Why DeFi agents need a private brain

By: bitcoin ethereum news|2025/05/04 19:15:01
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The following is a guest post and opinion of Matej Janež, Head of Partnerships at Oasis. At EthDenver earlier this year, one topic kept coming up again and again: AI and autonomous AI Agents . That excitement has carried over into other crypto conferences as the year has gone on. There is good reason for the excitement, too: these aren’t just ideas anymore – they’re here, and they’re handling real funds – but their reliance on transparent blockchains may become their biggest weakness. What are these AI agents exactly? They’re smart software programs that work on their own to handle specific tasks. In crypto, they can use machine learning and blockchains to watch markets, spot patterns, and make trades automatically. Unlike traditional trading bots, today’s AI agents are adaptive; they refine their behavior continuously based on what yields results. But there’s a big problem that has gone underappreciated and misunderstood: the fact that these onchain agents work on transparent blockchains makes their decision-making — their “brains” — essentially public. This openness creates real obstacles for agents trying to compete in financial markets. AI Agents in DeFi Right now, DeFi agents handle trading across decentralized exchanges, manage lending, and optimize yield farming. They react to market changes instantly, often making quick decisions with lots of money. Smart. Fast. Efficient. But they face a basic challenge. The very system that lets them operate – public blockchains – shows their strategies to everyone. Every transaction, every interaction with a smart contract, leaves a trail that reveals how they “think”. It’s not much different from playing poker with your cards face up on the table. Of course, one could run these strategies on private servers and only submit the final transactions to the blockchain, but this fundamentally defeats the purpose of crypto’s promise of transparency and onchain verifiability. The entire point of DeFi is to remove the need for trusted third parties and centralized systems. Consider what’s already happening in DeFi today. A yield farming bot continually scans for the best returns across protocols, moving millions between lending platforms based on subtle market shifts. If its strategy becomes visible on-chain, competitors simply watch which pools it enters and exits, at what thresholds, and with what timing—then clone the strategy without the research costs. In decentralized credit markets, AI agents that score wallets for under-collateralized loans become pointless if borrowers can see exactly which behaviors improve their scores, leading to artificial wallet patterns designed to game the system. Most concerning might be DAO treasury agents—when their rebalancing strategy is transparent, anyone can front-run major liquidity moves, effectively stealing from the community with each transaction. These aren’t edge cases; they’re fundamental flaws in applying AI to transparent systems where strategy execution and strategy development are impossible to separate. Arguably, worst of all is the potential for market manipulation. When bad actors understand how an agent makes decisions, they can create situations designed to trick it. Markets full of transparent agents are easy targets. Why a “Private Brain”? A “private brain” for DeFi agents would fix these problems. By keeping computations confidential, agents could make decisions without showing their logic or intentions until transactions go through. The security benefits are obvious. Strategies stay protected from copycats. Front-running becomes harder without seeing pending transactions. The agent’s work stays private. Teams that build better algorithms get to keep their edge, creating reasons to keep improving. The market rewards actual improvement instead of fast copying. On a larger scale, markets would become more stable. When agent strategies stay secret, you avoid herding – where multiple agents follow identical strategies. This cuts down on correlated market movements and lowers system-wide risk. If we keep going like we are now – if DeFi agents keep operating with glass-box brains, we should be worried about a few things happening. Market exploits will become more common and sophisticated. As agents handle more funds, the rewards for exploiting them grow too. Without privacy measures, these exploits become simple technical exercises rather than difficult security breaches. Strategy cannibalization is just as worrying. When winning strategies get copied quickly, they stop working as well. Eventually, all agents use similar approaches, creating a monoculture. The market loses variety and resilience. This leads to what you could all the “Hive Mind” problem; when all agents work the same way, they will react to market changes the same way too. This makes market swings bigger, increases volatility, and creates the risk of flash crashes when conditions trigger widespread identical responses. What starts as individual agents becomes, basically, one massive entity with system-wide effects. To spell it out: these are not the ingredients for a healthy market. Technical Solutions Trusted Execution Environments (TEEs) offer a solid way to create these private brains. TEEs provide secure areas where computation happens in isolation, protected even from the system hosting it. You can verify the work happened correctly, but the details stay private. This tech lets us balance openness and privacy. The framework of an agent can be public and verifiable, while the specific decision-making and strategy details stay protected. Adding private computation to DeFi agents isn’t just helpful—it’s necessary for algorithmic finance to grow properly. Without privacy, we’re building a market where innovation gets punished, exploitation gets rewarded, and system risks pile up under the surface. We’re at a critical juncture in AI-powered finance where our choices will determine whether autonomous agents create a more efficient market or a dangerously fragile one. The technology for private computation exists today, but implementing it requires deliberate action from builders and protocols alike. As financial intelligence moves increasingly on-chain, ensuring these systems can operate with computational privacy won’t just protect individual strategies—it will safeguard the integrity of the entire DeFi ecosystem. Source: https://cryptoslate.com/why-defi-agents-need-a-private-brain/

