Jan Liphardt is the Founder and CEO of OpenMind, a company developing a decentralized communication and coordination network for intelligent machines. His work spans single-molecule biophysics, AI, robotics, and decentralized systems, with a central focus on establishing verifiable trust in autonomous technology. ^{[1] [2]}​

Early Life and Education

Liphardt grew up in Michigan and New York. ^[3] His educational background has been reported differently across sources. According to his biography at Stanford University, he received a Bachelor of Arts from Reed College between 1993 and 1996, followed by a Ph.D. from the University of Cambridge's Churchill College, which he completed in 1999. ^[3] Other professional profiles state that he earned a Diploma in Physics from the University of Heidelberg in Germany and a Ph.D. in Physics from the University of Chicago. ^[1]

His doctoral research involved computational biology, where he used stochastic context free grammars (SCFGs) to identify complex genomic patterns, such as palindromic sequences, that are difficult for conventional machine learning models to detect. He applied this work to study signals in the yeast genome potentially related to translational recoding. ^[3]

Career

Liphardt's career has combined academic research in biophysics with entrepreneurship in the fields of robotics and decentralized technology.

Academic Career

After completing his Ph.D., Liphardt became a postdoctoral researcher at the University of California, Berkeley, working in the physics and chemistry departments under Carlos Bustamante and Nacho Tinoco, Jr. His postdoctoral work focused on developing methods to manipulate single strands of RNA with light to study the dynamics of small biological systems. He later became a divisional fellow at the Physical Biosciences Division of Lawrence Berkeley National Lab. ^[3]​

In 2004, he joined the physics faculty at UC Berkeley. He later moved to Stanford University, where he serves as a Professor of Bioengineering. ^{[2] [3]} At Stanford, he leads the Liphardt Lab, which concentrates on quantitative biology, synthetic biology, cancer research, and single-molecule biophysics. His academic lab has received funding support from government agencies including the National Cancer Institute (NCI), the National Institute of General Medical Sciences (NIGMS), the National Science Foundation (NSF), and the Department of Energy (DOE). Liphardt also teaches several courses at Stanford, including "Engineering Living Matter" (BioE80), "Beyond Bitcoin: Applications of Distributed Trust" (BioE60), and the AI/Machine Learning module in BioE301C. ^[3]​

Entrepreneurship and Writings

In addition to his academic work, Liphardt is an active entrepreneur and writer. In June 2016, he announced the launch of CancerBase.org, a project designed to live-stream crowd-sourced cancer data. ^[4] He also authors a personal blog called "Robots, Data, and Networks," where he explores topics like hybrid human-robot economies, the role of cryptocurrency for artificial general intelligence (AGI), and the application of AI in medical technology. ^[5] He has also been a contributor to publications such as CoinDesk. ^[1]​

In the mid-2020s, Liphardt founded OpenMind, a San Francisco-based company where he serves as CEO. ^{[6] [7]}​

OpenMind

Liphardt founded OpenMind to address what he identifies as a fundamental "trust gap" in robotics and AI. ^[8] The company's mission is to create a universal, secure, and interoperable network to serve as the "connective tissue" for intelligent machines, allowing robots and AI agents from different manufacturers to collaborate and share data safely. ^[7] The company's vision is a "connected ecosystem of intelligent machines that can think, learn, and collaborate - across platforms, manufacturers, and missions." ^[2]​

Technology and Architecture

OpenMind's platform is built on two core components designed to create an open and secure coordination layer for robotics.

• OM1 Operating System: An open-source, hardware-agnostic, and AI-native operating system for robots. It is described as an "equivalent of Android for robots," designed to allow any machine to "install intelligence" and enable it to perceive, adapt, and act within complex human environments. ^{[7] [2]}
• FABRIC Protocol: A decentralized coordination layer that acts as a "trust layer" for intelligent machines. It allows robots to securely verify identities, share contextual information, and coordinate tasks with other machines, regardless of their manufacturer. ^[7] Liphardt has also referred to a project named "FABRIC" in the context of teleoperation systems that enable human-in-the-loop control for building trust and collecting training data. ^[4]

The architecture is decentralized and utilizes blockchain technology to create a transparent and tamper-proof record of machine-to-machine interactions. Liphardt has stated that OpenMind uses the Ethereum blockchain and a concept he calls "ERC-7777 ('Governance for Human Robot Societies')" to "immutably expose each machine's governance logic." This is intended to create an auditable foundation for safety and trust. ^[4] He connects this directly to the challenge of implementing verifiable rules for AI behavior, stating:

"When Asimov enumerated his laws of robotics in 1950, he did not explain how those laws would be created, changed, and shared. Blockchains — immutable global ledgers — directly support this need: when you interact with a robot, you should be able to go somewhere to look up what rules it’s following." ^[9]

Funding

On August 4, 2025, OpenMind announced that it had raised a $20 million funding round. The round was led by Pantera Capital, with participation from Ribbit, Coinbase Ventures, HSG, DCG, Pebblebed, Topology, Primitive Ventures, Lightspeed Faction, and Anagram, among other angel investors. The capital was intended to scale the company's engineering team, expand global partnerships, and market its OM1 and FABRIC platforms for use in smart manufacturing, humanoid robotics, and autonomous transport. ^[7]​

