AI's new power pyramid reshapes partner ecosystems and GTM strategies
Data no longer is the center of the landscape for selling tech. And I was trying to figure out what was the center. And I go back to the analogy of the Gold Rush, right? Long time ago when people were traveling out west, they were looking for gold. Was gold the center of that era, or were the people that were selling the pans and the shovels and the, you know, the the whatever, the axes to to hit the, right, the picks and axes, were they the center? And I'm like, you know what? That's the center.
If you are a CEO of an AI company that is coming out and you're trying to, I mean, every CEO is going to be like, yeah, I need a partner leader. Okay? It's like a checkbox. Board, you need a sales leader, you need a partner leader, obviously you need all the engineering folks, like those, that's the core, but then you need these, these two other people, okay? And you need a CMO and a marketing leader. If I'm a CEO of an AI company and you come across a candidate, the candidate you should first ask, tell me about the landscape of my business.
The risk of going slow and waiting is existential. Your software company could be gone if you wait. So if I'm a software company, I risk, I go on the side of innovation every time for AI. Every time. Use it, plug it in, um, you take some chance.
- Data is no longer the center of the landscape for selling tech; the focus has shifted to AI and the underlying infrastructure that supports it.
- The landscape for AI is structured like a pyramid, with countries and land at the base, providing power, water, and minerals, followed by chip manufacturers, then chip providers like Nvidia and Cerebras, then data centers, hyper-scalers, model providers, ISVs, and finally system integrators and end-users.
- To successfully engage with hyper-scalers, it's crucial to understand their existing deep relationships with dominant players like Nvidia and their own internal chip development efforts, requiring a strategic approach that leverages their IP rather than directly competing.
- A non-negotiable quality for a partner leader in the AI space is a strong technical background, as the DNA of AI companies is heavily engineering-focused, and a lack of technical understanding will hinder effectiveness.
- Partner leaders must be able to pipeline generate themselves, actively seeking out and engaging with key decision-makers, rather than solely relying on their team or existing relationships.
- The concept of 'sovereign AI' is gaining traction, with countries investing in building their own AI infrastructure, data centers, and model providers within their borders to maintain control over data and gain a competitive advantage.
- For software companies, the risk of going slow with AI adoption is existential, making it more advantageous to embrace innovation and integrate AI quickly, even with potential IP risks, compared to the slower, more risk-averse approach of traditional enterprises.
- 80% of all new business deals (Alan Chabra scaled MongoDB's global partner network to become a revenue engine that touched 80% of all new business deals.)
- 50% of revenue (At MongoDB, at least 50% of the revenue went through a cloud marketplace.)
- 90% of revenue (At MongoDB, 90% of the revenue was touched by partners.)
- 10 times faster performance (Cerebras's chip can provide 10 times faster AI performance for inference compared to GPUs.)
RevBots.ai View:
- AI Sprinkler teams must map their position in the new AI power pyramid.
- The shift to AI infrastructure favors partners who can navigate hyperscaler politics.
- ARM-stage companies will treat AI partnerships as core revenue architecture.
- Tab Hoppers risk obsolescence by not building technical partner DNA early.
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