AI adoption in mechanical engineering has lagged far behind software. Code models now autocomplete entire systems, while mechanical engineers still rely on disconnected tools, manual workflows, and context buried in email threads. The limitation is not simply outdated software. The limitation is that todays AI does not understand the reasoning behind physical design.

Language models predict text. Mechanical engineering demands constraint evaluation, trade off analysis, and explicit reasoning about physical behavior. These are fundamentally different mental models. Until AI systems can reason across physics, requirements, and the decisions embedded in a design, they will not contribute meaningfully to hardware development. This is the gap that an Engineering Reasoning Model aims to close.

Why language models break when they touch mechanical design

A language model answers based on probability, not physics. It has no sense of force paths, tolerance propagation, material limits, or manufacturability. It cannot assess a design the way an engineer does because mechanical design is not a text prediction problem. It is a constraint satisfaction problem.

Every meaningful engineering decision depends on structured relationships. A rib is thickened because of stiffness, not because similar sentences appeared in the training set. A fillet is added because it reduces stress concentration, not because the phrase sounds plausible. A shaft diameter is chosen because it balances shear, manufacturing process limits, and cost curves, not because the number often appears with the word stainless.

Todays AI cannot internalize these reasons. It can describe design choices, but it cannot judge them.

Engineering reasoning is contextual, multi modal, and historically grounded

Mechanical designs are not isolated geometry files. They are the output of long workflows that mix requirements, simulation, tests, manufacturing feedback, supplier constraints, and design reviews. The logic behind any part often lives across CAD histories, PDF documents, email decisions, notebook sketches, and tribal knowledge absorbed over years.

A model that sees only the final geometry cannot explain why a dimension exists or what requirement it satisfies. A model that sees only documents cannot predict the effect of modifying a feature in CAD. A model that sees only simulation output cannot determine whether the simulation even reflects the intended design intent.

The reasoning lives in the connections. Without the ability to link these sources, AI cannot reconstruct or extend the logic that engineers rely on.

What an Engineering Reasoning Model must contain

To design or critique a mechanical system, a model needs an internal representation that is closer to an engineering constraint network than a sequence of tokens.

An Engineering Reasoning Model starts with access to the full set of artifacts engineers use. This includes feature level CAD histories, requirement structures, manufacturing notes, tolerance schemes, test results, and design feedback. These are the sources that encode why decisions were made in the first place.

Next, the model must encode constraints directly. Mechanical systems obey geometric relationships, material behaviors, allowable stress limits, thermal boundaries, and assembly conditions whether the model understands them or not. Without these constraints, any recommendation from an AI is guesswork.

The model also needs a way to evaluate alternatives. Mechanical engineering does not reward single answers. Engineers operate through trade offs. They compare stiffness against weight, complexity against reliability, and cost against performance. A useful model must be able to measure consequences and identify conflicts when a change propagates across the system.

Finally, an Engineering Reasoning Model must understand system hierarchy. Hardware is designed at multiple levels. A minor change in a pinion can affect subsystem clearances and mission level requirements. No language model today can reason across these dependencies.

Why the future of engineering AI depends on reasoning, not chat

Mechanical teams spend huge amounts of time rediscovering design intent. When requirements shift or tests expose flaws, the team often revisits the same decisions because the original rationale is lost. This slows iteration, creates ambiguity in reviews, and makes design knowledge fragile.

A real engineering AI is not a chat interface. It is a system that understands the structure of mechanical decisions. It should be able to detect when a feature breaks a requirement, when a tolerance becomes impossible, or when a design choice conflicts with manufacturing constraints. It should know how a change in one place affects the rest of the system.

This shift would reduce redesign cycles, enable faster reviews, and create more continuity in the engineering process.

How Tandem is building toward this future

Tandem is assembling the data foundation that an Engineering Reasoning Model requires. Today, the reasoning behind mechanical decisions is lost across tools. Tandem captures these relationships directly from the engineering workflow. This includes the evolution of features, the requirements that guide them, the rationale provided during reviews, and the feedback loops from manufacturing and testing.

By turning scattered engineering knowledge into structured context, Tandem creates the environment where true reasoning can happen. This is not a language challenge. It is a systems challenge. With complete design context, a reasoning model can move beyond descriptive answers and begin to operate on the actual logic of physical systems.

The long term path to engineering AI starts with preserving design intent. That is the constraint that has slowed progress across the entire sector. It is also the constraint Tandem is designed to remove.

No fun ending in this one. Sorry
-Arjun