Partnerships

On December 17, 2025, OpenMind announced a strategic partnership with NEAR AI. The collaboration involves integrating NEAR AI Cloud's private inference technology into OpenMind's robotics operating system. This is designed to address consumer privacy concerns for in-home robotics by offloading heavy AI computation to a secure cloud environment that uses Trusted Execution Environments (TEEs), such as Intel TDX and NVIDIA Confidential Compute. This allows data to be processed without being exposed, even to the cloud provider, and generates a cryptographic proof of secure processing. Ilia Polosukhin, CEO of NEAR AI, is also a personal investor in OpenMind. ^[10]​

Views and Philosophy

Liphardt's work is guided by a philosophy centered on building verifiable trust between humans and intelligent machines. He advocates for open and modular software so that humans can "look inside, understand how it works, and trust thinking machines." ^[4] He has frequently stated his belief that trust cannot be an optional feature but must be a core part of the system's architecture from the start. ^[8]

His views on key technological intersections include:

• AI Safety and Decentralization: Liphardt argues that as AI capabilities advance, decentralized systems become essential for safety. He believes that blockchains provide a necessary, immutable record of machine actions ("who did what, when"), which is critical for accountability and preventing malicious AI behavior. ^[4]
• The Role of Cryptocurrency in AI: In his writings, Liphardt has proposed that cryptocurrency will serve as "AGI’s fundamental payment and governance rail." He envisions a future where crypto wallets enable robots to transact with, collaborate with, and form teams with humans to accomplish shared goals. ^[5]
• Human-in-the-Loop Robotics: He is a proponent of using teleoperation to have humans remotely control robots. He frames this not as a way to reduce wages, but as a method to create new jobs globally, increase user trust by keeping humans involved, and collect data to optimize robot behavior for specific communities and cultural contexts. ^[4]

Reflecting his overall vision for human-machine interaction, Liphardt has said, "if we're going to live with machines, we should know how they think - and we should help them think better." ^[2]

Interviews

The Open Future of Robotics #01

On February 28, 2025, Jan Liphardt participated in an interview published on the YouTube channel of ETHDenver, in which he discussed topics related to robotics, software architectures, and open development models.

In the interview, Liphardt stated that robotic hardware has undergone continuous development over several decades, while the software systems that control robots are, in many cases, proprietary. According to him, this software structure limits external inspection, interoperability between platforms, and coordinated development across organizations. He associated these limitations with reduced transparency in how robotic systems process information and make decisions.

Liphardt described the OpenMind initiative as an effort to develop an open source software stack for robotics. He compared this model to software ecosystems in other technology sectors, noting that standardized and openly accessible platforms can support compatibility across devices and applications. In his account, such an approach could allow robotic systems to exchange data, operational routines, and learned behaviors across different environments.

The interview also addressed the use of large language models within robotic systems. Liphardt explained that multiple models can be used to convert sensor inputs into structured internal representations that inform robot actions. This design was presented as an alternative to traditional control pipelines, with the aim of handling complex sensory data and behavior generation within a unified framework.

A live demonstration conducted during the interview experienced technical issues, which Liphardt referenced as illustrative of ongoing challenges in deploying robotic systems outside laboratory conditions. These issues included hardware and communication constraints that affected system performance.

Liphardt further outlined a scenario in which robots maintain persistent digital identities and are capable of automated transactions with other machines. He mentioned blockchain based wallets as one possible mechanism for enabling machine to machine exchanges of data or services. According to his description, such features would support decentralized coordination among robotic systems operating within shared networks. ^[11]

Perspectives on Decentralized Robotics Systems #02

An interview with Jan Liphardt was published on the YouTube channel Fresh Consulting on August 18, 2025. In this interview, Liphardt outlines his interpretation of how decentralized systems relate to the development and deployment of intelligent robots in domestic, urban, and institutional environments. He frames robotics as moving from standalone machines toward interconnected systems that require defined mechanisms for coordination, identity, and oversight.

Speaking in his role as founder of OpenMind, Liphardt describes decentralized architectures as a structural approach in which robots can identify one another, exchange capabilities, and operate across shared environments without reliance on a single centralized controller. He contrasts this with centralized models, which he characterizes as limited when applied to autonomous robots operating simultaneously in homes, cities, and healthcare facilities. The interview addresses technical concerns such as machine identity, communication standards, transparency, and security, presenting them as unresolved requirements for large scale robotic interaction.

Liphardt explains that OpenMind’s software design is based on open source and modular principles. According to his description, robotic behavior is managed by multiple specialized AI components that communicate with each other, rather than by a single unified model. He presents this structure as a way to support system maintenance, incremental updates, and the controlled transfer of skills between different robotic platforms. The interview also references the use of publicly verifiable rule sets, including blockchain based records, as a method for documenting constraints on robotic behavior.

The interview further reflects Liphardt’s view that robotics applications are not limited to task execution. He discusses possible roles for robots in education, healthcare, accessibility support, and elder care, where machines may assist or accompany individuals within predefined boundaries. Throughout the discussion, he frames governance frameworks, ethical constraints, and public visibility into system behavior as conditions that must accompany continued robotics development. ^[12]