The Autonomous Hip Installer Needs a Manhattan Project

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THE AUTONOMOUS HIP INSTALLER NEEDS A MANHATTAN PROJECT

The Team That Will Build Autonomous Orthopedics

One mission with two co-leads and five integrated technical workstream


Kambiz Behzadi
October 19, 2026

The autonomous hip installer requires an integrated engineering mission. I propose a staged program, initially scoped at approximately $5 million, led jointly by a clinical-technical inventor and a surgical-robotics systems integrator. Five technical workstreams would build and test one apparatus around one clinical question: what does this patient’s measured mechanical response justify doing next? A university, a medical-device company, a technology company, or a coordinated partnership could host that effort.

Executive Summary

Autonomous Orthopedics proposes a surgeon-supervised autonomous hip installer that measures the patient-specific mechanical response of bone during preparation and implant insertion, interprets whether further action is justified, and governs the decision to continue, modify, or stop. The objective is to achieve reproducible fixation while limiting avoidable cortical injury and reducing the uncertainty surgeons now manage largely through tactile judgment. The system would connect sensing, biomechanical interpretation, prediction, controlled actuation, and verification in one integrated clinical workflow.

The strategic opportunity extends beyond a new instrument. If validated, this capability could change how surgeons and hospitals choose implant suppliers by shifting value from the implant alone to the quality, consistency, and evidence of its installation. A manufacturer that establishes the reference workflow, supporting clinical and economic evidence, and relevant intellectual-property rights could gain a durable competitive advantage; a late entrant could face both an engineering gap and restricted freedom to reproduce commercially important implementations.

The proposed initial program is a staged, approximately $5 million research-and-prototype effort focused first on a defined acetabular workflow. It would be led jointly by the originating clinical-technical inventor and a surgical-robotics systems integrator, with five tightly integrated workstreams: surgical robotics and systems integration; electromechanical instrumentation and embedded electronics; orthopedic biomechanics and independent validation; system identification, estimation, and control; and predictive computational modeling and learning. Quality, regulatory strategy, human factors, manufacturing, and cybersecurity would support the program from the outset.

The first investment is intended to produce reproducible instruments, credible fixation and injury measurements, validated stopping logic, comparative predictive models, and a supervised integrated  demonstrator. The work could be hosted by a university, a medical-device manufacturer, a technology company, or a coordinated hybrid partnership, provided that the participants operate under one mission, one integration plan, and clearly defined intellectual-property, data, publication, and commercialization rights.

The program is explicitly evidence-gated. Key uncertainties include whether measured interaction can predict fixation benefit and cortical-injury risk with sufficient reliability, whether integrated hardware can execute and stop safely in real time, whether the clinical and economic benefit is meaningful, and whether relevant patent claims provide commercially useful protection. The initial stage is designed to bound those risks before larger clinical and commercialization commitments.

The executive decision is to appoint the systems-integration co-lead, assemble the five workstreams, complete a joint technical and intellectual-property review, and fund the first integrated proof. Acting now creates the option to help define a new standard for orthopedic installation; waiting risks allowing a competitor to establish the capability, evidence base, rights position, and surgeon expectation first.

BEYOND FLYING CARS TO THE AUTONOMOUS HIP INSTALLER

A car leaves the ground. A patient hopes to walk again.

Elon Musk’s recent Roadster demonstration brought aerospace and automotive engineering together in an extraordinary display. On October 15, 2026, Tesla demonstrated airborne maneuvers using built-in thrusters developed through its collaboration with SpaceX. This was a demonstration, with production announced for 2028, not an established, widely available flying-car service.⁠[14]

It is the kind of achievement that makes people stop, watch in awe, and reconsider what engineers can build when they share an ambitious mission. I want to see that same ambition directed toward a problem we face on the operating table.

Now bring that ambition into the operating room.

A surgeon is preparing a hip for an implant. Resistance rises. The next size might create a better fit—or remove supporting bone that cannot be replaced. During insertion, another impact might improve fixation—or create a cortical crack. The immediate problem is not a lack of computational power somewhere in the world. It is the absence of a sufficiently informative instrument at the point where the surgeon must act. That is the problem Autonomous Orthopedics sets out to solve.⁠[11]

We should celebrate machines that make the extraordinary visible. We should also build machines that make consequential human decisions less uncertain.

For a person facing hip replacement, the technological frontier is not necessarily a vehicle in the sky. It may be an instrument that helps preserve the bone on which their future mobility depends. The autonomous hip installer is proposed as that kind of frontier: a flagship medical physical-AI program organized around a useful decision, a measurable physical result, and a patient’s life beyond the operating room.

The useful connection is technology crossing disciplinary boundaries. In the Roadster demonstration, automotive and aerospace capabilities met in one vehicle. In Autonomous Orthopedics, surgical knowledge, electromechanical engineering, biomechanics, control theory, and predictive modeling would be brought together around one mission.⁠[14]

We can celebrate a flying car and pursue a better hip replacement. Neither ambition requires a promise of perfection. The question is where else we should apply the determination to solve a difficult physical problem.

If we can assemble the talent to make a car leave the ground, why should we not assemble the talent to help a surgeon decide whether the next impact will secure an implant—or fracture the bone?

The flying car attracts attention and invites us to dream bigger. The surgeon’s dilemma gives that attention somewhere meaningful to go.

This paper connects three things: a technological feat that captures the imagination, a surgical decision that carries real anxiety, and a potentially protected capability that could change why surgeons choose one manufacturer over another.

THE MEETING NO IMPLANT COMPANY WANTS TO HAVE

Imagine a future hospital purchasing meeting.

Two manufacturers are presenting their hip systems. Both offer precisely manufactured implants. Both can discuss positioning, instruments, training, and clinical experience. But one offers an additional capability: a validated system that measures how the implant engages each patient’s bone, governs its installation, and records why the system continued, modified its action, or stopped. Its models improve through successive rounds of validated experience, with the aim of making fixation more reproducible and reducing avoidable fracture and fixation-related failure.

The conversation changes.

The question is no longer only, “Which cup or stem do we prefer?” or “Which implant costs less?” It becomes, “Which company can help our surgeons decide when to ream, when to impact, how much loading to apply, and when to stop?” The purchasing team asks a related question: “Which system can demonstrate fewer avoidable failures, readmissions, and revisions, and a lower total cost of care?”

