2026-02-07 19:00:03
:::info Astounding Stories of Super-Science March, 1932, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Affair of the Brains - Chapter VIII:Dr. Ku Shows His Claws
By Anthony Gilmore
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\ The scientist brushed back his thinning white hair with a trembling hand. He knew that voice. He walked over and put his hands on his friend's shoulders.
"Carse!" he exclaimed. "Thank God, you're alive!"
"And you," said the Hawk.
Ku Sui interrupted.
"I am most glad, honored Master Scientist," he said in the flowery Oriental fashion that he affected in his irony, "to welcome you here. For me it is a memorable occasion. Your presence graces my home, and, however unworthily, distinguishes me, rewarding as it does aspirations which I have long held. I am humbly confident that great achievements will result from your visit——"
Quickly Eliot Leithgow turned and looked squarely at him. There was no bending of spirit in the frail old man. "Yes," he said, "my visit. Your sickening verbal genuflections beautifully evade the details—the house of my friend raided at night; he, himself, unarmed, shot down in cold blood; his house gutted! You are admirably consistent, Dr. Ku. A brilliant stroke, typical of your best!"
Five faint lines appeared across the Eurasian's high, narrow brow. "What?" he exclaimed. "Is this true? My servitors must be reprimanded severely; and meanwhile I beg you not to hold their impetuousness against me."
\ Carse could stand it no longer. This suave mockery and the pathetic figure of his friend; the mention of raid and murder——
"It's all my fault," he blurted out. "I told him where you were. I thought——"
"Oh, no!" Dr. Ku broke in, pleasantly protesting. "Captain Carse is gallant, but the responsibility's not his. I have a little machine—a trifle, but most ingenious at extracting secrets which persons attempt to hold from me. The Captain couldn't help himself, you see——"
"It was not necessary to tell me that," said Leithgow.
"Of course," the Eurasian agreed and for the first time seriously; "but let me suggest that the end justifies the means. And that brings me to my point. Master Scientist, now you may know that I have for some time been working toward a mighty end. This end is now in sight, with you here, the final achievement can be attained. An achievement——" He paused, and the ecstasy of the inspired fanatic came to his eyes. Never before had the three men standing there so seen him. "I will explain."
His eyes changed, and imperiously he gave an order to his assistants. "A chair for Master Leithgow, and one for Carse. Place them there." Then, "Be seated," he invited them with a return of his usual seeming courtesy. "I'm sure you must be tired."
Slowly Eliot Leithgow lowered himself into the metal seat. Friday, ignored, shifted his weight from one foot to the other. The Hawk did not sit down until with old habit he had sized up the whole layout of laboratory, assistants and chances. The two chairs faced toward ward the high screen; to each side stood the five coolie-guards; mechanically alert as always; the four Caucasian assistants made a group of strange statues to the right.
Ku Sui took position, standing before the screen. Seldom did the cold, hard iron of the man show through the velvet of his manner as now.
"Yes," he said, "I will talk to you for a while; give you broad outline of my purpose. And when I have finished you will know why I have wanted you here so badly, Master Leithgow."
\ He began, and, as never before, he hid nothing of his monstrous ambition, his extraordinary preparations. With mounting fear his captives listened to his well-modulated voice as it proceeded logically from point to point. He had fine feeling for the dramatic, knew well the value of climax and pause; but his use of them was here unconscious, for he spoke straight from his dark and feline heart.
For the first time in the Affair of the Brains, the tiger was showing his claws.
"For a long time," Ku Sui said, "we four gathered here have fought each other. All over space our conflict has ranged, from Earth to beyond Saturn. I suppose there never have been more bitter enemies; I know there has never been a greater issue. I said we four, but I should have said we two, Master Leithgow. Captain Carse has commanded a certain respect from me, the respect one must show for courage, fine physical coordination and a remarkable instinct and capacity for self-preservation—but, after all, he is primarily only like the black here, Friday, and a much less splendid animal. It is a brain that receives my respect! A brain! Genius! I do not fear Carse: he is only an adventurer; but your brain, Master Leithgow, I respect.
