In an interview with crypto.news, Selva Ozelli speaks with RoboTech Frontier Hub founder Denis “Dan” Saklakov about the intersection of artificial intelligence, blockchain and finance, and how his AI assisted investment tool Meijin uses an investor’s risk profile to manage exit strategies for assets including cryptocurrencies.
Summary
- Meijin monitors assets after purchase and manages exit strategies based on the level of risk selected by the investor.
- Saklakov said blockchain can record AI system states, permissions, decision conditions and execution history without running the AI itself onchain.
- RoboTech Frontier Hub is developing projects across AI verification, robotics, human machine interfaces and advanced computing.
- Saklakov expects verification and deterministic execution systems to become increasingly important as AI takes a larger role in automated financial decisions.
The discussion also covers using blockchain to create verifiable records of AI decisions, permissions and execution history, as well as Saklakov’s work on AI verification, robotics, human machine interfaces and architectures designed to separate machine intelligence from the authority to act.
Denis “Dan” Saklakov is a lawyer, digital asset manager and applied AI scientist whose work includes digital assets, blockchain technology and artificial intelligence. He previously worked with a cryptocurrency exchange and, since 2024, has served as Managing Partner of RoboTech Frontier Hub, where he develops technologies at the intersection of AI, robotics, finance, human-machine interaction and advanced computing.
Tell us about your professional and educational background.
My background crosses law, mathematical statistics, finance, artificial intelligence and technology. I was originally trained as a lawyer and later moved into corporate finance, investment banking, M&A, private equity and asset management. I earned a master’s degree from Northwestern University and more recently completed the MIT Applied AI Science postgraduate program.
Today I live in New York City and serve as Managing Partner of RoboTech Frontier Hub. I describe much of my current work as AI and AGI architecture: how intelligent systems should use information, make decisions and interact with the real world without allowing computational capability to turn automatically into uncontrolled authority.
That question has practical applications in finance, robotics, AI verification, human-machine interfaces and advanced computing.
I describe my broader architecture for safe AGI in my recent book, Before the Machine Chooses for Us: An AGI Architecture for Freedom, Human Survival, and Shared Consciousness.
The central problem of the book is how to make advanced intelligence extraordinarily capable while preventing capability itself from becoming self-authorized power.
Tell us about your journey to form RoboTech Frontier Hub in 2024, an accelerator focused on advancing robotics, automation, and artificial intelligence technologies, particularly your AI-assisted investment tool Meijin.
RoboTech Frontier Hub grew out of a problem I encountered repeatedly while working with scientific and engineering ideas.
A lot of interesting technology never develops properly because inventors are afraid that explaining the scientific principle will allow somebody to steal the product. So everything remains secret. But when the science itself is hidden, other researchers cannot test it, criticize it, identify its limits or connect it with another field.
We decided to use a different model.
At RoboTech, we try to put real scientific work underneath the technology. Where appropriate, we publish enough of the scientific foundation for other researchers to examine, challenge and develop it. At the same time, we currently protect the actual implementation, algorithms and commercial technology.
In simple terms, we do not want to hide the science just to protect the product. We want the scientific idea to survive examination, and then we build technology on top of what remains valid.
Meijin is one example.
The basic observation behind Meijin is very simple. Investors often spend enormous amounts of time deciding what cryptocurrency, stock or ETF to buy, but devote much less disciplined thought to deciding when to sell it.
Meijin does not choose the asset for you.
You choose Bitcoin, Ethereum, Zcash, Moderna stock, an oil contract or another asset. Meijin starts working after you own it.
We first quantify the level of risk the investor is prepared to accept. The system then continuously monitors the position and manages an exit strategy around that risk profile.
It may reduce or sell a position in stages as conditions change.
The purpose is not to promise the exact highest possible selling price. Nobody can honestly guarantee that. The purpose is to make the sell decision systematic rather than emotional.
Crypto makes this particularly useful because the market operates twenty-four hours a day. The investor sleeps. The monitoring system does not have to.
Another important distinction is that the final execution logic is deterministic and auditable. We can use AI for analysis, but we do not want a language model simply improvising the final decision to move somebody’s money.
Meijin is also only one of our projects.
Awareness Runtime deals with another problem created by modern AI: a model can produce a very convincing answer that is not actually supported by evidence. We are developing a verification layer designed to evaluate what is supported before an AI-generated conclusion becomes a professional decision or an external action. That is particularly important for finance professionals and lawyers.
Theta-Star and the Guardrail work address a different question: even if an AI system reaches the correct conclusion, who gave it authority to act? We separate intelligence from authorization so that the system proposing an action cannot simply create its own permission to execute it. This becomes particularly important in the automation of pilots, drivers and other high-stakes professions where decisions may have to be made extremely quickly.
ActionAtlas takes some of our work into robotics. The idea is to provide robots with compact, specialized information packages that can remain locally available when permanent cloud connectivity, GPS or external infrastructure cannot be assumed. Essentially, it could function as a map for your home robot, for example.