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Debunking the AI Doomsday Myth: Why Establishment Inertia and the Software Wasteland Will Save Us

Original Title: Against Citrini7Original Author: John Loeber, ResearcherOriginal Translation: Ismay, BlockBeats


Editor's Note: Citrini7's cyberpunk-themed AI doomsday prophecy has sparked widespread discussion across the internet. However, this article presents a more pragmatic counter perspective. If Citrini envisions a digital tsunami instantly engulfing civilization, this author sees the resilient resistance of the human bureaucratic system, the profoundly flawed existing software ecosystem, and the long-overlooked cornerstone of heavy industry. This is a frontal clash between Silicon Valley fantasy and the iron law of reality, reminding us that the singularity may come, but it will never happen overnight.


The following is the original content:


Renowned market commentator Citrini7 recently published a captivating and widely circulated AI doomsday novel. While he acknowledges that the probability of some scenes occurring is extremely low, as someone who has witnessed multiple economic collapse prophecies, I want to challenge his views and present a more deterministic and optimistic future.


Never Underestimate "Institutional Inertia"


In 2007, people thought that against the backdrop of "peak oil," the United States' geopolitical status had come to an end; in 2008, they believed the dollar system was on the brink of collapse; in 2014, everyone thought AMD and NVIDIA were done for. Then ChatGPT emerged, and people thought Google was toast... Yet every time, existing institutions with deep-rooted inertia have proven to be far more resilient than onlookers imagined.


When Citrini talks about the fear of institutional turnover and rapid workforce displacement, he writes, "Even in fields we think rely on interpersonal relationships, cracks are showing. Take the real estate industry, where buyers have tolerated 5%-6% commissions for decades due to the information asymmetry between brokers and consumers..."


Seeing this, I couldn't help but chuckle. People have been proclaiming the "death of real estate agents" for 20 years now! This hardly requires any superintelligence; with Zillow, Redfin, or Opendoor, it's enough. But this example precisely proves the opposite of Citrini's view: although this workforce has long been deemed obsolete in the eyes of most, due to market inertia and regulatory capture, real estate agents' vitality is more tenacious than anyone's expectations a decade ago.


A few months ago, I just bought a house. The transaction process mandated that we hire a real estate agent, with lofty justifications. My buyer's agent made about $50,000 in this transaction, while his actual work — filling out forms and coordinating between multiple parties — amounted to no more than 10 hours, something I could have easily handled myself. The market will eventually move towards efficiency, providing fair pricing for labor, but this will be a long process.


I deeply understand the ways of inertia and change management: I once founded and sold a company whose core business was driving insurance brokerages from "manual service" to "software-driven." The iron rule I learned is: human societies in the real world are extremely complex, and things always take longer than you imagine — even when you account for this rule. This doesn't mean that the world won't undergo drastic changes, but rather that change will be more gradual, allowing us time to respond and adapt.


The Software Industry Has "Infinite Demand" for Labor


Recently, the software sector has seen a downturn as investors worry about the lack of moats in the backend systems of companies like Monday, Salesforce, Asana, making them easily replicable. Citrini and others believe that AI programming heralds the end of SaaS companies: one, products become homogenized, with zero profits, and two, jobs disappear.


But everyone overlooks one thing: the current state of these software products is simply terrible.


I'm qualified to say this because I've spent hundreds of thousands of dollars on Salesforce and Monday. Indeed, AI can enable competitors to replicate these products, but more importantly, AI can enable competitors to build better products. Stock price declines are not surprising: an industry relying on long-term lock-ins, lacking competitiveness, and filled with low-quality legacy incumbents is finally facing competition again.


