Internet Computer
Internet Computer: Blockchain or Layer 2 ecosystem resource for crypto users and developers.
ABAB Structured Brief
Internet Computer is indexed in ABAB Crypto Map under Blockchains & L2. This page keeps the official site, category, tags, and related ABAB coverage together as a searchable crypto project profile. Official domain: internetcomputer.org.
Related News & Analysis
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Interview with Brett Adcock, Serial Entrepreneur Worth $19 Billion: Commercialization Turning Point for Figure, New Lab HARK Creating a Revolution in AI Hardware and Computer Use
Podcast interview titled "$39B founder: “AI will eat the whole internet”" (My First Million podcast featuring Brett Adcock, founder of humanoid robot unicorn Figure, and former founder of Archer Aviation / Vettery). Here are the key points summarized: 1. Journey as a serial entrepreneur and the "Binary" business philosophy • From an Illinois farm to a $10 billion empire: • Founded and sold Vettery for over $100 million, took electric aircraft company Archer Aviation public; then invested personal assets, even mortgaged property to establish humanoid robot company Figure (valued at hundreds of billions), and incubated the campus gun prevention project Cover and the new generation AI lab HARK. • Binary Outcome: • No obsession with personal paper wealth (estimated by Fortune at about $19 billion); driven by extreme "conquest desire and ambition to reach the summit." • Firm belief that the outcome of robotics and hardcore technology is extremely binary—either it scales to change human productivity towards a trillion-dollar market value or it goes to zero, with no mediocre middle ground. 2. New lab HARK: Breaking API limitations to achieve true "AI Computer Use" • 99.9% of internet scenarios lack APIs: • Less than 1 in 1000 of the world's 1000 websites provide comprehensive Consumer APIs. Relying solely on APIs or MCP cannot build a true universal digital assistant (like ordering from DoorDash, complex ticketing, internal system operations). • HARK's technical path: Independently launching systems through cloud-based virtual sandboxes, using self-developed post-training reinforcement learning algorithms, allowing AI to control the entire internet like humans through "visual recognition of screens + moving the mouse + typing on the keyboard." • New AI-native hardware that "kills phones and computers": • Existing touch-screen-based iPhones and MacBooks were designed 20 years ago, not for the AI era. • HARK has assembled a team led by former core hardware design leaders from Apple, secretly developing the next generation of AI-native hardware, completely breaking free from the App Store framework, building all-weather multimodal interaction, edge-specific weights, and lifelong memory digital Jarvis. 3. Commercialization of Figure humanoid robots and general household bottlenecks • B2B industrial logistics scenarios are the first to close the loop (BMW case): • The automotive and logistics manufacturing sectors face over 100% turnover rates and labor exhaustion. Figure robots have completed real-world tests sorting single packages in 2.9 seconds and running continuously for over 200 hours, surpassing human workers in speed and ROI. • The real challenge of transitioning from industrial to household (General Robotics): • The difficulty lies not in manufacturing hardware but in the generalization of embodied intelligent brains in unstructured new environments. • For example, recognizing and folding clothes of different lighting, heights, and materials in homes never visited before. Figure is currently focused on tackling end-to-end large model generalization from "pixels to torques." 4. Hardcore entrepreneurial rules: Doing extremely difficult things is actually easier • Non-linear return logic: • Building a quadruped robotic dog may be one-third the difficulty of humanoid robots, but the commercial TAM for robotic dogs is very small; while humanoid robots, despite being 3-4 times harder, have a return rate that is in the millions. • Tackling the hardest hardcore problems naturally filters out 99% of mediocre competitors while attracting the world's top surplus talents and capital. • Technical interviews and talent identification: • 90% of engineers in the Bay Area cannot handle true hardcore breakthroughs. Personally conducting in-depth technical evaluations of core personnel to discern candidates as "practitioners who have experienced deep pits and bear scars" versus "theorists who have only observed others' achievements." 5. Personal workflow, energy allocation, and guidance from top mentors • The "three-drawer" rule of life: • Dividing life into "career, family, and social matters." To achieve A+ in career and family (with three children), he completely cut out all meaningless socializing, golf, and long-distance parties. • Inspiration from Jeff Bezos and Jensen Huang: • Bezos, as an investor in Figure, advised him: "You have completed the hardest part from 0 to 1; the next 1-2 years will be a life-and-death battle to truly scale the robots to mass production in the industrial sector, this is a decisive battle." • Firm belief that within the next 10-20 years, AI will consume the entire physical and digital world at a scale 100 times larger than the internet.
