Drug discovery is one of the most expensive failures in modern industry. Finding a single viable molecule can take a decade and cost billions, and most candidates still don’t make it. A generation of AI startups has promised to fix that — most have made the problem less painful for researchers who are already technically sophisticated enough to use the tools.

But SandboxAQ thinks the bottleneck isn’t the models. It’s the interface.

The company has teamed up with Anthropic to integrate its scientific AI models directly into Claude — putting powerful drug discovery and materials science tools behind a conversational interface that requires no specialized computing infrastructure to use.

Founded roughly five years ago as an Alphabet spinout, SandboxAQ counts Eric Schmidt, Google’s former CEO, as its chairman. The company, which has raised more than $950 million from investors, has built out a number of different business lines, including a cybersecurity business, for instance.

One of the more unique things SandboxAQ does, however, is produce large quantitative models, or LQMs. These proprietary models are “physics-grounded,” meaning they’re built on the rules of the physical world rather than patterns in text. They can run quantum chemistry calculations and simulate both molecular dynamics and microkinetics, the study of how chemical reactions unfold at the molecular level. That matters because it tells researchers how candidate molecules are likely to behave before anyone sets foot in a lab.

“Trained on real-world lab data and scientific equations, LQMs are AI models engineered for the quantitative economy, a $50+ trillion sector spanning biopharma, financial services, energy, and advanced materials,” the company said in a new release that strongly suggests Sandbox AQ isn’t building another chatbot or code assistant — it’s chasing the economy that AI is supposed to transform.

Chai Discovery and Isomorphic Labs — both well-funded bets on better models — have focused on the science. SandboxAQ is focused on who can actually use it.

“For the first time, we have a frontier [quantitative] model on a frontier LLM that someone can access in natural language,” Nadia Harhen, SandboxAQ’s general manager of AI simulation, told TechCrunch. Previously, users of SandboxAQ’s LQMs would have had to provide their own digital infrastructure to run the models.

SandboxAQ’s customers tend to be computational scientists, research scientists, or experimentalists. Generally, these people work at large pharmaceutical or industrial companies and are searching for new materials that can become marketable products.

“Our customers come to us because they’ve tried all the other software out there, and the complexity of their problem is such that it didn’t work or didn’t yield positive results for them when that translation went to take place in the real world,” said Harhen.

For years, the field of robotics has used the terms “dull, dirty, and dangerous” (DDD) to describe the types of tasks or jobs where robots might be useful—by doing work that’s undesirable for people. A classic example of a DDD job is one of “repetitive physical labor on a steaming hot factory floor involving heavy machinery that threatens life and limb.”

But determining which human activities fit into these categories is not as straightforward as it seems. What exactly is a “dull” task and who makes that assumption? Is “dirty” work just about needing to wash your hands afterwards, or is there also an aspect of social stigma? What data can we rely on to classify jobs as “dangerous?” Our recent work (which was not dull at all) tackles these questions and proposes a framework to help roboticists understand the job context for our technology.

First, we did an empirical analysis of robotics publications between 1980 and 2024 that mention DDD and found that only 2.7% define DDD and only 8.7% provide examples of tasks or jobs. The definitions vary, and many of the examples aren’t particularly specific (e.g., “industrial manufacturing,” “home care”). Next, we reviewed the social science literature in anthropology, economics, political science, psychology, and sociology to develop better definitions for “dull,” “dirty,” and “dangerous” work. Again, while it might seem intuitive which tasks to put into these buckets, it turns out that there are some underlying social, economic, and cultural factors that matter.

Dangerous Work: Occupations or tasks that result in injury or risk of harm

It’s possible to measure the danger of a task or job by using reported information: there are administrative records and surveys that provide numbers on occupational injury rates and hazardous risk factors. While that seems straightforward, it’s important to understand how these data were collected, reported, and verified.

First, occupational injuries tend to be underreported, with some studies estimating up to 70% of cases missing in administrative databases. Second, injuries and risk factors are rarely disaggregated by characteristics like gender, migration status, formal/informal employment, and work activities. For example, because most personal protective equipment, such as masks, vests, and gloves, are sized for men, women in dangerous work environments face increased safety risks.

These caveats are an opportunity for robotics to be helpful: If we went out and looked for it, we could probably find some less obvious dangerous work where robotics might be an important intervention, not to mention some groups that are disproportionately affected and would benefit from more workplace safety.

Dirty Work: Occupations or tasks that are physically, socially, or morally tainted

Colloquially, most people might think of dirty work as involving physical dirtiness, like trash, cleaning, or hazardous substances But social science literature makes clear that dirty work is also about stigma. Socially tainted jobs are often servile or involve interacting with stigmatized groups (e.g., correctional officer) and morally tainted jobs include tasks that people commonly perceive as sinful, deceptive, or otherwise defying norms of civility (e.g., stripper, collection agent.)