If one system demonstrates better outcomes and a lower total cost of care after its acquisition, training, and operating costs are included, a lower implant price alone may no longer win the business.

Now imagine the competing medical-device company’s board meeting.

The rival’s autonomous hip installer has demonstrated a meaningful advantage in mechanical execution and is built around commercially important patent claims, an established instrument platform, and models improved through validated experience. The competing manufacturer cannot answer that challenge simply by changing a brochure, recruiting another sales representative, or promoting another positioning-and-alignment feature. It needs an engineering response, a clinical response, and an intellectual-property response. Its implant may remain an excellent product and its positioning robot may perform well, while its competitor has changed the reason surgeons choose an implant and supplier.

The uncomfortable question in its own boardroom is immediate: Why did we wait?

That is a future scenario, not an announcement of an existing AO product. But it is the scenario an orthopedic manufacturer should consider while the cost of participation is still a development commitment rather than the consequences of entering late.

Autonomous Orthopedics is organizing around that possibility. The autonomous hip installer is its first mission. The instrument is the visible product; the deeper strategic asset is the architecture connecting measurement, mechanical interpretation, prediction, controlled action, and verified experience.⁠ [6]

The team required to build it is identifiable. The scientific questions are concrete. There are several credible places to house the work. What remains is the decision to bring the people, capital, and rights together.

The question is not simply whether your company could hire the engineers. It is whether your company wants to help build the system—or defend its implant business against the company that builds the system.

THE MOMENT THE SURGEON IS ALONE

Before that boardroom conversation can change, a much older conversation must change in the operating room.

The image suggests an implant size. The surgeon begins preparing the bone. Resistance rises. The reamer chatters. The broach feels tighter. The next decision is immediate: ream further or stop? Move to the next size or preserve the bone already engaged? The template offers a geometric plan. The hand is reporting a mechanical response. The surgeon must reconcile them before taking an action that cannot simply be undone.

Then comes implantation. The cup advances under successive impacts. A small gap remains. Impact again or stop? Increase the force or accept the present position? Another blow might improve fixation. It might accomplish little. It might crack the cortex. The surgeon feels the instrument, listens, watches, and makes the decision—knowing that both stopping too soon and continuing too far can harm the patient.

This is not a lack of intelligence, experience, or care. It is an information problem at the exact moment when information matters most. The surgeon is interpreting a hidden mechanical interface through its indirect effects on the hand. The anxiety comes from having to choose between two consequential errors without a quantitative account of the boundary between them.⁠[11]

As a surgeon, I know that this uncertainty is not abstract. It is the pause before another pass of the reamer, the hesitation before a harder blow, and the concern that a small gain in seating may carry a much larger mechanical cost. Confidence does not erase that responsibility. Better information and controlled execution could change how it is carried.⁠[11]

The receiving bone can be understood, in a simplified mechanical model, as a complex circular spring: deformation around an oversized implant generates contact forces that contribute to its grip. The useful result is the fixation remaining after the blow. Actual bone is heterogeneous, and the contact and deformation are not uniform. If additional loading increasingly produces plastic deformation or damages the supporting structure, the component can move farther without becoming proportionately better fixed.

Our preliminary study, published in the Journal of Medical Devices, exposed that distinction. Across two tested conditions in polyurethane bone-proxy experiments, nearly doubling the mean measured impact force in the proxy from 3,927 to 7,757 newtons produced another 0.7 millimeter of cup insertion. Axial extraction resistance increased from 765 to 867 newtons, approximately 13 percent.[1]

The experiment did not establish a clinical fracture threshold or prove that the higher-loading condition caused cortical damage. It established the reason to ask a better question: What are we doing to the patient’s bone to obtain the last increment of seating?

A geometric plan cannot answer that question by itself. Neither can a more powerful automatic percussive impactor. The answer requires measurements of the evolving interaction and a decision about what should happen next.

AO’s ambition is to make that mechanical judgment explicit in the system. Today, much of it resides in the surgeon’s tacit sensorimotor skills, which are difficult to explain, measure, and transfer. The intended product is an instrument that can support and, within validated limits, govern the decision to continue, modify the action, or stop. The surgeon should not have to infer every part of that interaction through feel alone while carrying the responsibility for its consequences.

THE BREAKTHROUGH IN THE MECHANICAL DECISION

Among several clinically suitable implants, the distinction that matters most to the surgeon may not be another variation in cup or stem design. It may be a system that can reliably help determine how to prepare the bone, how to install the component, and when further action is no longer justified.

Should I ream further? What do the resistance and chatter mean? Should I impact again? Should I increase the force? Have I achieved adequate fixation, or am I approaching a fracture?

These are not abstract engineering questions. They are the decisions the surgeon must make while holding the instrument, knowing that stopping too soon and continuing too far can both harm the patient. The anxiety comes from being responsible for the outcome without a quantitative account of the mechanical boundary between those errors.⁠[11]

The proposed product is a system that carries more of the decision-making burden. It would interpret the interaction, recommend the appropriate next step, and progressively execute validated actions under surgeon supervision. The surgeon would retain responsibility for the operation’s purpose, context, and exceptions; the instrument would carry more of the immediate work of measurement and mechanical control.[11] The development objective is an autonomous installer that earns increasing authority as its predictions and actions are validated. The strategic question is who will build that capability, establish its evidence, and secure the rights that make it difficult to reproduce.

If one manufacturer can deliver that capability reliably while others offer another choice among familiar implants, the basis of preference could change. The surgeon may choose the installer first—and the implant company with it.

THE COMPANY THAT MAKES EXPERTISE TRANSFERABLE

The goal is to make important elements of mechanical execution less dependent on where the operation occurs and how many comparable cases the individual surgeon has performed. The ambition is for a qualified surgeon in a small regional hospital to have access to the same validated measurement and installation capability as a surgeon at a leading center. That would extend a technical standard across settings; it would not make the patients, facilities, or every aspect of surgical care identical.

Imagine a qualified surgeon in a regional hospital receiving patient-specific measurement and validated installation guidance through AO. Different bones would require different actions, while both regional and specialist centers would work toward the same mechanical standard of preparation, fixation, and verification.