"For, naturally, brains will determine the future of these planets around us. The man with the most profound and extensive scientific knowledge united to the greatest audacity—remember, audacity!—can rule them every one!"
He paused and looked into the eyes of the Master Scientist. Pointedly he said:
"You, Master Leithgow, have the brains but not the audacity. I have the audacity and the brains—now that you are here."
\ Cold prickles of fear chased down Carse's and the scientist's spine at this obscure threat. Some of their reaction must have shown in their faces, for the Eurasian permitted himself a brief, triumphant smile and added:
"You shall know just what I mean in but a few minutes. Right now, in this very laboratory, the fate of the planets is being decided!"
Hawk Carse licked his dry lips.
"Big words!" he said.
"Easily proved, Captain Carse, as you'll see. What can restrain the man who can instantly command Earth's master-minds of scientific knowledge, the man who has both a considerable brain of his own to call on and the mightiest brains in existence, all coordinated for perfect, instant effectiveness. Why, with these brains working for him, he can become omnipotent; there can be but feeble resistance to his steps toward universal power! Only chance, unpredictable chance, always at work, always powerful, can defeat him—and my audacity allows me to disregard what I cannot anticipate."
"You talk riddles," answered Leithgow. "You do not explain your intended means. What you imply you can do with brains is utterly impossible."
"Impossible? Ever a foolish word, Master. You know that the brain has always been my special study. As much as ten years ago, I was universally recognized as the greatest expert in my specialty. But I tell you that my knowledge of the subject was as nothing then to what it is now. I have been very busy these last ten years. Look!"
With a graceful sweep of a hand he indicated the four coolie-guards and his four white-smocked assistants.
"These men of mine," he continued, "do they appear normal, would you say? Or, rather, mechanicalized; lacking in certain things and thereby gaining enormously in the values which can make them perfect servitors? I have removed from their minds certain superficial qualities of thought. The four men in white were, a few years ago, highly skilled surgeons, three of them brain specialists and noted for exceptional intellects and bold, pioneering thinking. I needed them and took them, diverting them from their natural state, in which they would have resisted me and refused my commands. Certain complicated adjustments on their brains—and now their brains are mine, all their separate skill at my command alone!"
\ Leithgow sat back suddenly, astonishment and horror on his face. His lips parted as if to speak, then closed tightly together again. At last he uttered one word.
"Murderer!"
Dr. Ku smiled. "In a sense, yes. But let me go on.
"The reshaping of these mentalities and of the mentalities of all my coolies, were achievements, and valuable ones; but I wanted more. I wanted much more. I wanted the great, important part of all Earth's scientific knowledge at my fingertips, under my control. I wanted the exceptional brains of Earth, the brains of rare genius, the brains that lived like lonely stars, infinitely removed from the common herd. And more than that, I wanted them always; I wanted them ageless. For I had to seal my power!"
The Eurasian's words were coming more rapidly now, though the man's thoughts and tone were still under control; and Carse, sitting there silently, felt that the climax was being reached; that soon something unthinkable, something of dread, would be revealed. The voice went on:
"These brains I wanted were not many—only six in all. Most of them you knew, Master Leithgow, these men who constituted the cream of Earth's scientific ability. Professor Estapp, the good-looking young American; Dr. Swanson, the Swede; Master Scientist Cram—the great English genius Cram, already legendary, the only other of that rank beside yourself; Professor Geinst, the hunchbacked, mysterious German; and Dr. Norman—Dr. Sir Charles Esme Norman, to give him his English title. I wanted these men, and I got them! All except you, the sixth!"
\ Again Dr. Ku Sui smiled in triumph. To Eliot Leithgow his smile was unspeakable.
"Yes," the elderly scientist cried out, "you got them, you murderer!"
"Oh, no, no, Master Leithgow, you are mistaken. I did not kill them. Why should I be stupid as to do that? To these men I wanted so badly? No, no. Because these five scientists disappeared from Earth suddenly, without trace, without hint of the manner of their going, the stupid Earthlings believe they were killed! Stupid Earthlings! Abducted, of course; but why assume they were killed? And why, of all people, decide that Master Scientist Eliot Leithgow had something to do with their disappearance? I confess to having planted that evidence pointing to you, but if they had the sense of a turnip they would know that you were incapable of squashing a flea, let alone destroying five eminent brothers in science! You, jealous, guilty of five crimes passionel! Pour le science! Credulous Earthlings! Incredible Earthlings! And here are you, a hunted man with a price on your head!