NeuroPhase explores another frontier: interfaces between computational systems and human neurological signals. Our longer-term objective is to move the machine interface closer to the natural information processes of the human brain rather than requiring the human body to become progressively more invasive just to communicate efficiently with machines. In other words, the long-term direction is a merger of human and machine, but one in which we try to move the machine closer to the human rather than forcing the human body to become progressively more machine-like.
We are also researching quantum computing in microgravity and orbital environments. The underlying scientific question is whether certain quantum-computing platforms could benefit from combinations of microgravity, low temperatures and mechanical isolation. Longer term, this could become relevant to the training and operation of future quantum-enabled superintelligent systems in distant orbital environments.
Another direction, Critical Systems, applies analytical and optimization methods to physical supply chains, including identifying real bottlenecks in critical industrial and aerospace manufacturing.
These projects look very different, but they come from the same model: identify the underlying scientific problem, determine where the evidence and mathematics actually support the idea, expose enough of that scientific foundation to examination, and then build a protected technology within those boundaries.
Tell us about the intersection of blockchain and AI.
For me, one of the most useful intersections of blockchain and AI is trust in machine decisions.
This becomes very concrete with something like Meijin.
Suppose an AI system analyzes a crypto position and decides that the investor should reduce it.
The important questions are not only: “What did the AI decide?” or “Was the prediction good?”
We also need to know what information the system used, what state it was in, what rules applied and whether the system was actually authorized to execute the decision.
Blockchain can help with that without running the AI itself on-chain.
The computation can remain on conventional hardware. What can be cryptographically anchored to an independent ledger is the relevant state of the system, permissions, decision conditions and execution history.
The important part is that the AI making the decision should not control the record that is later used to prove what happened.
It should not be able to make one decision, rewrite the history and later claim that it made another one.
Blockchain does not magically make an AI model correct. A false statement can be stored perfectly on a blockchain.
What blockchain can provide is provenance and a record that is difficult for the decision-making system itself to rewrite.
For crypto investors, that distinction becomes increasingly important as AI moves from simply providing market commentary to systems that can potentially interact directly with accounts, exchanges and financial infrastructure.
Imagine the same problem in medical care. You may want a nanorobot to clean your arteries, but you absolutely want to prevent it from doing something that could kill you. You therefore need a bulletproof, externally protected record of the rules defining what that nanorobot is and is not permitted to do. This is exactly the kind of problem for which blockchain or another independently secured cryptographic ledger can become extremely useful.
I think we are moving toward a world in which AI provides analysis at machine speed, while cryptographic systems help establish what state existed, what authority was granted and what action actually occurred.
That is much more interesting to me than simply putting another token around an AI product.
AI has been in the forefront of news lately, with the leaders of the largest AI companies suggesting that development of frontier models should slow down. Any thoughts on this?
AI is developing very quickly, and in crypto the consequences will probably become visible particularly fast because digital-asset markets are already digital, global, automated and open twenty-four hours a day.
I do not think the useful question for a trader is simply whether AI development is “too fast” or “too slow.”
The practical question is what happens when increasingly capable AI systems begin analyzing markets, executing strategies and communicating with financial infrastructure faster than a human being can realistically supervise every individual decision.
That is why I think the next stage is not simply better prediction.
We need better verification.
A very powerful AI that produces a trading recommendation in milliseconds is not particularly useful if nobody can determine whether the underlying information was reliable or whether the system had permission to make the resulting transaction.
This is where I see AI and blockchain becoming complementary.
AI can perform increasingly sophisticated analysis.
Blockchain and related cryptographic infrastructure can help establish provenance, permissions and an independently verifiable record.
Deterministic execution systems can then define what the AI is actually allowed to do.
For the retail investor, all of that should ultimately become almost invisible.
The user should not need to understand the internal architecture.
The practical experience should be much simpler: I selected this asset, I defined how much risk I am willing to accept, the system is watching it continuously, and I can later understand why it acted.
That is the direction we are pursuing with Meijin.
I have previously suggested that the development of AI may become difficult to forecast by conventional methods on a relatively short horizon, potentially around the end of this decade. I do not treat 2030 as some scientifically established deadline. It is a forecasting horizon.
But even without reaching anything we would call AGI or technological singularity, AI is already becoming capable enough to change how financial decisions are made.
For crypto, that change is not theoretical.
Markets already operate at machine speed.
The challenge now is making machine-speed intelligence trustworthy enough to use.
How can people reach you?
The easiest way to reach me is by email at [email protected].
RoboTech Frontier Hub:
https://www.robotechfrontierhub.com
My website:
LinkedIn:
https://www.linkedin.com/in/denissaklakov
I also publish scientific work on arXiv and Zenodo, and research and commentary through Medium and saklakov.com.
About the Author:
Selva Ozelli Esq, CPA, is an international digital asset legal expert and author of Sustainably Investing in Digital Assets Globally and an award winning artist. Her writings are translated into 45 languages and republished in over 200 global publications. She is recognized as an expert media/TV commentator on global AI, digital asset regulation, tax, and technology matters.
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