That extends the value of geometric and positioning precision. It offers a route to making mechanical expertise transferable and reducing the isolation of the surgeon at the most consequential moments of preparation and installation.

For surgeons, the attraction would be less uncertainty at the point of preparation and impaction. For hospitals, it would be more reproducible execution and a record of the mechanical process, with the prospect of fewer avoidable failures, readmissions, and revisions if clinical benefit is demonstrated. For payers and insurers, demonstrated improvement at a favorable total cost could support preferential coverage or purchasing requirements. These economic and adoption effects must be established through clinical and cost evidence.

The promise is not immunity from liability. It is a stronger basis for the act of surgery: a trained clinician retains judgment and authority⁠[10], while the instrument carries more of the burden of measurement, interpretation, and controlled execution.

A manufacturer that delivers this capability could change what surgeons ask of an implant platform. Its product would no longer be only the component that enters the bone. It would include the system that helps determine how the component should enter, what its installation achieved, and when further loading should cease. The value would be felt at the moment of bone preparation and installation, not merely described in the implant catalog.

My commercial thesis is direct: where several implants are clinically suitable, a tool that reliably helps make these decisions could matter more to the surgeon than another incremental distinction between cup or stem designs. Implant engineering remains important. But a catalog difference and relief from a consequential intraoperative uncertainty do not compete for attention in the same way. Where implants are increasingly commoditized, the installation process remains the white space.

From the surgeon’s side of the table, competing implants may look increasingly alike while the uncertainty of installation remains intensely personal. If one system can credibly answer “continue, modify, or stop,” the basis of preference can move from the implant alone to the capability surrounding it. A manufacturer could then lose business without its implant having become technically inferior.

The competitive threat is not merely a better robot. It is a new reason to choose an implant company. The installer could become the product that wins the surgeon—and brings the implant business with it.

DePuy Synthes’ KINCISE platform and Zimmer Biomet’s HAMMR signal that manufacturers already recognize the strategic value of the installation instrument.⁠[18,19]

In my view, at the forces used in preparation and implantation, delivering impacts without quantitative feedback from the patient’s bone makes these tools more dangerous than helpful. A powered hammer, whether on a road crew or in an operating room, does not determine when to stop merely by delivering repeatable blows. The surgeon must still judge whether the next blow will improve fixation or injure the bone. AO would add a measured conversation with the patient’s bone: what did the last action achieve, and does the response justify another? Existing powered impactors could be candidates for an AO retrofit that adds sensing, interpretation, and response-based control, subject to engineering feasibility and validation.

WHY THE AUTONOMOUS-VEHICLE ANALOGY MATTERS

Anti-lock braking did not eliminate the brakes or make the driver unnecessary. It placed fast electronic feedback between the driver’s braking command and wheel lock, regulating pressure to help preserve
control. What began as a distinctive capability became standard equipment across vehicle segments. The important change was not simply stronger braking hardware; it was a system governing how that hardware was used.⁠[12]

That is the relevant ambition for AO: not a surgeon with less judgment, but a surgeon whose instruments carry more of the fast mechanical work. A validated system that helps determine whether to continue preparation, deliver another impact, or stop could move from an attractive advantage to an expected part of the operation.

The jet age made the same competitive point in another form. Jet propulsion enabled larger, faster, more productive airliners and changed the economics of air travel.⁠[13] The strategic shift was not merely a refinement of the familiar aircraft. It was a different expectation of what long-distance commercial travel should deliver.

A new technology does not have to eliminate every older alternative to make the absence of its capability commercially painful. The strategic danger begins when customers stop treating an advance as optional and start asking why a supplier does not offer it.

A route map does not drive a car. A planned cup position does not install an implant.

In both cases, the missing capability is the system that interprets the changing physical situation, evaluates possible actions, executes an appropriate one, and checks the result. For AO, the road is the patient-specific bone-implant interaction. The steering and braking equivalents are the direction, energy, timing, preparation increment, and decision to stop.

The deeper parallel is accumulated experience. One installation can contribute evidence to a later, validated model used in another operating room. Expertise would no longer have to accumulate only inside individual surgeons. Measurable elements of that expertise could also be recorded, tested, and transferred through the system, while remaining subject to the limits of the measurements and models.[6]

That makes the competitive question architectural. The manufacturer of an implant and the developer of the intelligence governing its installation need not be the same company. An incumbent can help define that relationship now or discover later that someone else has defined it.

The hip installer is the first vehicle. The potentially enduring asset is the protected and validated control system beneath it.

A DISTINCTIVE CAPABILITY THAT COMPETITORS CANNOT READILY COPY

In Zero to One, Peter Thiel describes the difference between competing over familiar products and creating something sufficiently distinctive to escape that competition.⁠[15]

Autonomous Orthopedics proposes such a change in the basis of orthopedic competition. Its commercial thesis is that, among suitable implants, surgeons could place greater value on a system that helps decide how to prepare the bone, how much loading to apply, and when to stop. The company delivering that capability would no longer be competing only on its implant. It would be competing on the quality and consistency of the installation.

If AO’s clinical value is demonstrated and commercially meaningful patent claims protect important implementations, the installer could become both a powerful reason to choose that company and a difficult capability for competitors to reproduce. That is the potential creative monopoly: not another version of the same implant, but a protected installation capability that changes what surgeons expect an implant company to provide.

Thiel’s distinction also explains why making something useful and retaining a share of its value are separate achievements. An inventor can reveal an opportunity without securing a durable place in the business built around it.⁠[15]

For AO, the clinical capability would create the reason to choose the system. The intellectual-property position could help protect commercially important ways of delivering it. Patents can provide exclusionary rights over the claimed inventions; their effect depends on the actual claim coverage, not a general declaration of ownership over an entire field.⁠[7]

The patent condition matters because it defines the competitive scenario. Once stated, however, its potential consequence should be taken seriously. A company with a working installer and relevant rights would not be offering simply another variation in the implant catalog. It could be offering a capability that surgeons begin to expect and competitors cannot immediately or freely reproduce.

A competitor’s implant would not have to become inferior for its business to become vulnerable. It could remain an excellent implant while surgeons increasingly prefer the company that helps them install it. 