"So for ten years you have thought I murdered those five men? No, no. They were very much alive for eight years and very troublesome prisoners. It took me eight years to solve the problem I had set myself.
"You will meet them in a minute—the better part of them. You'll see for yourself that they are very usefully alive. For I succeeded completely with them. I have sealed my power!"
His silk pajamalike clothing rustled loud in the strained silence as he turned to the screen behind him. For some obscure reason the perfume about him, flowers of tsin-tsin, seemed to grow in their nostrils.
"Observe!" he said, and lifted it aside. An assistant threw a switch on a nearby panel. The unnatural quiet in the laboratory was resumed.
"The ultimate concentration of scientific knowledge and genius! The gateway to all power!"
\
:::info About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books.
This book is part of the public domain. Astounding Stories. (2009). ASTOUNDING STORIES OF SUPER-SCIENCE, MARCH 1932. USA. Project Gutenberg. Updated JAN 5 2021, from https://www.gutenberg.org/cache/epub/29310/pg29310-images.html
This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org, located at https://www.gutenberg.org/policy/license.html.
:::
\
2026-02-07 18:59:59
CutePetPal is an early-stage pet care app that earned a 23 Proof of Usefulness score by centralizing pet health data, reminders, and emergency info to reduce owner overhead
2026-02-07 18:00:04
\ If you follow the discourse surrounding AI security today, you'd be forgiven for thinking we're standing on the edge of an existential cliff. The headlines and security warnings are relentless, painting a picture of an immediate and unprecedented threat landscape.
Every week brings a new, dire headline that seems to amplify the sense of impending crisis:
This often refers to research demonstrating how autonomous AI systems, designed to perform helpful tasks, can be manipulated via subtle inputs known as adversarial examples or jailbreaking techniques to perform malicious actions, bypass safety guardrails, or even launch sophisticated attacks against underlying infrastructure. The core worry here is the loss of control over powerful, autonomous agents.
This covers the dual threat posed by Large Language Models (LLMs). The primary concern is that LLMs can lower the bar for entry into cybercrime by helping novices draft highly convincing phishing emails, generate exploit code, or perform reconnaissance. Secondly, there is a concern that attackers can use LLMs as a component in their attacks, leveraging them to automate complex, adaptive social engineering campaigns or to dynamically discover and craft zero-day payloads.
This analogy highlights the new class of vulnerability specific to AI systems. Just as SQL injection exploits a failure to sanitize user input destined for a database, prompt injection exploits a failure to properly isolate a user's instructions (the 'prompt') from the model's internal operating instructions. This allows an attacker to seize control of the model's behavior, leading to data exfiltration, unauthorized actions, or policy violation.
The cumulative implication of this constant stream of negative news and fear-driven analysis is often the same: AI itself is the problem. The narrative tends to focus on the inherent instability, unpredictability, and risk associated with the core technology, leading to the belief that the technology must be contained, strictly regulated, or fundamentally redesigned to be safe. It frames AI as an inherently insecure and destabilizing force.
As someone who has spent years analyzing real-world attacker behavior, this framing has always felt incomplete. Most claims rely on hypotheticals, toy examples, or intentionally unsafe demonstrations. Very few ask a simpler question: What attack surface is actually exposed by today’s real AI integrations? So, instead of adding another opinion, I decided to measure it.
The rise of sophisticated language models has necessitated the development of robust and standardized mechanisms for interaction with external resources. To this end, the Model Context Protocol (MCP) has been introduced, establishing a crucial, standardized framework that enables language models to securely and predictably invoke external tools and services.
This protocol acts as a secure intermediary, allowing the language model to extend its capabilities beyond its internal knowledge and computational boundaries. In essence, the MCP is vital for transforming language models from sophisticated text processors into capable and interactive agents that can perform real-world tasks by securely and contextually integrating with the broader digital ecosystem. MCP is often cited as evidence that “AI systems are dangerous,” yet it is also concrete, open source, widely replicated, and measurable. That makes it an ideal test case for separating facts from fear.
