The Social Intelligence Thesis

Moxi doesn’t try to be autonomous. It’s designed around a fundamental insight from Thomaz’s two decades of research: robots working alongside people in unstructured environments need social intelligence more than full autonomy.

The robot navigates hospital hallways, opens elevators and doors independently, avoids colliding with people or equipment, and “happily poses for selfies” when staff want photos. It uses facial and voice recognition to verify deliveries to the right person. When it encounters ambiguous situations, it asks humans for help rather than making incorrect assumptions.

This isn’t the humanoid robotics playbook. Moxi has arms and grippers for mobile manipulation, but its core technical advantage is human-guided learning. “The more your staff uses Moxi, the more Moxi learns and adapts to your environment and way of doing things,” Diligent’s technical documentation states.

Every interaction generates training data. More deployments improve the visual language models that enable “semantic scene understanding in a busy human, indoor environment”—different from autonomous cars or drones operating in outdoor spaces with clearer sensor data.

By March 2025, one million deliveries meant the robots were “continually learning about being mobile and manipulating” in exactly the environments where they needed to operate. That compounding data advantage doesn’t require betting on autonomous breakthrough—it builds competitive moat through operational deployment.

The Pandemic Acceleration

Between 2018 and early 2020, Diligent was methodically scaling. “A couple of years ago, if we were working with a hospital it was because they had some special funds set aside for innovation or they had a CTO or a CIO that had a background in robotics,” Thomaz noted in 2022. “But it certainly wasn’t the first thing that every hospital CIO was thinking about.”

Then came COVID-19.

“Now that has completely changed,” Thomaz continued. “We’re getting cold outreach on our website from CIOs of hospitals saying ‘I need to develop a robotic strategy for our hospital and I want to learn about your solution.’ Through the pandemic, I think everyone has seen that the workforce shortage in hospitals is only getting worse.”

Mary Washington Healthcare’s experience exemplified the shift: “We started implementing Moxi into our clinical workflows in December and planned on going through a trial phase to determine if the technology would be helpful to our staff. Right away, we could see the impact Moxi made on the efficiency of our staff and how morale immediately increased. We were so impressed with what Moxi took off our team’s hands, we chose to add even more robots to our fleet.”

In April 2022, Diligent raised $30 million in Series B funding led by Tiger Global, bringing total capital raised to ~$50 million. By September 2023, they added $25 million more.

That’s ~$75 million total raised over seven years to achieve 1 million deliveries across dozens of hospitals. By comparison, Jibo raised $73 million, shipped thousands of robots to consumers, then shut down when the novelty wore off.

The Deployment Metrics

Moxi’s core workflows span the mundane tasks that consume healthcare professionals’ time without requiring their expertise:

Laboratory deliveries: Transporting lab samples between floors and departments, eliminating nurse trips to central labs.

Medication transport: Moving pharmacy deliveries to patient floors, enabling “Meds to Beds” programs where patients receive prescriptions before discharge.

Supply replenishment: Delivering wound dressings, IV supplies, and equipment between storage and clinical areas.

Personal item transfers: Moving patient belongings or lightweight equipment between units.

When Moxi completed its millionth delivery in March 2025, it was delivering medication to an outpatient infusion center in Chicago—a workflow where speed directly impacts patient experience. Cancer patients arriving for chemotherapy treatments need their medications immediately to minimize time in the clinic.

“The pharmacy is far from the infusion center,” Thomaz explained. “We hear a lot from the infusion clinic teams that use Moxi that they just love seeing the efficiency. They love seeing that people can get in the chair and get their meds started right away.”

Each deployment follows a 12-week implementation timeline: Diligent’s team customizes operational workflows for the specific hospital environment, trains staff on interacting with Moxi, and integrates the robot into existing clinical systems.

The business model is Robotics-as-a-Service (RaaS). “Cost is based on the customized utilization of each Moxi robot for every hospital’s particular needs,” Thomaz noted. This recurring revenue model aligns incentives—Diligent succeeds when hospitals see sustained value, not one-time hardware sales.

Strategic Positioning: Healthcare First, Then Adjacent Markets

Diligent isn’t trying to build “the robot for everything.” They’re building trust and technical capability in one demanding vertical, then expanding.

“If we go from hospitals to assisted living, for example, then you start to see a very interesting trajectory where a company could go from hospitals to structured nursing homes to eventually the home,” Thomaz outlined in 2025. “I personally think there are a lot of companies going out and going straight to humanoids for the home. But we’ve kind of taken this very concerted path to become a trusted partner in healthcare.”

The technical progression makes strategic sense. Hospitals are semi-structured environments with clear workflows, sophisticated users comfortable with technology, and acute pain points that justify automation costs. Success there builds credibility for less-structured environments.

Current expansion focuses on patient-facing tasks: delivering pillows, water, or comfort items directly to patient rooms. These workflows would translate directly to assisted living facilities, where similar delivery tasks exist but with residents rather than patients.

The market opportunity is substantial. The global smart hospital market was estimated at $27.6 billion in 2021, projected to reach $82.89 billion by 2026, compounding at 24.6% annually. Robotic automation addresses the core constraint: workforce shortages that limit hospital operational capacity regardless of available beds or equipment.

The Academic-to-Commercial Bridge

Thomaz’s path illustrates a capital-efficient model for translating frontier research into commercial deployment:

Phase 1 (2002-2015): Academic Foundation Build deep technical expertise in a focused problem domain. Thomaz’s MIT dissertation and Georgia Tech lab established her as a leading authority on socially-guided machine learning and human-robot interaction. This expertise became competitive moat when deploying in complex environments.

Phase 2 (2015-2017): Government-Funded Market Discovery NSF grants funded rigorous customer discovery (150 hours shadowing nurses) and technical proof-of-concept in real environments. This de-risked both market and technical assumptions before raising venture capital.

Phase 3 (2017-2020): Controlled Commercial Deployment Seed funding enabled partnerships with four Texas health systems for research trials. Learning from these deployments shaped product design, operational workflows, and business model before scaling.

Phase 4 (2020-present): Scale Against Proven Demand Pandemic-driven urgency validated the thesis. Series B and subsequent funding accelerated deployment to dozens of hospitals, with each installation improving the underlying machine learning models.

This contrasts sharply with venture-first approaches where companies raise large rounds, build impressive demos, then search for customers willing to pay. Thomaz spent years validating demand and refining technology in real environments before seeking institutional capital.