The Scoreboard Is Lying

Exponential Patience Problem

Construction trains people to think in sequences. Every person who has spent time on a jobsite or in an OAC meeting has this linear logic drilled into them through thousands of repetitions.

The industry problem is that everyone assumes the path to the endgame for themselves or their organization is linear. And that’s fair, because it always has been. Because that’s how buildings get built. And most importantly, because linear, sequential logic is the mental model construction was built on.

However, the path forward is not linear. The correct path forward is exponential. Through this lens, it becomes more clear how the next few years, the middlegame, has to be played.

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Skipping the middle

Many in this space are assuming the conditions of the endgame already exist. A few examples.

An argument is currently being made that AI will eliminate RFIs. The endgame crowd says AI will eliminate RFIs (wrong, the preconditions don't exist yet). The linear crowd says organize your data first, then deploy AI (wrong, too slow). The correct answer is to deploy AI against the messy data now, surface what matters, and let the structured data layer emerge from usage rather than from a two-year data governance initiative.

Many are also arguing that automation will compress closeout from twelve weeks to two. This assumes someone documented what got installed vs. specified. The reality though is closeout is a bear because as-built drawings are a fiction maintained by mutual consent between contractor and owner. The linear argument is to document everything but your project engineer probably didn’t. The correct answer for closeout is to have agents cross-reference submittals to progress photos, to completed RFIs, to BIM and create a turnover package to the owner with built-in O&M manuals. The documentation emerges from the process, not before it.

Another claim is that machine learning will predict schedule delays. The endgame crowd assumes structured data is flowing from a process that still runs on phone calls. The linear crowd says capture those calls with voice AI. That solves collection. It does not solve interpretation or action. The challenge is to question why a digital representation of reality is even needed within Autodesk Build/Forma, et al. or even the right goal. It feels right to disagree because it’s logical, it’s clean, and it’s correct in the old world. However, the fact that it’s true in the old world is what makes it so dangerous as a mental model in the new world.

These are endgame, future state dynamic environments grafted onto an industry that has barely played the opening sequence.

Turner’s in the middlegame and is playing a different game entirely, which I’ve previously written about. There are only handful of companies that will be able to compete with them within 5-10 years. And therefore the endgame solutions being pitched actually work for that segment of the market because the preconditions exist.

These are outliers and yet the construction technology market is building products as though every GC is Turner. They’re selling endgame software to opening-game companies and wondering why adoption keeps stalling with pilots.

The exponential trap

Here is where the linear mental model does real damage.

A startup building for the linear world optimizes existing workflows. Faster submittals. AI-powered estimating. The pitch is efficiency, the pricing is per-seat, and the customer conversation is about time savings. These companies look successful for 18-48 months. They close deals with VPs of Preconstruction who have an AI budget and need to show their CEO they’re doing something.

And then the floor collapses. Because the workflows they optimized stop existing in their current form. When a project engineer or project manager can do the work that currently requires three people, “faster submittals” is not a product category worth buying, or investing, anymore. In parallel, Todd Saunders and Cory LaChance recently showed how industry expertise is more valuable than technical expertise in the age of the exponential, which is exactly this dynamic playing out in real time. Cory used his 15+ years of experience + Claude Code and created a working application that reads piping isometric drawings and automatically extracts every weld count, every material spec, every commodity code.

But the startups that understand the exponential path have a different problem. They look slow. Their growth curves are flat early. They’re building infrastructure, data layers, structured ontologies, transaction networks. A linear (or momentum) investor sees a startup that can’t show ARR traction in year two. An exponential investor sees a loading spring.

This is where patience becomes the differentiator.

The data still has to get structured. The approval chain still has to get documented. The procurement process still has to exist before the payment network can sit on top of it. But the exponential middlegame builds that infrastructure through usage, not before it. The AI surfaces the data, the system captures the workflow, and the foundation emerges from solving real problems rather than from a two-year data clean up initiative that never gets finished. This work does not demo well. It doesn’t make it easy for a principal to argue for an investment in Monday’s investment committee meeting. But it is the only path to the curve.

The danger is that companies doing this work will compare themselves against linearly progressing peers, see someone else ahead on a metric that doesn’t matter in 36 months, and panic. They’ll abandon the middlegame to chase short-term traction. They’ll ship endgame features before the preconditions exist. And they’ll end up exactly where the skip-the-middle companies end up: building castles on sand.

The filter

There is a sorting mechanism forming in this industry. It works on executives, investors, managers, and founders.

Are you maximally AI-pilled or not? Have you updated your priors about what is possible, and are your decisions reflecting that update?

More importantly, the secondary filter to that question is judging if you believe the world is changing exponentially. If so, you also need the discipline to do the middlegame work that makes the exponential payoff possible.

A GC executive who passes this filter is asking what their org chart looks like when a 200-person company has the cognitive output of a 600-person company.

An investor who passes this filter is underwriting middlegame patience at exponential conviction.

I’m not convinced most have adjusted.

Two paths, same destination, different math

Everyone in construction is headed for the same endgame. The question is the math of how they get there and where they actually end up.

Path one assumes linear progress. You buy AI tools, deploy them, measure the ROI, and iterate. Each year is a little better than the last. This path is comfortable because it fits the mental model. It is measurable, sequential, and familiar. It produces reasonable results at a reasonable pace. And it will leave you standing still while the exponential companies pull away.

Path two assumes exponential progress with middlegame patience. The early years look indistinguishable from path one, maybe worse. The growth is flat. The foundational work is invisible. The scoreboard says you’re behind. But the infrastructure is compounding underneath, and when the curve hits, it hits all at once.

The companies, investors, and executives on path two will outperform in the long run, but they must be willing to be wrong for a while.

The endgame will arrive. It will not arrive because someone simulated it convincingly enough. It will arrive because companies did the patient, foundational, middlegame work of building the infrastructure that makes it possible. And they did it with the conviction that the curve was coming, even when the scoreboard said otherwise.

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You cannot rush the middlegame just because you see the endgame. But you also can’t play the middlegame with linear assumptions.