This analysis deliberately avoided speculative or adversarial setups. The study's design employed a conservative methodology involving five key steps:
Only MCP servers that actually ran and exposed tools were included, as an attack surface that doesn’t execute is not an attack surface.
\
Once runnable servers were isolated, a clear pattern emerged: MCP servers expose familiar security primitives, not exotic AI-only capabilities.
| Capability Class | Description | |----|----| | Filesystem access | Read/write operations on files or documents | | HTTP requests | Fetching or scraping remote URLs | | Database queries | Structured data retrieval | | Process execution | Local command or script execution | | Orchestration | Tool chaining, planning, or agent control | | Read-only search | Constrained data or API lookups |
The key point here is that these primitives already exist throughout modern software systems. MCP simply exposes them in a standardized way.
\
Despite widespread claims that AI tooling routinely enables dangerous behavior, the observed distribution was far more restrained.
| Capability | Relative Frequency | |----|----| | Filesystem access | Medium | | HTTP requests | Medium | | Database queries | Low | | Process execution | Rare | | Orchestration | Occasional |
Observation: \n The initial findings suggest a significant divergence from the prevailing, often alarmist, narratives concerning the inherent insecurity of advanced AI systems. Specifically, research into the practical exploitation of these systems revealed that arbitrary command execution, a capability that could lead to catastrophic system compromise or data breach, was identified as theleast common high-impact security vulnerability.
This counter-intuitive result directly challenges the widespread assertion that current AI models are fundamentally "reckless" or prone to easily exploitable, high-severity flaws that grant attackers deep control over the underlying infrastructure. While other vulnerabilities undoubtedly exist, the low frequency of command execution flaws implies that the robust input sanitization, sandboxing, and architectural defenses in place may be more effective than commonly acknowledged. It urges a more nuanced discussion in the cybersecurity community, shifting the focus from generalized panic to a targeted understanding of the actual, statistically significant risks posed by modern AI deployments.
The data suggests that security efforts might be more effectively spent addressing common, lower-impact vulnerabilities that occur with higher frequency, rather than focusing predominantly on the rarest, albeit most sensational, potential exploit.
Individually, many servers appear harmless. Risk increases when multiple primitives coexist in the same server, especially when orchestration is present. The security risk posed by an individual MCP server is often minimal or appears innocuous when viewed in isolation. A single, narrowly-scoped MCP might perform a necessary function, such as managing a simple configuration change or performing a basic logging operation. However, the potential for exploitation escalates dramatically when multiple servers are deployed and co-exist within the same server or operational environment.
This compounding risk is rooted in the combinatorial possibilities that arise when distinct primitives can be chained or leveraged in sequence. For example, one MCP server might be capable of reading sensitive configuration data, while another might be capable of altering firewall rules, and a third might be able to restart a critical service. Individually, these actions are limited. Together, they form a potent attack vector, enabling an attacker to escalate privileges, achieve persistence, and exfiltrate data.
The true inflection point for this risk is the introduction of orchestration. Orchestration, whether provided by automated security tools, container platforms (like Kubernetes), cloud-native frameworks, or sophisticated scripting, is designed to efficiently coordinate and manage these distributed capabilities. While intended for benign purposes, such as scaling, self-healing, and automated deployment, it simultaneously provides the ideal mechanism for an adversary.
An attacker who gains control over the orchestration layer can effectively direct a symphony of malicious actions, turning a collection of harmless primitives into a powerful, automated weapon system. This transforms a localized threat into a systemic one, where the environment's own automation is used against it to achieve complex, multi-stage attacks at speed and scale.
| Composition | Security Implication | |----|----| | HTTP fetch + filesystem write | Content injection / persistence | | Database query + orchestration | Data exfiltration pipelines | | HTTP fetch + planning | Prompt-injection amplification | | Filesystem write + orchestration | Supply-chain style poisoning |
MCP isn't inventing these chains. It's just making it far easier to put them together.