a16z Closed-Door Debate: From Existential Risk (X-Risk) Regulatory Traps, Internal Network Agent Protection to New Paradigms in Probabilistic Programming
1. Core Controversy: "Pacing" or Regulatory Capture? • The open letter from Anthropic founder Dario Amodei has sparked intense debate in the industry: • The frontier large model safety and sandbox isolation initiative proposed by Dario and others is reasonable at the pure technical engineering level; • However, the focus of the controversy lies in the narrative packaging of "pacing" and the so-called "existential risk (X-Risk)". • The logical flaw of the concept of "pacing": • Lack of a reference frame: There has never been a publicly agreed benchmark speed for technological evolution. Announcing "we have decided to slow down" when no one knows the original completion node is essentially similar to the media claiming "the unannounced Apple car has been delayed," which lacks measurable standards for a self-consistent narrative. • A middle ground that pleases neither side: Attempting to walk a compromise between the internal extreme pause faction (Pause/Doomer) and external regulatory bodies has resulted in radicals accusing it of "just slowing down instead of stopping," while regulators deem it as "acknowledging the existence of harm yet still racing ahead." • Reflecting on the moral coercion of "existential risk (X-Risk)": • Historically, nuclear weapons development teams were well aware of their absolute lethality on a physical level, thus establishing the highest level of national control mechanisms; • If the heads of large model laboratories genuinely believe there is a 10% probability of human extinction, the only ethical response would be a complete halt or total nationalization; • The reality is contradictory: If one side portrays extinction risk while continuing to accelerate financing and pushing into the commercial market, it can easily be exploited by politicians, leading to excessive regulatory capture that could strangle startups and the open-source ecosystem. 2. Historical Reflection: From Nuclear Bombs, Early Internet Viruses to Regulatory Lag • Prerequisites of the "Fact Pattern" in policy-making: • The automotive industry was already widespread in the early 20th century, and it wasn't until Ralph Nader published "Unsafe at Any Speed" in the 1960s that stringent safety regulations were formed; • The aviation industry experienced a 40-year period of technical trial and error from the Wright brothers' first flight to the establishment of a complete FAA airworthiness certification system; • Traditional regulation must be based on specific damages that have occurred and a clear causal chain; attempting to implement "predictive legislation" before technology has matured is almost certain to stifle innovation. • If we were to apply today's fear paradigm to the Internet of the 1990s, it would never have emerged: • In the era of Windows 95 and early PCs, connected computers faced widespread risks from worm viruses and network interruptions (for example, the Morris worm paralyzed 10% of backbone networks, leading to frequent disconnections and business losses); • At that time, Congress passed the Computer Fraud and Abuse Act (CFAA) targeting specific system intrusion cases; if the Internet's underlying infrastructure had been completely locked down in 1994 out of fear of hackers, the modern digital economy would not exist. • The potential spread risk of the European GDPR model: • Europe, lacking local underlying tech giants, tends to legislate aggressively in compliance and antitrust areas; • Beware of Europe refining regulation into a "digital airbag reminder" for AI Agents—where every time an Agent calls an external API or performs file read/write, a GDPR-like disclaimer confirmation box pops up, ultimately leading to widespread user cognitive numbness. 3. The Real New Security Front: Engineering Threats from the Emergence of Internal Network Agents • The complete failure of traditional internal network security assumptions: • Past IT and enterprise information security were based on an implicit assumption: 95%-99% of internal employees comply with regulations most of the time, with malicious insiders being a very low probability event. • Internal GitHub, Slack, approval flows, and financial reimbursement systems often lack strict concurrency throttling, relying solely on single sign-on (SSO) and coarse-grained identity authentication. • Agent swarms as "internal network distributed denial-of-service attacks (DDoS)": • When internal employees start to batch schedule thousands of autonomous Agents to write code, call APIs, and run automated tests, the software behavior is structurally identical to high-frequency DDoS attacks. • Agents possess rapid retry and tireless characteristics, easily exhausting internal microservice resources in a dead loop, and may even trigger dangerous data overwrites due to misunderstanding instructions. • Next-generation operating systems and permission granularity innovation: • The existing operating system permission system is too crude (either fully open or frequently prompting for confirmation); • There is an urgent need to reconstruct the underlying security stack to support fine-grained dynamic permission isolation (e.g., instantaneous read/write control for specific folders, adaptive API rate monitoring, and audit tracking), shifting the security focus from the metaphysical level of species survival back to concrete engineering defenses. 4. Paradigm Shift: The Rise of the Jev Model and the Return of Probabilistic Programming • The essential conflict between natural language interaction and traditional software systems: • The industry has previously focused on a "text input, text output" chat model, but forcing structured software to parse unstructured long text is extremely costly and error-prone; • Relying on complex prompt constraints and schema validation cannot guarantee 100% deterministic output and wastes enormous computational costs and response delays on meaningless question-and-answer redundancy. • The core breakthrough of the Jev model: from "generating text" to "semantic decision-making": • The input remains semantic context, but the output completely abandons generative text; • Among a given set of discrete options or routing conditions, it directly returns the optimal decision and precise probability distribution with extremely high throughput and low latency; • This completely overturns inefficient chat box interactions, allowing traditional software code to directly use large model outputs as the basis for logical branching decisions. • The revival of half a century of computer science legacy: • The core proposition of programming languages in the early 1960s-1970s was simulation and probabilistic modeling (e.g., handling ballistic trajectories, wind fluctuations, and other non-deterministic physical systems); • Modern software code has long been limited to Boolean algebra (absolute certainty of if/else); • With the implementation of Jev-like architectures, the core programming paradigm is shifting entirely to condition branches based on probabilistic confidence (if x% then ...), seamlessly integrating probabilistic language models with classical deterministic software engineering. 5. The Shift of Innovation Focus: From the Base Model Layer to the Model Periphery • The "critical mass dilemma" at the giant platform layer: • Leading large model laboratories are currently mired in maintaining large infrastructure operations, resolving alignment disputes, and managing massive user compatibility, severely dispersing their focus, making it difficult to optimize all vertical scenarios from the platform layer. • The autonomous evolution of application layers and peripheral tools: • The history of the software industry shows that core innovations often emerge outside the platform (analogous to the "Sherlocking" process where third-party independent tools are integrated into systems); • Large models are no longer merely revered as ultimate deities but are evolving into standard underlying components like databases and compilers; the core technological moat of future software is shifting entirely to how to reconstruct contextual systems, state persistence, and high-reliability business flows around the model's periphery.