From a broader perspective, almost all existing software is garbage, which is an undeniable fact. Every tool I've paid for is riddled with bugs; some software is so bad that I can't even pay for it (I've been unable to use Citibank's online transfer for the past three years); most web apps can't even get mobile and desktop responsiveness right; not a single product can fully deliver what you want. Silicon Valley darlings like Stripe and Linear only garner massive followings because they are not as disgustingly unusable as their competitors. If you ask a seasoned engineer, "Show me a truly perfect piece of software," all you'll get is prolonged silence and blank stares.


Here lies a profound truth: even as we approach a "software singularity," the human demand for software labor is nearly infinite. It's well known that the final few percentage points of perfection often require the most work. By this standard, almost every software product has at least a 100x improvement in complexity and features before reaching demand saturation.


I believe that most commentators who claim that the software industry is on the brink of extinction lack an intuitive understanding of software development. The software industry has been around for 50 years, and despite tremendous progress, it is always in a state of "not enough." As a programmer in 2020, my productivity matches that of hundreds of people in 1970, which is incredibly impressive leverage. However, there is still significant room for improvement. People underestimate the "Jevons Paradox": Efficiency improvements often lead to explosive growth in overall demand.


This does not mean that software engineering is an invincible job, but the industry's ability to absorb labor and its inertia far exceed imagination. The saturation process will be very slow, giving us enough time to adapt.


Redemption of "Reindustrialization"


Of course, labor reallocation is inevitable, such as in the driving sector. As Citrini pointed out, many white-collar jobs will experience disruptions. For positions like real estate brokers that have long lost tangible value and rely solely on momentum for income, AI may be the final straw.


But our lifesaver lies in the fact that the United States has almost infinite potential and demand for reindustrialization. You may have heard of "reshoring," but it goes far beyond that. We have essentially lost the ability to manufacture the core building blocks of modern life: batteries, motors, small-scale semiconductors—the entire electricity supply chain is almost entirely dependent on overseas sources. What if there is a military conflict? What's even worse, did you know that China produces 90% of the world's synthetic ammonia? Once the supply is cut off, we can't even produce fertilizer and will face famine.


As long as you look to the physical world, you will find endless job opportunities that will benefit the country, create employment, and build essential infrastructure, all of which can receive bipartisan political support.


We have seen the economic and political winds shifting in this direction—discussions on reshoring, deep tech, and "American vitality." My prediction is that when AI impacts the white-collar sector, the path of least political resistance will be to fund large-scale reindustrialization, absorbing labor through a "giant employment project." Fortunately, the physical world does not have a "singularity"; it is constrained by friction.


We will rebuild bridges and roads. People will find that seeing tangible labor results is more fulfilling than spinning in the digital abstract world. The Salesforce senior product manager who lost a $180,000 salary may find a new job at the "California Seawater Desalination Plant" to end the 25-year drought. These facilities not only need to be built but also pursued with excellence and require long-term maintenance. As long as we are willing, the "Jevons Paradox" also applies to the physical world.


Towards Abundance


The goal of large-scale industrial engineering is abundance. The United States will once again achieve self-sufficiency, enabling large-scale, low-cost production. Moving beyond material scarcity is crucial: in the long run, if we do indeed lose a significant portion of white-collar jobs to AI, we must be able to maintain a high quality of life for the public. And as AI drives profit margins to zero, consumer goods will become extremely affordable, automatically fulfilling this objective.


My view is that different sectors of the economy will "take off" at different speeds, and the transformation in almost all areas will be slower than Citrini anticipates. To be clear, I am extremely bullish on AI and foresee a day when my own labor will be obsolete. But this will take time, and time gives us the opportunity to devise sound strategies.


At this point, preventing the kind of market collapse Citrini imagines is actually not difficult. The U.S. government's performance during the pandemic has demonstrated its proactive and decisive crisis response. If necessary, massive stimulus policies will quickly intervene. Although I am somewhat displeased by its inefficiency, that is not the focus. The focus is on safeguarding material prosperity in people's lives—a universal well-being that gives legitimacy to a nation and upholds the social contract, rather than stubbornly adhering to past accounting metrics or economic dogma.


If we can maintain sharpness and responsiveness in this slow but sure technological transformation, we will eventually emerge unscathed.


Source: Original Post Link


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