A recurring theme in AI security discussions, particularly in the realm of LLMs, is the vulnerability to prompt manipulation, often sensationalized as "prompt injection." This focus, while understandable given its directness, often overshadows more systemic and impactful security flaws. In practice, our research and the broader industry's experience have consistently shown that schemas matter far more than simple prompt-based attacks.
The schema in this context refers to the entire operational framework: the architecture, the underlying data models, the access controls, the orchestration logic, and the pre- and post-processing steps that wrap around the core LLM. A sophisticated attacker will not focus on coaxing a slight deviation from the model's output via a clever prompt. Instead, they will exploit weaknesses in a way the AI system interacts with external data sources, executes code, or leverages integrated tools.
For instance, an insecure schema might:
In essence, while prompt injection proves that the LLM is a complex, non-deterministic input validator, the severe, business-critical risks are rooted in the brittle scaffolding around the model, the schema. Securing AI systems requires shifting the security focus from merely defensive prompting techniques to adopting sound, installing traditional software, apply security principles across the entire AI application stack, and treating the LLM as just one component of a larger, interconnected, and potentially vulnerable system.
| Schema Pattern | Risk Implication | |----|----| | url: string | High (arbitrary network access) | | path: string | High (potential traversal / overwrite) | | query: string | Medium (depends on backend) | | enum(values) | Low (constrained intent) | | Hard-coded parameters | Minimal |
In MCP servers, the schema is the trust boundary. If a tool accepts unconstrained input and passes it to a powerful sink, the risk is architectural, not AI-driven.
The widespread discussion proclaiming that AI introduces an "unprecedented risk" often misses the more nuanced and architecturally significant reality. The most critical takeaway from current security research and incident analysis is not that AI represents a fundamentally new or unmanageable level of danger. Instead, the profound consequence of integrating AI, particularly in the form of LLMs and intelligent agents, is that AI fundamentally shifts the location where essential security decisions are made and enforced.
\ This is not a mere incremental update to existing security models; it represents a deep architectural shift in how applications are constructed and defended. Security focus is being moved:
| FROM: The Traditional Application Security Focus | TO: The Modern AI-Driven System Security Focus | |----|----| | UI Flows (User Interface) | Schemas (Data and Interaction) | | Security was heavily focused on validating inputs and protecting against manipulation of front-end components and presentation layers. | The emphasis moves to strictly defining and validating the structure and content of data exchanged between models, tools, and the application, ensuring input and output types are rigorously adhered to. | | Human Intent and Manual Vetting | Tool Composition and Orchestration | | Logic was often designed around what a human intended to do, relying on role-based access control (RBAC) and explicit permissions tied to individual actions or user profiles. | Security now centers on how the AI agent selects, chains, and uses various external tools (e.g., APIs, databases, services). The integrity and safety of the entire sequence of actions must be guaranteed, focusing on preventing prompt injection from driving unintended tool use. | | Application Logic (Hard-Coded Business Rules) | Execution Context Isolation (Sandboxing) | | Security resided within monolithic or tightly coupled business logic, where rules were explicitly coded and easy to audit within the application's source code. | The focus shifts to isolating the AI model's runtime environment, ensuring that the consequences of any erroneous or malicious model output (e.g., code generation or external calls) are strictly contained and cannot impact critical infrastructure or bypass perimeter defenses. |
This displacement of the security locus is an architectural shift, not merely a reflection of failure in existing security practices. It demands that organizations adapt their defensive strategies, security tooling, and developer training to address these new points of control. Recognizing that the primary security boundary is no longer the application’s front door, but the configuration and management of the intelligence pipeline itself.
The pervasive discourse surrounding AI security is often dominated by fear-mongering and hypothetical worst-case scenarios. However, for practitioners, improving the state of AI security requires a shift in perspective: we must replace fear with measurement and pragmatic discipline.
LLMs and other complex AI/machine-controlled processing systems are not inherently insecure; they are simply capability-rich systems. This abundance of functionality means they require the same rigorous, disciplined approach to threat modeling, secure configuration, and operational oversight that we already apply to other critical infrastructure like automation pipelines and cloud APIs.