“Dirty work” is a social construct that can vary across time (like tattoo industry stigma in the US) and culture (such as nursing in the US vs. in Bangladesh). One way to measure whether work is “dirty” is by using the closely related concept of occupational prestige, captured through quantitative surveys where people rank jobs. Another way to measure it is through qualitative data, like ethnographies and interviews. Similar to “dangerous,” we see some hidden opportunities for robotics in “dirty” work. But one of our more interesting takeaways from the data is that a lower-ranked job can be something that the workers themselves enjoy or find immense pride and meaning in. If we care about what tasks are truly undesirable, understanding this worker perspective is important.

Dull Work: Occupations or tasks that are repetitive and lacking in autonomy

When it comes to defining dull work, what matters most is workers’ own experiences. Outsiders can make a lot of false assumptions about what tasks have value and meaning. Sometimes things that seem boring or routine create the right conditions for developing skills and competence, such as the concentration needed for woodworking, or for socializing and support, when tasks are done alongside others. Instead of assuming that repetitive work is negative, it’s important to examine qualitative data on how people experience the work and what purpose it serves for them.

DDD: An actionable framework

In our paper, we propose a framework to help the robotics community explore how automation impacts individual jobs. For each term—dull, dirty, and dangerous—the framework gathers key pieces of information to reflect on what physical or social aspects of the task are, in fact, DDD. Worker perspective is an important part of all three considerations. The framework also emphasizes awareness of context, i.e. the physical and social environment of an occupation and industry that can influence the DDD nature of a task. Our corresponding worksheet suggests existing data sources to draw on, as well as encouraging us to seek out multiple perspectives and consider potential sources of bias in the information.

What makes tasks dull, dirty, or dangerous depends on the perspective of the humans doing those tasks.RAI

Let’s take, for example, the waste and recycling industry. The world generates over 2 billion tons of waste annually, and this figure is expected to rise to nearly 4 billion tons by 2050. Intuitively, trash collection seems like a job that hits all the Ds. Going through our worksheet, we confirm that globally, workers in this industry face significant health hazards (dangerous), and waste collection is ranked as a low-status job (dirty), although interestingly, many workers take pride in providing this essential service.

The job is also repetitive, but there are aspects that make it not dull. Specifically, workers cite the day-to-day interaction with their coworkers (which includes extensive insider vocabulary, work hacks, and mutual aid groups) and task variety as two of the most enjoyable aspects of the job. Task variety includes inspecting their vehicle and equipment, driving their truck, coordinating with crew members, lifting bins and bags, detecting incorrect sorting of waste, and unloading at the end destination.

This finding matters, because some types of robotic solutions will eliminate the parts of the job that workers most appreciate. For instance, the National Institute for Occupational Safety and Health (NIOSH) recommends the adoption of automated side loader trucks and collision avoidance systems. This innovation increases safety, which is great, but it also results in a sole worker operating a joystick in a cab, surrounded by sensor and camera surveillance.

Instead, we should challenge ourselves to think of solutions that make jobs safer without making them terrible in a different way. To do this, we need to understand all aspects of what makes a job dull, dirty, or dangerous (or not.) Our framework aims to facilitate this understanding.

Finally, it’s important to note that DDD is only one of many possible approaches to classify what work might be better served by robots–there are lots of ways we could think about which types of tasks or jobs to automate (e.g., economic impact, or environmental sustainability). Given the popularity of DDD in robotics, we chose this common phrase as a starting point. We would love to see more work in this space, whether it’s data collection on DDD itself, or the creation of other frameworks.

At RAI, we believe that the fusion of robotics and social sciences opens a whole new world of information, perspectives, opportunities, and value. It fosters a culture of curiosity and mutual learning, and allows us to create actionable tools for anyone in robotics who cares about societal impact.

Dull, Dirty, Dangerous: Understanding the Past, Present, and Future of a Key Motivation for Robotics, by Nozomi Nakajima, Pedro Reynolds-Cuéllar, Caitrin Lynch, and Kate Darling from the RAI Institute, was presented at the 21st ACM/IEEE International Conference on Human-Robot Interaction (HRI) in Edinburgh, Scotland.

In the late 1940s—when computer engineers were grappling with unreliable hardware and noisy transmission environments—a team of engineers inside a modest lab at the University of Manchester, England, confronted a problem so fundamental that it threatened the viability of digital computing itself. Machines could generate bits, but they could not reliably read them back.

The inconsistent reading back of memory data did not initially present itself as a grand theoretical challenge. It showed up as something more mundane: inconsistent computing results.

Engineers including Frederic C. Williams, Tom Kilburn, and G. E. (Tommy) Thomas traced the failures not to logic errors but to the physical behavior of the machines themselves. The team devised a technique for keeping a transmitter and a receiver synchronized without relying on a separate clock signal. Their innovation, known as Manchester code or phase encoding, encoded each bit with a transition in the middle of the bit period, effectively embedding timing information directly into the data stream to be a self-clocking signal. So, even if the signal degraded or the timing drifted slightly, the receiver could continually keep time based on those regular transitions.

By eliminating the need for separate clocks and reducing synchronization errors, Manchester code made data transfer more robust across cables and circuits.

