No Front Door
inspired by my previous post “The Scoreboard is Lying”.
i don’t believe theres a single product that abstracts away that much functionality, compute, or product capability into a single usable interface in the construction space. not like GPT, google or claude.
the canonical person to build for in construction is the PM. and as you look at PM’s role, they’re held responsible for deliverables through procore, bluebeam, SAP, email, excel, powerpoint, etc. the PM is the product. they’re the one that serves as the translation and abstraction layer between input and deliverable, agnostic of the software solution.
so to best serve this role, i’ve tried to work through marketmaps, venture portfolios, etc. and have yet to find a solution that has a single entry point, a la GPT or claude code, that can be the front door into the rest of the capability of a full end-to-end workflow in construction. let alone having built the product with potential to deliver and capture the exponential slope of product deliverables.
generally, workflows in construction aren’t linear. they’re concurrent, multi-party, and full of conditional logic that changes across companies, projects, etc. however, the fact that many of these are concurrent actually better allows for multiple agents to work in parallel vs. serial fashion, as most construction tech tools force their users to behave.
i believe the single entry point argument adds greater credence to my secondary argument of the slope of possibility. parallelizing agents to deliver value is the compounding mechanism to capture learnings as those learnings then begin to represent the breadth of capabilities to solve for. in turn, those newly surface capabilities lead to new products to build and that’s how you actually deliver against the exponential slope of product. this is why GPT and claude are so good. each interaction and capability influences the next with emergent understand within a single system, not across a portfolio of them. today, that’s the PM’s responsibility to hold of these in their head. but if their mental load is fragmented across tools, that also means there’s fragmented learning. and it’s the learnings that create the exponential slopes.
the analogy is google. by indexing the web and then enabling search, they learned the relationships between disparate ideas/sites/content + intent around that. then they created "learning harnesses" via products like maps/android/gmail/youtube/chrome/etc. the underlying concept is that you need compounding context to make the next iterative product capability and therefor product experience that much better.
the challenge with this modality is theres a dislocation today between how enterprises interact and buy product vs. the necessary capabilities required to move the industry forward via contextualized and aggregated learnings.
so people build feature factories to try and find the entry point to try and solve for this dynamic. the problem is that those feature factories lead to m.c. escher’s stairway to nowhere or even worse to a rube goldberg experience with contractors, where progress is observed but energy is wasted.
so if you’re going to build a product that consolidates a wide breadth of capability into a single entry point, you must do so in a way that allows for exponential learnings based on behavioral, temporal, and relational inputs.
