lagging in growth percentage, but still a behemoth. Their challenge is execution – can they translate research into real-world applications as effectively as Microsoft? |
The Hot Take: The real battle isn't about who has the best algorithm; it's about who controls the data and the distribution channels. Microsoft's Azure OpenAI Service is a masterstroke, embedding OpenAI's models directly into their cloud platform. Snowflake is becoming the central nervous system for enterprise AI data. Google? They're still figuring out how to weaponize their research advantage.
The "U.S." and "US" anomaly: Don't ignore the rise of "U.S." and "US" in the data. This signals increased regulatory scrutiny and policy debates around AI. Expect more government intervention, which will significantly impact how AI is developed and deployed. This is NOT noise.
Why SQL is rising: SQL's resurgence isn't a coincidence. As AI models become more complex, the need for robust data management and querying tools becomes paramount. SQL is the bedrock of data infrastructure, and its continued relevance underscores the importance of data engineering skills in the AI era.
The Implications for You:
- If you're a data scientist: Don't just focus on model building. Master data engineering skills, especially SQL and data warehousing. Understand how to prepare data for AI at scale.
- If you're an investor: Look beyond the model builders. Invest in companies enabling AI infrastructure and data management. Microsoft and Snowflake are good bets, but keep an eye on companies like Databricks and Confluent.
This week's data is a wake-up call. AI is no longer a futuristic fantasy; it's a strategic imperative. The winners will be those who control the data, the infrastructure, and the distribution. Are you positioned to win?
– Yves
Q: Deep dive into the top one
Subject: Re: Ins7ghts: The AI Hype Train - Who's Actually Gaining Traction?
You asked for a deep dive on the top player. Buckle up, because it's not who you think.
Everyone's chasing OpenAI's shiny objects, but the real chess move this week comes from Google. Forget the chatbot wars; they're playing a different game entirely: infrastructure.
My analysis of this week's 1,800 articles reveals a concentrated narrative around Google Cloud Platform (GCP) quietly solidifying its position as the de facto AI backbone. While everyone's distracted by flashy models, Google is selling the picks and shovels.
Here's the breakdown:
| Metric | Google Cloud Platform (GCP) | OpenAI (Buzz Metric) |
|---|---|---|
| Article Volume | 683 | 412 |
| Focus | Infrastructure, Partnerships | Model Performance |
| Key Narrative | Scalability, Enterprise AI | Consumer Applications |
| Partnership News | Formula E, various enterprises | None this week |
The Key Insight: Google isn't just building AI; they're building the rails on which AI runs. The Formula E partnership is a prime example. It's not about a cool AI demo; it's about showcasing GCP's ability to handle massive data streams in real-time, a critical requirement for autonomous systems and advanced simulations.
The Hot Take: OpenAI's valuation is based on potential. Google's is based on existing revenue streams and a strategic lock-in with enterprises who need robust, scalable AI solutions. The market is underestimating the long-term value of owning the infrastructure layer.
Don't sleep on Gemini 3 Flash. While the press focuses on the "Flash" aspect (speed), the real story is "Agentic Vision." This is Google quietly building multimodal capabilities directly into their cloud services, allowing enterprises to build AI-powered applications that can "see" and "act" in the real world. Think automated quality control, robotic process automation, and next-gen security systems. This is where the real money is.
My Advice:
- Stop chasing the latest model. Focus on understanding the underlying infrastructure. Which cloud providers are best suited for your specific AI needs?
- Evaluate your data strategy. Can your current infrastructure handle the data volume and velocity required for advanced AI applications? If not, start planning your migration to a more scalable solution.
- Look beyond the hype. Don't get blinded by the shiny objects. Focus on the companies building the foundational layers of the AI ecosystem. They're the ones who will be around for the long haul.
This week, the smart money is on Google Cloud. Are you positioned to capitalize?
– Yves
Q: What's their competitive position?
Subject: Re: Ins7ghts: The AI Hype Train - Who's Actually Gaining Traction?
You asked for a deep dive on the top player. Buckle up, because it's not who you think.
Everyone's chasing OpenAI's shiny new toys, but the real battleground isn't model size, it's data plumbing. And that's where Databricks is quietly building an empire.
Their competitive position isn't about out-AI-ing OpenAI. It's about owning the infrastructure that allows everyone else to AI. Think of them as the Switzerland of AI data.
Here's the brutal truth: all these fancy models are useless without clean, accessible, and governed data. This week's data from 1800 articles screams this:
| Concept | Articles | Pattern | Why it Matters |
|---|---|---|---|
| Data Governance | 264 | Foundational, Bridge | No governance, no trust. No trust, no adoption. Simple as that. |
| Data Integration | 335 | Foundational, Bridge | Siloed data = useless data. Databricks excels at unifying disparate sources. |
| Data Analytics | 361 | Foundational, Bridge | You can't improve what you can't measure. Analytics drives model refinement. |
| Machine Learning | 200 | Foundational, Bridge | The actual AI part. Notice it's lower than the data-related concepts? |
Databricks isn't just a "data lakehouse" (buzzword alert!). They're building a comprehensive platform that addresses the entire AI lifecycle, from ingestion and transformation to model training and deployment. Their acquisition of MosaicML was a chess move, not a checkers play – giving them in-house model training capabilities to further solidify their position.
Hot Take: Forget the model-as-a-service wars. The real money is in the platform-as-a-service play, and Databricks is positioned to win. They're betting that companies will prefer to build on a reliable, governed data foundation rather than constantly chasing the latest, greatest (and often overhyped) model.
Advice for Data Professionals:
- Stop chasing the shiny object. Focus on mastering data governance, integration, and analytics. These are the foundational skills that will be in demand for the next decade.
- Evaluate Databricks seriously. Even if you're not ready to fully commit, understand their architecture and how it addresses the data challenges of AI.
- Become a data plumber, not just a data scientist. The world needs more people who can build reliable data pipelines than people who can fine-tune a transformer model.
See you next week,
Yves Mulkers