Those qualities later made it a natural fit for technologies such as Ethernet and early data storage systems. Its self-clocking nature helped standardize how machines communicate, and it laid the groundwork for modern networking and digital communication protocols.

On 13 April 2026, this breakthrough was honored with an IEEE Milestone plaque during a ceremony at the University of Manchester. Dignitaries from IEEE and the university attended the ceremony.

Embedding timing in signals

Those 1940s Manchester University engineers were working on systems that fed into the Manchester Mark I, one of the first practical stored-program machines.

When troubles arose, they used oscilloscopes to probe signals. They found that electrical pulses did not arrive with consistent timing. Memory signals also blurred over time, making them harder to read, and when long runs of identical bits occurred, the waveform flattened into stretches with no transitions.

That led to a crucial insight: The problem was not just detecting whether a signal was high or low; the system also lost track of when to sample the signal. Without reliable timing markers, even correctly formed signals were misread. Bits could effectively be lost or miscounted because the system fell out of sync.

At first, the engineers tried to tame the hardware. They experimented with stabilizing circuits and more consistent pulse generation, attempting to impose a regular rhythm on an inherently unstable system. But the fixes proved fragile, and the electronics of the day could not maintain the required precision. So the Manchester group took a different approach.

If the hardware could not provide a dependable clock, the signal itself would have to carry one. Instead of representing data as static levels, each bit changed state, with a guaranteed transition in the middle.

Embedding timing in the signal reduced erratic behavior. Machines were suddenly able to reliably transmit, store, and read back data—an essential step toward practical stored-program computing.

Making signals unmistakable

The Manchester code addressed several issues at once. Regular transitions allowed continuous timing recovery. Transitions proved easier to detect than static levels, and long runs of identical bits no longer produced flat, ambiguous waveforms. Rather than fighting the imperfections of early electronics, the design worked with them.

From lab curiosity to a global standard

What began as a local solution in Manchester shaped digital communication systems for decades, including early Ethernet technology, for which timing and shared-medium communication were central challenges.

According to Robert Metcalfe, a member of the team that built the first Ethernet system at Xerox PARC in 1973, he and his colleagues relied on Manchester code.

“Manchester code solved a fundamental problem for us: timing,” Metcalfe says, explaining that each bit carried its own clock and removed the need for a global synchronized signal.

That self-clocking property wasn’t the only benefit provided by the encoding scheme. On a shared coaxial cable, Manchester encoding did more than provide timing. Each transceiver left the medium undriven—effectively “off”—most of the time, allowing packets from other machines to pass without interference. Even during transmission, a station drove the signal only about half the time, leaving the line undriven during the other half of each bit cycle.

This distinction—between a driven signal and an undriven line, rather than simple 1s and 0s—allowed receivers to recover both data and clock timing while also monitoring the cable for other activity. If a transceiver detected a signal when it expected the line to be undriven,the signal indicated that another station was transmitting at the same time. In other words, the system could detect collisions in real time and respond accordingly.

The idea has proven durable far beyond local networks. Manchester code is being used aboard theVoyager spacecraft, which are now cruising through interstellar space—underscoring its reliability in extreme environments.

The code also has found its way into everyday consumer electronics. Infrared remote controls for televisions and audio equipment commonly rely on Manchester code through protocols such as RC-5, developed by Philips in the early 1980s. The protocol encodes commands as timed infrared signals transmitted by a handset’s integrated circuit and LED, allowing devices to reliably interpret button presses even through noise and signal distortion. Manufacturers across Europe—and many in the United States—adopted the approach, extending Manchester code into the home.

Why the Milestone matters

An IEEE Milestone designation recognizes technologies with enduring impact. Manchester code qualifies because it solved a foundational timing problem at a critical moment in computing history.

Without a way to embed timing in the data itself, early digital systems would have remained fragile and unreliable. Manchester code helped transform them into dependable machines, and it enabled much of today’s digital communication.

“Manchester code solved a fundamental problem for us: timing,” —Robert Metcalfe, an Ethernet inventor

Key participants at the plaque dedication ceremony included Tom Coughlinm 2024 IEEE president; Duncan Ivison, University of Manchester president and vice chancellor, and Nagham Saeed, chair of the IEEE U.K. and Ireland Section.

Talks by Kees Schouhamer Immink (the 2017 IEEE Medal of Honor laureate probably best known for his work that made compact discs and other high-density digital media practical) and Peter Green (Manchester’s deputy dean for the engineering faculty) highlighted the code’s lasting impact on digital data storage and communications.

The IEEE Milestone plaque for the Manchester code reads:

“At this site in 1948–1949, Manchester code was invented for reliably encoding digital data stored on the Manchester Mark I computer’s magnetic drum. It became a standard for computer magnetic tapes and floppy disks and was used in digital communications, including the Voyager 1 and 2 spacecraft and early Ethernet networks. It found wide use in domestic remote controllers, radio frequency identification (RFID) tags, and many control network standards.”

Administered by the IEEE History Center and supported by donors, the Milestone program recognizes outstanding technical developments worldwide. The IEEE U.K. and Ireland Section sponsored the nomination.