WHAT AO BRINGS TO THE TABLE

A large company can hire excellent people. It cannot acquire another party’s patent rights merely by hiring people capable of understanding the invention.⁠[7]
AO’s offering is the connection between a clinically specific problem, an instrument foundation developed over years, and an architecture-level intellectual-property strategy. The proposed program is not starting with a blank screen and the instruction to “add AI.”

The instrument portfolio includes issued U.S. patents associated with APIM and VIOI, a European VIOI patent, and an issued ESSOB patent. Three subsequent U.S. nonprovisional utility applications seek protection for the AO platform. The references distinguish the core instrument inventory, additional related grants, and the AO applications. Issued patents and applications are different assets; their numbers identify the records for diligence, while their claims determine the relevant scope.[5] The originating instruments are the evolutionary precursors to AO.

APIM — Automatic Prosthesis Installation Machine — introduces controlled mechanical input and force-response feedback. Each impact both acts on the implant and interrogates the interface. The published method examines the response pattern, its plateau at a particular input energy, and the number of impacts required to reach successive plateaus. An energy-specific plateau is not, by itself, a demonstrated clinical stopping endpoint; the stopping rule must be established against fixation and injury measurements.[1,3]

ESSOB — Electronic Signature Sizing of Bone — seeks to quantify the changing resistance encountered during preparation. Motor current, electrical power, and direct mechanical sensing can provide measurements of the interaction that the surgeon otherwise judges largely by feel. Its issued Electronic Signature for Bone Preparation patent is part of that foundation.[2,4]

VIOI — Vibratory Insertion of Orthopedic Implants — adds controlled vibration to the available insertion methods. It expands the possible response to a difficult interface beyond simply delivering a harder blow.⁠[3]

These are mechanics-first, pre-AI concepts. Calling them “analog precursors” describes their place in the program’s evolution; they can include digital electronics, software, and conventional feedback control.

Their strategic value is the continuity they create. The instruments generate the interaction data. The data inform the interpretation. The interpretation supports the next action. The architecture connects those steps into one surgical capability.

WHY THE ARCHITECTURE CLAIMS MATTER

The subsequent AO application work seeks protection beyond an isolated sensor or powered tool. It addresses specific implementations connecting measured interaction, patient-specific mechanical interpretation, prediction of candidate actions, force control, coordinated instruments, and validated improvement across procedures.⁠[5,6]

The most consequential possibility is that commercially meaningful claims protect important ways of joining those functions. In that case, replacing one sensor or redesigning a handle might not remove the relevance of the architecture-level rights. The competitive discussion would extend from the appearance of the instrument to how the system operates.

Some instrument rights have already been granted. Additional architecture-level rights are being sought. The competitive scenario in this paper assumes that relevant claims issue, remain enforceable, and cover a commercially attractive implementation. It is not a claim to own the entire field of orthopedic
autonomy.⁠[3‑7]

Within that scenario, an early partner could do more than add a feature to an existing robot. It could help define and secure rights to the operating architecture of a new implantation capability. That is how a company could leapfrog an incremental product roadmap rather than merely improve its position within it.

For an incumbent, the asymmetric risk deserves attention. An early research program can be limited, costed, and tied to milestones. A successful competitor with relevant rights, a validated workflow, and growing surgeon demand could be much harder to answer. The exposure is not only a missed instrument sale. It is the possibility that demand for the installer begins to redirect demand for the implants installed with it.

Being first to demonstrate a credible clinical benefit is not the same as being first to announce an idea. The advantage would come from establishing the reference workflow, the supporting evidence, the relevant rights, and the surgeon’s expectation of what an installation system should do. A later entrant could find itself trying to reproduce a capability while the first successful provider is already refining and deploying it.

“We can build something too” is not the complete response. The harder question is what can be built, with which rights, on what timeline—and how much business might shift while the answer is being developed.

A FLAGSHIP PHYSICAL AI PROGRAM WITH A PATIENT AT ITS CENTER

AO addresses physical AI in orthopedic surgery, where preparation and implantation can involve substantial mechanical loading at a vulnerable biological interface. Its significance lies in making a consequential clinical decision more reliable through a system that senses, interprets, acts, and checks what its action achieved. High force creates demanding engineering requirements; the clinical value comes from controlling its effect on the patient.

The proposed system must account for the fact that each action changes the material being measured. The implant advances, contact changes, the bone deforms, and the next decision must account for what preceding actions have done. The information is incomplete, the interaction is patient-specific, and some errors cannot be reversed.⁠[11]

AO is a candidate flagship for medical physical AI because its intelligence would be judged at the point of contact with the patient. The task is not simply to recognize an image, reproduce a trajectory, or generate a recommendation detached from execution. It is to interpret a changing bone-implant interface, evaluate the next preparation or insertion action, govern high-force interaction, and verify its consequences.

Behzadi Postulate 1, a design principle for AO, is straightforward: intraoperative information is useful only if it can support better, faster decisions and improved results without overwhelming the surgeon. When an instrument can perform a defined computation faster than the surgeon, with the reliability required for the task, that computation should occur in the instrument.

The spider and its web provide an analogy for extended cognition: the working system extends beyond the animal itself. For AO, measurement, interpretation, and appropriate control would be distributed across instruments and computation, while the surgeon retains authority over clinical purpose and exceptions. The instrument should make the surgeon more capable without asking the surgeon to interpret ever more signals. The intended demonstration is a clinically meaningful improvement in fixation, injury avoidance, and consistency of mechanical execution.[6,11]

A common, successful operation can still contain a consequential decision that deserves better tools. The unresolved decision during preparation or impaction is the unmet need. It justifies investigating a better instrument.[1,11]

Validated learning across procedures is central to that ambition. Each recorded case can contribute an action history, measured responses, and linked outcomes. During an operation, those measurements update the estimate for the current patient. Across cases, the accumulated evidence can be used to develop and test improved models. Each new case adds experience that can improve future prediction and control.

A program operating across many institutions could eventually draw on hundreds of thousands or more procedures, exposing its models to a breadth of bone conditions and installation histories no individual surgeon could encounter alone. The ambition is a collective mechanical knowledge base that improves next-action prediction. Its value would depend on measurement quality, representative cases, reliable outcome labels, and demonstrated performance on new patients and settings, rather than the case count alone.

