In the Age of AI, Every Partnership Becomes a Data Deal
If you're building partnerships in 2025 and you're not thinking about data—you're not really doing partnerships.
Remember when a "strategic partnership" meant putting each other's logos on a website and maybe co-hosting a webinar? Those days are over.
I was talking to a partnerships director at a major SaaS company last week, and she told me something that stopped me in my tracks: "We don't even call them partnerships anymore. We call them data arrangements." She wasn't being cynical—she was being honest about what actually drives value in 2025.
The rise of AI has fundamentally rewired what partners bring to the table. Distribution still matters, but it's table stakes. The real question now is: what data are you sharing, and how does it make both companies smarter?
The New Partnership Currency
Here's what's changed: AI doesn't run on good intentions or handshake deals. It runs on data. And the companies building the best AI products aren't just collecting their own data—they're orchestrating intricate webs of data partnerships that would make a 1990s media conglomerate jealous.
Every modern integration has an unspoken second layer. Sure, your CRM might integrate with their email platform to sync contacts. But what's really happening is that their AI is learning from your customer interaction patterns, while your algorithms get smarter about predicting email engagement based on their delivery data.
Partnerships are no longer just business development. They're data architecture decisions.
Three Case Studies Where Data Is the Real Deal
Case 1: Red Ventures and OpenAI - The Content Intelligence Play
Red Ventures runs a portfolio of high-intent websites like Bankrate, Healthline, and CNN Underscored. On the surface, their partnership with OpenAI looks straightforward: Red Ventures' content surfaces in AI assistant responses, driving traffic back to their sites.
But dig deeper and you'll see the real value exchange. Red Ventures has spent years building structured content taxonomies—they know exactly which financial questions drive conversions, which health topics generate the most engagement, and how to present complex information in digestible formats.
For OpenAI, this isn't just another content licensing deal. It's access to a masterclass in human information needs, packaged as training data. Red Ventures gets AI-driven traffic; OpenAI gets a PhD in content optimization across multiple verticals.
Case 2: Snowflake and NVIDIA - Where Your Data Lives Determines Everything
This partnership was announced as a way to bring AI compute closer to enterprise data. NVIDIA provides the processing power; Snowflake provides the platform where that data already lives.
But consider the intelligence NVIDIA gains from this arrangement. They get anonymized insights into how different industries structure their data, which types of queries are most common, and how enterprise AI workloads actually behave in production. This isn't just about selling more chips—it's about understanding the enterprise AI landscape better than anyone else.
Meanwhile, Snowflake gets to offer AI capabilities without customers having to move their data anywhere. The partnership creates a moat around both companies' core businesses while generating valuable insights for future product development.
Case 3: Spotify's Transcription Strategy - Free Tools, Valuable Insights
Spotify offers podcasters free transcription services powered by OpenAI's Whisper technology. Creators get searchable transcripts; Spotify gets to be helpful.
But here's what's really happening: every transcript becomes indexed, searchable data. Spotify is building the world's largest database of spoken content, categorized by topic, sentiment, and engagement patterns. They're learning what topics resonate with different audiences, how successful creators structure their content, and which conversation patterns predict viral episodes.
This data doesn't just improve Spotify's recommendation algorithms—it positions them to become the intelligence layer for the entire podcasting ecosystem. They can tell creators not just what's popular, but what's about to become popular.
Think Strategically About Data Partnerships
Not every partnership needs to be a data deal. But if you're in the AI business—or soon will be—these are the right questions to ask:
→ What data is flowing, and in which direction?
Be clear on what’s shared, what’s enriched, and what’s retained.
→ Who benefits from model improvement?
Is one party’s system getting meaningfully better while the other sees little change?
→ Is the value unique to this partnership?
Great partnerships generate compound insights that neither side could achieve alone.
→ What happens if it ends?
Practical, not pessimistic. What lives on—product features, trained models, user behavior?
The Bigger Picture: Data as Leverage
The companies winning in this new model aren’t necessarily louder about partnerships. But they’re more intentional.
They’re designing interactions—APIs, integrations, co-builds—that accelerate learning across their ecosystem. They’re less focused on surface-level alignment, more focused on defensibility through intelligence.
If you lead partnerships today, the real opportunity isn’t just expanding your footprint. It’s tuning your feedback loops.
Data isn’t a byproduct anymore.
It’s the point.
And the best partnerships reflect that.
On Monday, we’ll explore what modern AI-native partnerships actually look like—lean, measurable, and high-impact