The uncomfortable but ultimately empowering truth is that a significant number of so-called AI "risks" are, in fact, old security problems wearing new language. Concepts like insecure defaults, supply chain vulnerability, injection flaws, and misconfigured access controls have been fundamental concerns for decades.
And that is excellent news.
It is good news because it means that we are not starting from scratch. We already know how to solve these problems. The established security principles, frameworks, and methodologies, such as the Principle of Least Privilege (PoLP) and defense-in-depth architecture, remain perfectly valid. The challenge is not inventing a new science, but systematically adapting existing security expertise to the new attack surfaces and system characteristics introduced by AI.
By framing AI security as an extension of established enterprise security rather than an entirely new, insurmountable problem, organizations can move from paralyzing anxiety to effective, measurable risk reduction.
\n \n \n
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2026-02-07 17:59:59
Black Market SSP is a real-time currency and petroleum tracking app for South Sudan that earned a 60 Proof of Usefulness score for delivering practical financial data to everyday users.
2026-02-07 15:40:20
A typical software engineer deals daily with threads, processes, synchronization, race conditions, context sharing etc. A typical frontend engineer does not, but to build modern scalable interactive apps, one should.
The DOM is single threaded (still is, and might be forever). But we want to do more with it. Here are some cases where a single thread starts becoming a bottleneck:
Multi threading on the web can be classified into four broad categories:
This is the traditional Web worker model. Compute on the client can be distributed to multiple Web Workers.
Press enter or click to view the image in full size 
Implementations:
| ❤️ | 😭 | |----|----| | Supported by the platform OOTB | Limited to compute, no access to DOM | | Worker threads are lightweight | API is a bit clunky in some cases | | Workers can make HTTP calls | Transferring data between workers can be expensive due to serialization, cannot transfer functions. |
\ Bonus: SharedArrayBuffer and Atomics
The Web Worker message-passing model has a fundamental limitation: data must be copied or transferred between threads. For large datasets this serialization overhead can negate the benefits of offloading work.
SharedArrayBuffer solves this by allowing multiple threads to read and write to the same memory region. Combined with Atomics for synchronization, you get primitives similar to threads in C++ or Java.
| ❤️ | 😭 | |----|----| | Zero-copy data sharing between threads | Only works with typed arrays, not arbitrary objects | | Significant performance gains for large datasets | Requires COOP/COEP headers, breaks embedding scenarios | | Enables true shared-memory parallelism | Still no access to DOM |
\
Work is rescheduled as per priority, giving a sense of a responsive application. Still uses a single thread.
Implementations:
\
| ❤️ | 😭 | |----|----| | Simple to use if you already use the latest React versions | No benefit to the initial render performance | | | Dependency on React as a framework for everything | | | Single threaded, so low end CPU devices do not benefit | | | Repriotizes existing work, the strategy will fail when there is just more work to do like data visualizations |
\
Press enter or click to view image in full size 
Implementations:
\
| ❤️ | 😭 | |----|----| | Fast initial render performance, as DOM can be precreated on the server | Hydration on the client might be complex/expensive | | | No perf benefits beyond the first render | | | Need to maintain a server side DOM implementation | | | Not all features are supported in server side rendering |
\
With the new Offscreen canvas API (widely available since March 2023), you can create and control a canvas from a Worker. This brings us within striking distance from our goal, true multi-threaded rendering.
Implementations:
| ❤️ | 😭 | |----|----| | Create and Mutate visual elements from a Worker thread | Canvas is a very low level API, need to use an abstraction layer | | Simple API | Not too many feature rich Canvas libraries exist vs SVG/HTML rendering (React, D3, Highcharts etc) | | WebGL/WebGPU support | Canvas is not responsive, need to redraw when resized | | | Need to handle DOM events from the Main thread, as workers do not have DOM access | | | Canvas is stateless, so state updates/interactivity requires full redraw vs surgical updates |
\
The DOM is both created and mutated by separate workers. There are two approaches which make this possible, and we will talk about the current implementations for each.
