Founders must consider their sales motion (e.g., PLG vs. enterprise sales-led) when designing the product. A product built for one motion won't sell effectively in another, potentially forcing a costly redesign. This concept extends "product-market fit" to "product-market-sales fit."

Startups often fail to displace incumbents because they become successful 'point solutions' and get acquired. The harder path to a much larger outcome is to build the entire integrated stack from the start, but initially serve a simpler, down-market customer segment before moving up.

Stop targeting the ambiguous "mid-market." Your strategy, hiring, and ACV must align with either a marketing-led SMB motion or a sales-led enterprise motion. Blending them leads to failure as they are distinctly different games.

While consolidating tools seems efficient, using specialized, best-in-class AI agents for each GTM function (one for outbound, one for inbound) yields superior results. The depth and focus of specialized tools enable more powerful and nuanced use cases, justifying the management overhead of multiple systems.

Large enterprises don't buy point solutions; they invest in a long-term platform vision. To succeed, build an extensible platform from day one, but lead with a specific, high-value use case as the entry point. This foundational architecture cannot be retrofitted later.

Most successful SaaS companies weren't built on new core tech, but by packaging existing tech (like databases or CRMs) into solutions for specific industries. AI is no different. The opportunity lies in unbundling a general tool like ChatGPT and rebundling its capabilities into vertical-specific products.

Stop thinking of sales, marketing, and support as separate functions with separate tools. AI agents are blurring these lines. A support interaction becomes a lead gen opportunity, and a marketing email can be sent by a 'sales' tool. Prepare for a unified go-to-market operational model.

Traditional software required deep vertical focus because building unique UIs for each use case was complex. AI agents solve this. Since the interface is primarily a prompt box, a company can serve a broad horizontal market from the beginning without the massive overhead of building distinct, vertical-specific product experiences.

The sweet spot for their transformational AI platform wasn't the largest corporations, which are too rigid to adopt new tech. Instead, it was mid-market companies (100-1,000 employees) that had budget and pain but were agile enough to implement new workflows successfully.