Federated learning is one possible way to organize that development: participating institutions can train locally and combine model updates while retaining their source data. A centralized dataset is another. Either arrangement needs compatible measurements, outcome definitions, permissions, and comparative validation. Federated learning is a method for coordinating training; validated learning is the requirement that an update earn deployment through evidence.[17]

My invitation to technology leaders is to bring that same ambition into a setting where the value of success is immediately understandable.

Bring the same ambition here. Help build a physical-AI capability whose value a patient can understand without knowing what a neural network is.

ONE MISSION AND FIVE INTEGRATED TECHNICAL WORKSTREAMS

A project of this kind needs the concentration of a mission-led program: one objective, accountable leadership, protected development time, and specialists working on the same apparatus. That is the organizational meaning of a Manhattan Project analogy here. The proposed effort has its own medical purpose and a bounded initial budget. What it seeks from an institution or technology leader is the ability to bring the right people together, commit resources, and keep them responsible for a common result.

The mission is straightforward:

Develop a surgeon-supervised autonomous hip installer that prepares and installs press-fit components according to the patient’s measured mechanical response, seeking adequate fixation while limiting avoidable cortical injury.

The first demonstrator should isolate one acetabular workflow. The full mission connects preparation and installation, then extends to the femoral side through its own development and validation. The initial instrument may be handheld, fixture-mounted, or robot-mounted. Autonomy concerns who determines and governs the physical action, not whether the housing resembles a humanoid robot.

I propose to serve as clinical-technical program lead and originating inventor. My responsibility would be to keep the program anchored to the actual surgical problem, its instrument mechanisms, its clinical decisions, and its intellectual-property continuity.

The co-lead should be a surgical-robotics and systems-integration scientist who takes responsibility for turning the architecture into a working engineering system.

This person would also lead the robotics and integration workstream. The two roles should be complementary, with explicit decision responsibilities rather than competing chains of command.

Beneath them belong five integrated technical workstreams. Each may require several subspecialists. None should operate as a separate project that happens to share the AO name.

Autonomous Orthopedics follows the innovation sequence Peter Mulford presented in Innovation in the Age of Hybrid Intelligence: define the intended outcome, determine what must be predicted, identify the necessary data, and then select the system and algorithms.[16]

First, our intended outcome is reproducible, patient-specific fixation with less avoidable cortical injury and less uncertainty for the surgeon deciding whether to ream further, impact again, increase force, or stop. Second, we define the prediction: what will the next action do to the bone–implant interface, and will its expected fixation benefit justify its mechanical risk? Third, we identify the data needed to answer that question: synchronized measurements from the instruments and their interaction with bone, the history of preceding actions, and independently measured fixation and injury outcomes. Fourth, we select and integrate the appropriate methods — electromechanical sensing, biomechanical models, estimation and control, and machine learning where it improves prediction — and connect them to instruments that can execute and verify the resulting action under surgeon supervision. [16]

This is why the five technical workstreams must operate as one team: each supplies a necessary part of the same clinical answer. The starting point is the decision that makes the surgeon hesitate. The science, engineering, and people are organized to make that decision measurable, predictable, and actionable.

Peter Mulford explains how to organize the innovation. Peter Thiel explains why the resulting company could become difficult to compete with. The surgeon’s hesitation explains why anyone should care. The five workstreams below put those three ideas into a single development program.⁠[15,16]

1. SURGICAL ROBOTICS AND SYSTEMS INTEGRATION

This is the group responsible for the whole machine.

Its lead needs expertise in surgical robotics, physical interaction, real-time system integration, and experimental hardware. Supporting specialists may include robotics-software engineers, actuator and motion-control engineers, and engineers responsible for the surgeon interface and hardware-software integration.

The immediate task is not to design the most impressive robotic arm. It is to connect instruments, sensors, models, decision logic, actuation, and verification so that they function as one system.

When software requests an impact, what physical event is actually delivered? When a stop is requested, is another pulse already mechanically committed? When a sensor becomes unreliable, how does the instrument behave? When the surgeon changes the plan, do the model, controller, and actuator share the same interpretation?

Those questions belong to an integration owner, not to whichever laboratory happens to notice the problem first.

This workstream succeeds when the complete apparatus works — not when each component works separately.

2. ELECTROMECHANICAL INSTRUMENTATION AND EMBEDDED ELECTRONICS

This group builds the instruments that make the rest possible.

The electrical and instrumentation engineer develops sensor interfaces, signal conditioning, amplifiers, acquisition electronics, synchronization, motor measurements, and electrical diagnostics.

The mechanical engineer develops instrument geometry, impact mechanisms, shafts, couplings, sensing structures, load paths, fixtures, housings, and the mechanical integrity of the assembly.

The embedded-software engineer makes the instrument record, communicate, identify its operating state, perform its commanded action, and report faults reliably.

Mechatronics is the overlapping integration discipline that joins mechanics, electronics, sensing, actuation, and embedded control. The team needs that capability; it does not necessarily need a separate specialist with that exact title. In a small team, a strong mechanical or electrical engineer may supply it. As several instruments are integrated, a dedicated mechatronics engineer may become valuable.

For AO, their deliverables include APIM, ESSOB, the relevant sensorized instruments, accessible patient-side sensing, synchronized recordings, and a reproducible experimental setup.

The first version can be built without machine learning. That does not make it a minor engineering task. Software can use measured forces, motion, and electrical signals—including voltage and motor current—to estimate what is happening at the hidden bone–implant interface. The instruments must provide enough reliable information to guide the next action, and the hardware must deliver that action as intended.

This team turns the idea into hardware that can ask the bone a repeatable question.

3. ORTHOPEDIC BIOMECHANICS AND INDEPENDENT VALIDATION

This group determines what the answer means.

It requires an experimental orthopedic or musculoskeletal biomechanist, with expertise in bone mechanics, press-fit contact, implant fixation, and mechanical testing. Supporting capabilities include strain measurement, imaging, specimen preparation, and statistical experimental design.