Web Worker w/ DOM
Worker DOM: The DOM API in a web worker
Worker DOM library implemented DOM within web workers, all the mutations are done within the worker, and then periodically synced with the Main DOM. Checkout these slides for more details on how this works under the hood.
| ❤️ | 😭 | |----|----| | Performance benefits both for the first render and subsequent mutations | The complexity of maintaining a parallel DOM implementation, which will lag behind the browser's implementation | | Uses the familiar WebWorker API | Some APIs need a workaround to work. Some APIs cannot be supported |
\ \ Parallel DOM via cross-origin SubFrames
PDom: Multiprocess DOM via cross-origin Iframes
With the release of performance isolation in Chrome 88, it’s now possible to have multiple subframes on a webpage, which might be running in a separate process. The PDoM library tries to exploit this capability by providing an ergonomic abstraction for web developers to use.
| ❤️ | 😭 | |----|----| | Uses the web platform, with a thin abstraction layer. No new DOM implementation. | Need to set up a separate web server with specialized DNS config | | All DOM APIs are supported; no need to change the code | Only supported in Chromium-based browsers (Chrome/Edge) as of today. | | First-class support to parallelize any React component | |
\
What we didn’t talk about today is that you could also use the above techniques in combination with one another. For eg, you could use the “Compute only worker threads” with “Parallelized create only” to achieve performance benefits beyond just the initial render.
2026-02-07 15:27:56
The fusion of AI integration for blockchain with decentralized systems is rapidly moving from academic theory to real business impact. In the last week, Ethereum introduced a new AI agent economy standard designed to make intelligent code interact more efficiently with smart contracts. Market data suggests that the global AI agents market in the year 2025 is estimated at $7.84 billion and is expected to reach around $53.62 billion by 2030, growing at a CAGR of 46.3%, proving the market demand for agent-based automation across industries.
For enterprises looking to introduce decentralized innovation, tapping a generative AI development company or investing in professional AI consulting services could unlock future practical applications for anything beyond a pilot experiment. In this blog, we will focus on real-world applications of AI agents for Web3 and architectural choices augmenting these claims to viability in production.
\
Web3 platforms are undergoing a struggle not for lack of innovation, but in dealing with operational complexity, slow decision-making, and minimal automation. To tackle these challenges, AI agents are introduced into decentralized workflows.
Reducing Operational Complexity in Web3 Systems
Managing decentralized applications usually involves coordination between multiple protocols, data sources, and governance rules. AI integration for blockchain simplifies this by enabling agents to:
Managing decentralized applications need to interact with multiple protocols, data sources, and governance rules. The integration of AI technology in blockchain simplifies the operational activities by enabling the following agents to:
This reduces manual oversight while improving system reliability.
In addition, such a technique can reduce the manual supervision over on-chain conditions and improve system reliability.
Enabling Faster and Smarter Decision-Making
Traditional Web3 governance and execution models are reactive. AI agents help transform decision-making into proactive analyses since agents can:
It allows decentralized platforms to be more agile yet maintain control.
Enhanced Security, Risk Management, and Compliance
The primary challenges in adopting Web3 are security and regulatory compliance. AI agents can lend assistance by:
When guided by strong AI consulting services, these agents function like state-of-the-art guards within clear boundaries of mitigating technical and regulatory risks.
Scaling Web3 Platforms Without Centralized Control
As Web3 platforms grow, manual intervention does not scale. AI agents provide:
During the design and development phase of the decentralized AI system, collaboration with a generative AI development company becomes critical.
Real Business Use Cases of AI Agents in Web3
AI agents for Web3 are beyond the experimentation stage. They are being employed in production environments for automation, managing risk, and supporting optimal decisions. Use cases illustrate the effect of AI integration for blockchain to deliver clear business outcomes when it is supported by the right architecture and AI consulting services.
DeFi Risk Monitoring, Yield Optimization, and Capital Efficiency
AI applications in Web3 are exemplified by high-speed and volatility in the decentralized financing area. The applications of AI software agents include:
This would allow DeFi platforms to shift from reactive mitigants toward a more active risk management approach with capital efficiency.
DAO Governance and Decision Intelligence
Voter fatigue and limited data context are significant challenges in DAO governance. AI agents for Web3 improve governance by:
AI agents do not completely replace public participation but provide AI agents support for swifter decision-making.
