Platform Selection | Ins7ghts Q&A

Turn 1

Q: What foundational technologies should our AI platform include?

Hey Ins7ghters,

Everyone's drooling over the latest LLM, but building a robust AI platform isn't about chasing the shiniest new toy. It's about the plumbing. This week's analysis of 1,800 articles reveals a glaring disconnect: everyone's talking about AI, but nobody's focusing on the boring (but essential) foundations.

The Provocative Truth: Your AI platform will fail spectacularly without a rock-solid data foundation. Period.

Let's cut through the hype and get real. Here's what you actually need:

Foundation	Why It Matters	My Hot Take
Data Integration	Forget vendor lock-in. You need a platform that can ingest data from anywhere. Think beyond APIs. Consider event-driven architectures (Kafka, Pulsar) for real-time data streams.	Stop building data silos! Invest in a robust data mesh strategy. Look at companies like Thoughtworks and Starburst for guidance.

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Turn 2

Q: Which vendors lead in these?

Hey Ins7ghters,

Everyone's drooling over the latest LLM, but building a robust AI platform isn't about chasing the shiniest new… vendor. It's about boring, reliable infrastructure. Last time, we talked about the foundational technologies. Now, let's talk vendors.

Hot Take: The "leader" in any of these categories today will be disrupted tomorrow. Don't marry a vendor; marry open standards.

Here's the truth: no single vendor dominates across the entire stack. You'll be stitching together solutions. The real question is: who's building the best components?

Let's break it down by layer, focusing on momentum and adoption (based on this week's data):

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Turn 3

Q: What's the integration evidence?

Hey Ins7ghters,

The "best" AI platform is the one that actually gets used. And that hinges on integration, not just raw algorithmic power.

Forget pie-in-the-sky demos. I want to see battle scars. Who's really connecting the dots between these foundational layers we talked about last time?

Here's the inconvenient truth: most vendors are still selling point solutions, not platforms. They talk integration, but their tech tells a different story.

So, where's the evidence? This week's data from ~1,800 articles paints a clear (if slightly depressing) picture. Look at the negative growth in key hubs: AI, Data Integration, Data Engineering, Data Analytics, Data Management, and Data Pipelines are all down significantly. This isn't just seasonality; it's a sign that the hype cycle is outpacing real-world implementation.

Here's my take on who's showing actual integration capabilities, and where the gaps remain:

Try this conversation (3 turns) →