The group measures preparation geometry, insertion, retained grip, extraction or pushout resistance, micromotion, cortical deformation, and cortical cracking. It distinguishes useful fixation from resistance caused by an unfavorable condition. It determines whether a warning preceded an injury or merely registered one that had already occurred.

The injury target is not the elimination of every microscopic trabecular alteration. It includes small cortical cracks, occult fractures, and overt structural fracture — injuries that matter to the supporting construct.

This group must also prevent a misleading success: an instrument that stops so early that it avoids a crack but leaves inadequate fixation. The endpoint has to respect both sides of the problem.

Its independence is methodological. The reference measurements cannot be defined simply by whether they agree with the software under development.

The biomechanist is not the person who tests the finished robot at the end. This is the person who makes it possible to know what the robot should be trying to achieve.

4. SYSTEM IDENTIFICATION, OBSERVABILITY, ESTIMATION, AND CONTROL

This is the mathematical and real-time interpretation group.

It needs a control-systems scientist or engineer with dynamical-systems, estimation, and implementation expertise, supported by real-time software and signal-processing engineering. A general software developer is not a substitute for that specialization.

System identification asks how the instrument and interface respond to a defined input. Observability asks which relevant conditions the measurements can distinguish. Estimation asks what condition is most consistent with the measurements now. Control determines the permitted response and governs its execution.

A practical early task is already available: analyze successive impact responses, detect their energy-specific plateaus, count impacts to plateau, and determine when a task-specific stopping rule, once validated against fixation and injury measurements, calls for reassessment or stopping.⁠[1]

For ESSOB, the same group connects motor and mechanical measurements to changing preparation resistance. For multiple sensing locations, it preserves meaningful differences rather than allowing a simple average to hide them.

These functions can begin with conventional equations, estimators, signal processing, and explicit decision rules. They are not automatically AI because they respond to feedback, and they are not restricted to elementary if-then logic.

This group makes the instrument’s measurements usable for the decision that must be made now.

5. PREDICTIVE COMPUTATIONAL MODELING AND LEARNING

This is the prediction group and asks the next question: what will happen if we take a particular action?

Its expertise should combine computational mechanics, scientific machine learning, probabilistic modeling, and data engineering. Deep-learning specialists may contribute where their methods add value. The requirement is not simply someone who can attach a general language model to the data stream.

The model receives the current interaction history, relevant patient and implant information, prior experiments, and the physical relationships already established. It evaluates candidate actions: another impact, a different energy, a pause, a change in direction, a vibration-assisted mode, or further preparation.

The outputs must concern the operation: expected advancement, fixation benefit, cortical-injury risk, and uncertainty. The group compares those predictions with independent results and tests whether the model improves the decision beyond the original response-pattern rules. Tests must separate training data from new specimens and relevant operating conditions; many impacts in one specimen do not constitute many independent validation cases.

Model improvement spans cases. Measurements during a procedure update the patient-specific estimate. Evidence gathered across experiments and, eventually, clinical procedures supports successive model versions that are tested, versioned, and validated before release. Evaluation must include failures and cases outside the intended operating range, alongside successful insertions. Centralized and federated development are possible arrangements; neither removes the need to establish that a new model improves the relevant decision.[6,17]

The proposed BONES — Biomechanical Optimized Neural Engineering System — draws on this work, biomechanics, and estimation. It is not an isolated neural network beyond which classical engineering has no role. Estimation and control can predict; learned models can operate in real time. The division is one of responsibility: workstream four owns the live interpretation-and-control loop; workstream five develops and validates better predictive models.

This group turns accumulated mechanical experience into a tested advantage for the next case.

PUT THEM AROUND THE SAME EXPERIMENT

Imagine the first laboratory insertion.

A controlled impact is delivered. Several signals change. One response develops differently from the others. The cup advances slightly. A model recommends stopping.
The electrical engineer asks whether the signal is genuine. The mechanical engineer asks what the load path transmitted. The biomechanist asks what happened to fixation and cortex. The estimation specialist asks which interpretation the measurements support. The prediction specialist asks whether the next-action forecast was correct. The robotics co-lead asks whether the command, delivered action, timing, and recorded event actually agree. The surgeon asks whether the resulting decision would have helped in the operating room.

These are not seven meetings. They are seven views of the same experiment.

The program needs a common clock, common definitions, a common dataset, and a common account of what constitutes success. It also needs dedicated engineers and researchers whose daily work is building and testing, not merely attending advisory calls.

Quality, regulatory strategy, human factors, manufacturing, and cybersecurity must support the five groups from the outset at a level appropriate to development. Their function is to make translation possible, not to substitute documentation for a working system.

Five excellent laboratories do not automatically make one excellent instrument. The mission is what joins them. 

WHERE TO BUILD THE PROGRAM

THE UNIVERSITY ROUTE

A university could host the program under a surgical-robotics co-lead who assembles the five capabilities across existing laboratories. AO would bring the clinical-technical leadership, instrument foundation, and intellectual-property position. Dedicated researchers and engineers would build the integrated apparatus.

The invitation is concrete: we are assembling funding for an approximately $5 million staged program; can your institution help define and execute it? The first response should be a named co-lead, participating specialists, facilities, and a costed initial study — not a letter of general support.

Funding could combine industry sponsorship, family-office capital, venture investment in the development company, and suitable research grants. The university would gain the opportunity to establish experimental methods, produce results, train researchers, and help define a clinically consequential field.

The condition is integration. The program must not dissolve into disconnected grants. Intellectual property, software, data access, publication, and commercialization rights should be organized around a shared development plan.

The university’s opportunity is to become a place where the autonomous hip installer is built, not merely discussed.

THE MEDTECH ROUTE

Zimmer Biomet, Stryker, Johnson & Johnson, Smith+Nephew, or another manufacturer could host a dedicated internal program, using external specialists where useful. The five capabilities could come from existing teams, targeted recruitment, and sponsored academic work.

For a manufacturer, the first embodiment can be deliberately close to the business: one implant family, one instrument workflow, one measurable decision that improves installation. The larger ambition would connect the company’s portfolio to a patient-specific preparation and control capability.

The strategic proposition is stronger than an instrument upgrade. A manufacturer with relevant rights and a validated installer could compete on what happens between opening the implant package and obtaining fixation. Its competitors would have to respond to the performance of the combined system, not only the  specifications of the implant. A sales team cannot answer a demonstrated decision-making advantage with another comparison of cup coatings or stem profiles alone.

