When I was building Glue, I watched something remarkable happen across our customer base. Every founder or engineering leader who got access to it had the same reaction: "Why didn't we have this before?" Not because Glue does something magical. But because it does something obvious once you see it - it gives product leaders the same kind of leverage that Cursor gave to engineers.
Cursor didn't invent AI. It didn't invent code completion. What it did was connect AI to your actual codebase. Suddenly, autocomplete wasn't generic - it understood your architecture, your patterns, your tech stack. It watched what you were building and suggested the next line with precision. Engineers shipped faster because the tool was finally contextualized to their reality.
Product managers are still waiting for their equivalent.
The Cursor Moment
Let me be precise about what Cursor actually did, because this matters for where product management is heading.
Before Cursor, you had Copilot. Copilot was trained on the internet. It knew billions of code patterns. It was genuinely useful. But when you opened a file in your specific codebase - with your naming conventions, your architectural decisions, your particular approach to error handling - Copilot had to start from scratch. It would generate technically correct code that didn't match your project.
Cursor changed that. It indexed your codebase. It understood your specific patterns. When you asked it to complete a function, it didn't just know programming - it knew your programming. The result: autocomplete that felt less like a tool and more like a teammate who had actually read all your code.
Engineers got faster. But more importantly, they got more confident. The tool wasn't second-guessing them or suggesting generic solutions. It was grounded in their reality.
That's the moment that matters. Not the AI itself. The grounding.
What Product Managers Actually Need
Now think about what product managers do when they make decisions.
You're sitting in a roadmap meeting. Engineering says a feature request from your largest customer is going to take three sprints. You have a hunch it shouldn't take that long. Your product intuition says the codebase can handle it in one sprint. But you don't actually know. You can't open the code and read it - that's not your job. So you guess. You negotiate. You push back based on feeling.
Or worse: you accept the estimate and deprioritize the feature, not because it's wrong for customers, but because you don't have proof it's technically feasible in the timeline that matters.
Or: you spend weeks in discovery calls with customers, mapping out what they need, building the business case - only to learn three weeks into development that the architecture makes your solution impossible. Now you're redesigning, re-estimating, and your customer is frustrated.
These are problems that happen because product decisions are made without codebase reality.
Engineers have the codebase reality. But they communicate it through estimates and pushback. Product managers have customer reality. But they communicate it through roadmaps and priorities. The two truths rarely meet.
When they don't meet, you ship the wrong thing. Or you ship the right thing, but it takes three times as long. Or you ship something that technically works but misses what the customer actually needed because the constraints were never articulated.
The bottleneck isn't the decision-making process. It's the information asymmetry. Product managers are making strategic calls about a codebase they can't see.
What "Cursor for PMs" Actually Looks Like
Let me ground this in a concrete workflow.
You're a PM at a Series B SaaS company. Your product is a workflow tool. A large customer comes in with a request: "Can you add the ability to bulk-import data from our legacy system? We have 500,000 records to migrate."
Without codebase intelligence: You ask engineering. They say six weeks, because they'd need to build validation, error handling, rate limiting, data reconciliation. You push back - it's critical for closing the deal. They push harder. You're at an impasse based on gut feel.
With codebase intelligence - the Cursor-equivalent moment: You have a tool that understands your codebase. You ask it: "Can we build bulk import in two weeks?" It reads your import architecture. It sees you already have validation for single-record imports. It understands your rate limiting is configurable. It knows your error handling pattern. It comes back and says: "Two weeks is tight but doable. Here's the blockers: you'll need to refactor the validation layer for batch operations - that's 3 days. Your reconciliation logic doesn't handle partial failures - another 2 days. But your rate limiting is already flexible. If those two things get done first, bulk import is a two-week add-on."
Now you have a real conversation. Not "can we do it" but "what specifically needs to happen first for us to do it in the timeline that matters." You can close the deal because you have a credible plan, grounded in actual code architecture, not estimates.
Better: you can talk to engineering before customer calls. You can qualify opportunities based on what's technically feasible. You can prioritize work that unblocks multiple customer requests because you understand the architecture well enough to see the leverage points.