RWA Tokenization and Asset Lifecycle Management
Real-world asset platforms can never be entirely handled with standard smart contracts for reasons of the basic complexity that they bring to their work. AI agents make scalable automation by:
For RWA platforms, AI integration for blockchain is paramount to achieve regulation-ready operations.
Web3 Exchanges, Compliance, and Market Surveillance
Indeed, intensified regulatory rules compel exchanges to fortify their cybersecurity and regulatory compliance efforts. AI solutions assist in this regard by:
This is where the professional AI consulting services ensure auditable and compliant automation.
Web3 Infrastructure and Operations Automation
AI agents are also transforming Web3 infrastructure, beyond financial use cases. The AI agents intervene in:
All these capabilities have great potential to save on a lot of manual effort and thus improve platform reliability at scale.
\
Across these use cases, the AI model does not matter. It is all about the architecture. A capable generative AI development company focuses on the following:
When properly designed, AI agents become a reliable execution layer rather than a new source of risk.
\
Building AI agents is not just about deploying a cumbersome model and tying it to a smart contract; that's grossly inadequate for any upfront system. A production system must be designed with more balanced intelligence, decentralization, security, and compliance. This is where strong AI integration for blockchain becomes essential.
Data Layer: On-Chain and Off-Chain Intelligence
AI agents require continuous information from a variety of data sources to function. The data includes:
A properly designed data layer should ensure data accuracy, immediate access, and have a safeguard against any kind of tampering.
Intelligence Layer: Decision and Reasoning Engines
In the intelligence layer, AI agents gather data to make decisions. The job primarily entails:
The different components of this layer must tightly couple with business goals and the best AI consulting services.
Execution Layer: Smart Contracts and System Controls
AI agents do not directly act on the blockchain but through different controlled execution paths, such as;
This separation allows for bounded and auditable autonomy.
Governance and Oversight Layer
In real-world scenarios, AI agents are much safer if they have governance mechanisms:
Any capable generative AI development company integrates these controls directly into the system at its inception.
Without the right architecture, AI agents tend to instigate more risk than value. With the right design, they grow and become a reliable execution layer for scalable, compliant Web3 platforms.
Developing AI agents for Web3 involves less training on advanced models and requires concentrating more on clear forward-leaning design and implementation choices. This results in the prompt deployment of AI agents and at the same time, minimizes the risks.
Start With a Clear Business Problem
AI agents should attempt to solve specific problems in the operational context and not exist as arbitrary tools. From a risk management framework or compliance automation to its rightful focus, radical clarity at the start ensures that AI integration for blockchain delivers measurable value.
Keep Autonomy Bounded
Allowing AI agents with an unlimited regulatory framework is a common mistake. The most effective systems set clear delineation lines by which the agents can execute low-risk decisions without human intervention, then escalate high-risk options to human oversight.
Design for Transparency from the Start
The degree to which AI may be explained is a critical issue in the Web3 context. One way is to keep detailed records of the decisions and actions of agents to enable auditing and insight for stakeholders. This builds trust and simplifies compliance.
Align AI With Governance and Compliance
The AI must support the very same rules that belong to the platform it is federated with. Solid AI consulting services are useful as they could enable the alignment of agent behavior with governance models, regulatory needs, and internal policies.
Choose the Right Development Partner
Choosing an experienced generative AI development company enables the positioning of AI agents on security, scalability, and decentralization rather than retrofitted later.
Implementing these best practices is the key in transforming businesses from experimenting with AI agents in a Web3 world into having highly reliable production entities.
AI agents for Web3 have moved far beyond experimentation to serve as an execution layer, practically inherent to decentralized platforms. This blog shows, real impact of AI integration for blockchain to solve logical, immediate risk management inefficiencies, governance efficiency, compliance and operational scalability. The value does not lie in the models alone but in clever architecture, bounded autonomy, and strong oversight. For enterprises and Web3 builders, success depends on reorienting technology to business goals by well-defined AI consulting services. By collaborating with an expert generative AI development company, one would ensure the top quality of these systems for security, transparency, and operability from the very beginning. As the ecosystem of Web3 expands and matures, AI agents are likely to add immense value to decentralized systems in making them substantial and operational at scale.
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