That possibility can justify action before the final market is visible. The first commitment need not be an acquisition or an unlimited platform build. It can be a funded team, a joint technical and claims review, and milestone-linked development rights.

The boardroom question is: Would we rather invest now to help define the capability surgeons may come to expect, or defend our hip business after a competitor has made it the reason to switch? 

THE BIG TECH ROUTE

NVIDIA, Google, Microsoft, Meta, Tesla, or another technology company could choose to host the integrated program, supplying the robotics and computational core while recruiting orthopedic, instrumentation, and biomechanical expertise. A device-development partner would provide the path toward a clinical product.

The attraction is a physical-AI problem with a consequential endpoint. The machine must act on patient-variable material, interpret a partly hidden interface, and decide whether another action is justified. The published cup-insertion experiments illustrate kilonewton-scale responses.[1] AO development planning considers transient instrumentation demands on the order of 10 to 15 kilonewtons, with task-specific ranges and operating limits. That is an engineering design envelope, not a force prescription or a claim that every procedure reaches those loads. It creates demanding requirements for sensor range and bandwidth, load-path integrity, actuator dynamics, and control of a short-duration event.

The demonstration worth pursuing is not a robot performing a theatrical movement. It is a machine making a useful mechanical decision that a surgeon previously had to make without equivalent measurements.

An internally sponsored team could make this a distinctive research mission. But the contribution must include people, instruments, experiments, and clinical translation — not only computing credits. The organization must decide who owns the complete outcome.

A successful program would say something substantial about physical intelligence: it can connect an action to its effect on a human structure, recognize when further action is unjustified, and make the resulting capability useful beyond the laboratory.

The broader research opportunity is response-based control of substantial force applied to deformable, partly observed structures, including human tissue. Selected soft-tissue robotic tasks, including those studied with Intuitive’s da Vinci system, involve forces of a few newtons, with reported mean peak forces around 4–5 N in selected bench tasks and force-feedback settings.⁠[20] Robots that lift or support people and perform physically demanding work will need to manage loads well beyond this range, potentially reaching kilonewton-scale forces in particular applications. Orthopedic impaction already presents kilonewton-scale peak loads⁠[1], making the autonomous hip installer a potential initial research testbed for methods relevant to future consumer, military, and commercial robotics. The opportunity is to use measured load transmission and structural response to estimate the evolving mechanical state, predict the consequences of another action, and determine when to continue, adjust, or stop. Each application would require its own mechanical models, operating limits, and validation. Peak force alone cannot define safe interaction: duration, contact area, loading rate, direction, mechanical energy, and the condition of the contacted structure all matter.⁠[21]

THE HYBRID ROUTE

The strongest host may combine those arrangements. A manufacturer supplies product engineering and translation; a university supplies experimental and scientific depth; a technology partner supplies selected computational capability. AO supplies the originating clinical-technical and intellectual-property foundation.

The institutional model is not imaginary. Imperial College London lists Zimmer Biomet among its musculoskeletal industry collaborators. In August 2026, Imperial and Smith+Nephew also announced a five-year surgical-robotics partnership designed to place company engineers alongside academic researchers.⁠[8,9]

A future AO collaboration linking a manufacturer such as Zimmer Biomet with an institution such as Imperial is therefore an intelligible organizational proposal, not an announced agreement. The essential requirement is that the institutions share one mission, one integration plan, and defined rights and responsibilities.
The program should not become less coherent because several logos appear on the cover.

WHAT THE FIRST FIVE MILLION DOLLARS IS MEANT TO CHANGE

Approximately $5 million is the proposed scale of the initial staged research-and-prototype program, not secured funding or the complete cost of clinical commercialization.

It should purchase a team capable of answering the central question with a working apparatus: can measured interaction support better preparation and installation decisions while preserving fixation and reducing avoidable cortical injury?

The first spending should create reproducible instruments and credible reference measurements. Subsequent commitments should establish useful stopping logic, compare predictive models, and demonstrate supervised integrated execution. Synthetic-bone and cadaveric work should support the transition toward a separately planned clinical program.

The objective is not simply a larger collection of waveforms or a more elaborate presentation. It is a result that changes the next decision: continue, simplify, redirect, partner, or invest further.

For an incumbent, that is both an offensive and a defensive proposition. The company is investigating a possible new source of demand while reducing the chance that it first encounters the technology as someone else’s differentiated product. A competitor’s success would not pause its commercial effect while a late entrant assembled a team, built evidence, negotiated rights, and sought the necessary approvals.

The initial development risk can be bounded. The commercial cost of letting a competitor define the next requirement for implant selection may be much harder to contain.

THE DECISION BEFORE THE DEMONSTRATION

An inventor cannot build this system alone. A university cannot turn every promising result into a product alone. A manufacturer does not originate every important invention internally. A technology company cannot replace surgical knowledge with computing power.

Their capabilities are complementary. The opportunity is to put them under one mission before that mission becomes an obvious procurement category.

Spock would analyze the uncertainty; Kirk would decide which risk was worth taking and commit the crew to the mission. AO needs both: disciplined analysis and the courage to act before the outcome is certain. The executive decision is to give the engineering a mission and the resources to pursue it before the market demands an answer. Caution that funds a decisive experiment is responsible. Caution that waits for a competitor to prove the opportunity can become expensive.

AO brings an invention-led position around a consequential clinical problem and a concrete structure for building the next capability. The intellectual-property opportunity and the execution plan belong in the same conversation, before either has become someone else’s advantage.

If that capability succeeds, the surgeon’s questions—”Should I ream more? Should I impact again? Should I increase the force?”—would no longer have to be answered through tactile judgment alone. The manufacturer’s question could change from “Which implant will they buy?” to “Whose installation system will they refuse to operate without?”

The second question should concern every company that expects its present position to carry unchanged into the next generation of orthopedics. The surgeon would not be choosing between two similar-looking implants alone. The surgeon would be choosing whether to return to an uncertainty that another system had already helped resolve.