Even better: you can do discovery differently. When a customer says "we need bulk import," instead of assuming you understand what that means, you can ask: "What are the blockers when you do it now?" You get specific answers. You take those answers to your codebase intelligence tool. You get back: "Ah, they're running into the partial failure issue - that's a real constraint in your system." Now you're not just building what they asked for, you're solving the actual problem grounded in how your system actually works.
Why This Matters Right Now
YC published their Winter 2026 Request for Startups with a category called "Cursor for Product Managers." Andrew Miklas, the partner who published it, framed it like this: "Cursor solved code implementation. Nobody has solved product discovery."
That's the insight. Agentic coding tools - Cursor, Copilot, and the tools coming after them - will make code implementation nearly free from an effort perspective. You'll be able to ship features faster than you ever could before. But the bottleneck will move upstream. The real constraint won't be implementation anymore. It will be the decision about what to implement.
What to build will be decided by whichever team has the clearest view into three things: what customers need (customer signals), what's technically possible (codebase reality), and what moves the business forward (business strategy).
Right now, most product teams lack the visibility into at least one of those. Customer signals are noisy. Business strategy is clearer but often disconnected from reality. And codebase reality? That's a black box. It's where product decisions go to die because they hit constraints that weren't visible during planning.
The teams that close that gap - that ground product decisions in actual codebase reality - will ship better products, faster, with higher confidence. They'll take on customer requests that competitors would reject as infeasible. They'll deprioritize work that looks good until you understand the technical cost.
That's not a nice-to-have. That's competitive advantage.
Where We Are With Glue
When we started building Glue, the insight was simple: product managers should be able to ask their codebase questions. Not "what does the code do" - that's not the right interface. But "can we build X in Y weeks" or "what's the actual constraint when customers ask for Z" or "where in our codebase would a change to our data model ripple through the most systems."
We built it for product managers, product leaders, and engineering leaders who wanted a smarter interface between customer reality and codebase reality.
What we've learned: the best product decisions happen at the intersection of those two truths. Glue doesn't replace judgment. It replaces guessing. You still make the call about whether to build something, but you make it grounded in information, not intuition.
That doesn't sound revolutionary until you realize how many product decisions are made today based on hope that engineering's estimate is conservative.
The Category Is Just Beginning
The "Cursor for Product Managers" category is still emerging. Tools exist - ours is one - but the category itself is just waking up. Most product teams haven't even conceived of the question yet. "Can I ask my codebase whether a feature is feasible?" sounds obvious once you hear it, but it's not obvious until you see it work.
What will become obvious: the gap between product reality and codebase reality is where most product risk lives. Close that gap, and you eliminate an entire class of surprises - scope creep, missed requirements, technical pivots in the middle of development.
Close it well, and you don't just reduce risk. You accelerate. You can take more customer requests seriously because you know which ones are actually feasible. You can scope work correctly because you understand the constraints upfront. You can make roadmap decisions faster because the information is there.
That's Cursor's lesson applied to product leadership. Not magic. Not automation. Just the right information at the moment of decision.
FAQ
Is this just about asking the codebase questions?
Partially. The real leverage is connecting customer signals to codebase reality at the right moment - usually during discovery or roadmap planning, when you're deciding what matters and what's feasible. The tool just makes that connection visible.
Doesn't engineering already know the codebase constraints?
Yes. But engineering communicates constraints through estimates and pushback - very blunt instruments. Product managers communicate needs through roadmaps and priorities - equally blunt. What's missing is a shared language about what's actually possible. Codebase intelligence creates that language.
Won't this slow down product decisions?
The opposite. Right now, decisions are made without full information, so you find out what you missed during development. This surfaces constraints upfront, which actually speeds up decision-making because you're not discovering blockers in the middle of execution.
Does this replace product-engineering collaboration?
No. It enhances it. Instead of engineering having to explain architecture to product, the tool does some of that translation. You end up with better conversations because you're both starting from the same information.
What about legacy codebases that are messy?
They benefit most. Clean codebases are easy to understand anyway - you might ask an engineer and get a fast answer. Messy systems with unclear architecture? That's where codebase intelligence saves you weeks of discovery and prevents decisions made on outdated mental models of how the system actually works.