The choice is not between imagination and rigor. The human story explains why the problem matters, the engineering mission explains what must be built, and the competitive risk explains why an institution may choose to act before the outcome is certain. None of those arguments requires a promise of perfection.

For the technology leader: If our engineers can build extraordinary machines, why not direct that capability toward this surgical decision?

For the implant-company executive: What happens to our business if another company answers that question first—and secures the rights to the capability our surgeons begin asking for?

For the university professor: Will you help assemble the people who can answer that question, define the experiments that make the answer credible, and build the first integrated proof?

The university, MedTech, Big Tech, and hybrid routes remain different ways of executing the same mission, not separate stories competing for the program’s attention.

The autonomous hip installer needs a team. The company that helps assemble that team could help determine not only how the implant is installed, but which implant system the surgeon chooses.

The invitation is to appoint the co-lead, assemble the five workstreams, and fund the first integrated proof.

Do not wait until your surgeons ask why only your competitor can help them make the decision.

Sources and portfolio notes

[1] Behzadi K, Rusk J. Characterization of Acetabular Cup Insertion Forces in Cancellous Bone Proxy for Validation of an Invasive Sensing Model and Development of Automatic Prosthesis Installation Device: A Preliminary Study. Journal of Medical Devices. 2021;15:021001. Table 2 supplies the numerical comparison; Figures 7-8 present the proposed response-profile and feedback approach. DOI: 10.1115/1.4049085. Source

[2] Behzadi K. Electronic Signature Sizing of Bone. Author technical paper dated September 5, 2023. Describes electrical-signature and direct mechanical-sensing approaches to quantitative bone preparation. Source

[3] Representative issued instrument patents: U.S. 11,331,069, Invasive Sense Measurement in Prosthesis Installation; and U.S. 11,576,790, Prosthesis Installation Systems and Methods. Patent disclosures describe inventions and are not clinical performance evidence. Source | Second grant

[4] ESSOB. U.S. Patent 12,599,455, Electronic Signature for Bone Preparation. Issued April 14, 2026; U.S. application 18/405,881. Public grant text. Public grant

[5] Instrument and AO platform patent references. The core groupings follow the author’s published portfolio index; additional directly related grants are identified separately. Patent and application numbers below are emphasized for reference and do not establish clinical performance or the breadth of enforceable claims.

[6] Behzadi K. The Autonomous Hip Installer Is Coming: Autonomous Orthopedics Is Building the Force-Control and Learning Layer Beneath It. Non-confidential MedTech leadership paper, September 2026. Architecture, progressive development, model governance, and strategic participation framework.

[7] United States Patent and Trademark Office. Managing a Patent. General explanation of patent rights. Individual portfolios require separate analysis of claim scope, ownership, maintenance, validity, and territory. USPTO

[8] Imperial College London. New Centre from Imperial and Smith+Nephew Will Accelerate Innovation in Surgical Robotics. August 18, 2026. Describes a five-year university-industry collaboration; it does not describe an AO agreement. Source

[9] Imperial College London. MSk Lab: Industry Collaborators. Lists Zimmer Biomet among industry collaborators. It does not establish an AO partnership or a specific Zimmer Biomet-Imperial robotics program. Source

[10] U.S. Food and Drug Administration. Computer-Assisted Surgical Systems. Guidance on device-user training, credentials, and institutional responsibilities. The discussion of possible liability or payer implications in this paper is a strategic proposition, not a legal opinion or coverage determination. Source

[11] Behzadi K. From Tacit Skill to Controlled Systems (Precision Without Control): Physical AI and the Standardization of Orthopedic Force. September 13, 2026. Clinical-mechanical framing, distributed interpretation, and the proposed force-control architecture.

[12] Bosch. Safe Braking: Start of Developing Bosch’s Anti-Lock Braking System ABS. Historical account of electronic pressure regulation and the progression to standard equipment across vehicle segments. Source

[13] Smithsonian National Air and Space Museum. The Jet Age. Historical account of jet propulsion and its effects on airliner capability and air-travel economics. Source

[14] Reuters. Tesla’s New Roadster Turns in Air with SpaceX Thrusters, Won’t Land until 2028. October 16, 2026; republished by Mint. Reports the October 15 demonstration and announced production timing. The comparison here concerns an engineering demonstration, not a widely available flying-car service. Source

[15] Thiel P, with Masters B. Zero to One: Notes on Startups, or How to Build the Future. Crown Business, 2014. See also Thiel P, Competition Is for Losers, The Wall Street Journal, September 12, 2014. These sources support the distinction between competing over familiar products and creating and capturing the value of a distinctive capability. The application to AO is the author’s strategic thesis. Book | Author’s essay

[16] Mulford P. Innovation in the Age of Hybrid Intelligence. Presentation slides, DOC SF 2026, BTS. The photographed sequence identifies the intended outcome first, the required prediction second, the necessary data third, and the system or algorithm fourth. The AO application and team organization are developed here by Behzadi. Official event agenda: DOCSF 2026

[17] Kairouz P, McMahan HB, Avent B, et al. Advances and Open Problems in Federated Learning.
arXiv:1912.04977, version 3, March 9, 2021. Background on decentralized model training and its technical challenges; the proposed AO learning program is the author’s application of these concepts. Research paper

[18] Johnson & Johnson. Launch announcement for the KINCISE 2 Surgical Automated System. June 3, 2025. Describes powered impaction, procedural control, and the intended reduction of physical burden on surgeons. Manufacturer source

[19]HAMMR Operation and Maintenance Guide. DOC0179252, IFU-HAMMR-EN-US, Revision 8, December 2024. See pages 16 and 19 on surgeon decisions and impact-energy selection. Manufacturer instructions hosted by Celestica. Operating guide

[20] Awad MM, Raynor MC, Padmanabhan-Kabana M, Schumacher LY, Blatnik JA. Evaluation of forces applied to tissues during robotic-assisted surgical tasks using a novel force feedback technology. Surgical Endoscopy. 2024;38:6193–6202. doi:10.1007/s00464-024-11131-z

[21] Falco JA, Marvel JA, Norcross RJ. Collaborative Robotics: Measuring Blunt Force Impacts on Humans. Proceedings of the 7th International Conference on the Safety of Industrial Automated Systems. Montreal; 2012. NIST publication