Hyperliquid Founder's Circle of Friends Exposed: This Group of 'Olympic Gold Medalists' is Dominating Silicon Valley's AI Scene

marsbitPublished on 2026-04-16Last updated on 2026-04-16

Abstract

This article profiles a group of former Olympiad medalists, now prominent AI entrepreneurs, who first connected through a Hudson River Trading (HRT) internship and high school competitions. The circle includes Jeff Yan (founder of Hyperliquid), Alexandr Wang (head of Meta's AI division after selling Scale AI), Scott Wu (CEO of Cognition, creator of AI engineer Devin), Johnny Ho (CSO of Perplexity), Jesse Zhang (CEO of Decagon), Demi Guo (CEO of Pika), and Steven Hao (CTO of Cognition). Their shared background in elite math and programming contests forged a powerful network. Now, they are leading some of Silicon Valley's most valuable AI companies, with several ventures achieving multi-billion dollar valuations. The piece draws a parallel to the "PayPal Mafia," highlighting their collective focus on engineering excellence, efficiency, and a shared vision for shaping the future with AI.

Author: Azuma, Odaily Planet Daily

Yesterday, a feature interview with Hyperliquid founder Jeff Yan in "Colossos" magazine went viral across the internet (Related reading: 11 People, Zero Funding, Earning $900 Million Annually: The Crazy Life of Hyperliquid Founder Jeff Yan).

In the interview, Jeff Yan disclosed a little-known story — during his junior year at Harvard, he participated in the inaugural internship program at the quantitative trading giant "Hudson River Trading" (HRT). Ten interns were selected that year. Apart from Jeff Yan, who chose the cryptocurrency track, several others from that intern class have now become prominent figures in the AI field, including Meta's AI business line head Alexandr Wang, Decagon founder and CEO Jesse Zhang, and Cognition founder and CEO Scott Wu.

Odaily Note: The intern group photo shared by Jesse Zhang

According to additional disclosures by Scott Wu himself, the HRT internship was not the starting point of their friendship. As early as high school, many of them had met through Olympic competitions (Jeff Yan, Scott Wu, and several others had won gold medals), and that small circle included many more notable names, including but not limited to Perplexity co-founder and CSO Johnny Ho, Pika co-founder and CEO Demi Guo, and Steven Hao, Alexandr Wang's former partner in founding Scale AI......

During the formative years of Jeff Yan, Scott Wu, and others, the "PayPal Mafia" represented by Elon Musk and Peter Thiel was already dominating the business world, and people began searching for the next similar special network. Jeff Yan's small circle had also discussed this topic. The 19-year-old Alexandr Wang once said to his friends: "Why can't it be us?"

Ten years later, Alexandr Wang's bold statement seems to be coming true. Leveraging AI, this group of young people from the Hudson River is stirring up the era in their own way.

Alexandr Wang: Zuckerberg's AI Brain

Alexandr Wang is perhaps the most well-known figure in this small circle. Born in 1997 in Los Alamos, New Mexico, Alexandr Wang is a descendant of Chinese immigrants. His parents were physicists at the Los Alamos National Laboratory — where the United States' first atomic bomb was secretly developed during World War II.

Alexandr Wang was passionate about mathematics and programming from a young age. He qualified for the Mathematical Olympiad Program in 2013, the US Physics Team in 2014, and reached the finals of the US Computer Science Olympiad in 2012 and 2013.

In 2015, Alexandr Wang dropped out of the Massachusetts Institute of Technology and founded Scale AI the following year, which annotates data used for training AI in computer vision and audio transcription. Riding the wave of the AI boom, Scale AI's valuation soared, reaching $7.3 billion by 2021. With a 15% stake, Alexandr Wang's net worth surpassed the $1 billion mark.

In June 2025, Meta, which had clearly fallen behind in the AI race, acquired a 49% stake in Scale AI for $14.3 billion. Zuckerberg's附加条件 was that — the soul of Scale AI, the then 28-year-old Alexandr Wang, must join Meta. Alexandr Wang subsequently joined Meta and began leading Meta's AI development team, the "Meta Superintelligence Labs" (MSL).

On the night of April 8th, Zuckerberg's big gamble paid off. MSL officially released its first self-developed AI model, Muse Spark. Muse Spark is a native multimodal reasoning model supporting tool calling, visual chain-of-thought, and multi-Agent orchestration. It is the most powerful model Meta has released to date. During training, MSL observed predictable scaling improvements in the model during pre-training, reinforcement learning, and test-time reasoning stages.

Scott Wu: Olympiad Prodigy, Creator of a $10 Billion AI Unicorn

Scott Wu was born in 1997 in Louisiana to a family of Chinese immigrants. Growing up, Scott Wu actively participated in programming and mathematics competitions, winning three gold medals in the International Olympiad in Informatics (IOI), including first place in 2014.

After high school, Scott Wu attended Harvard University but dropped out after two years. During his undergraduate studies at Harvard College, he was a member of the university team that participated in the 2016 International Collegiate Programming Contest (ICPC), winning a gold medal and achieving an overall third-place ranking.

In 2019, Scott Wu co-founded the social platform Lunchclub as its CTO. In 2023, Scott Wu, along with friends Steven Hao and Walden Yan (both Olympic gold medalists), co-founded Cognition, where he serves as CEO.

In 2024, the Cognition team launched the world's first autonomous AI software engineer, Devin. This product can independently complete code writing, testing, and deployment, and supports the decomposition and collaboration of complex tasks. It significantly outperformed GPT-4 in the SWE-bench benchmark test. In May of the same year, Cognition secured $175 million in funding led by Peter Thiel's Founders Fund, valuing the company at $2 billion post-money. In September 2025, Cognition raised another $400 million, skyrocketing its valuation to $10.2 billion.

By early 2026, Cognition's annualized revenue had reached $400 million.

Johnny Ho: Net Worth $2.1 Billion, Once Considered Acquiring TikTok and Chrome

Like Scott Wu, Harvard graduate Johnny Ho won three gold medals in the International Olympiad in Informatics (IOI), achieving a perfect score and first place in 2012.

In August 2022, Johnny Ho co-founded Perplexity with Aravind Srinivas, Andy Konwinski, and Denis Yarats. Perplexity is positioned as an AI search engine company, providing a search service with conversational answers that display citation sources and offer related question suggestions.

In 2023, Perplexity's monthly visits reached 10 million; by April 2024, its monthly active users had reached approximately 15 million. That same year, Perplexity embarked on a疯狂融资 spree. In its fourth funding round at the end of the year, it raised $5 [Note: Likely a typo, probably meant $500 million or similar, but translated as written], reaching a valuation of $9 billion; In July 2025, Perplexity completed another $100 million in new funding, raising its valuation to $18 billion.

It is worth mentioning that Perplexity has initiated several bold "minnow-swallowing-whale" level acquisition proposals (with VCs willing to provide capital), including an offer to acquire TikTok in early 2025, proposing to merge Perplexity, TikTok's US operations, and new capital partners into a new entity, and in August 2025, proposing to Google to acquire its core product, the Chrome browser, for $34.5 billion.

According to the latest data from Forbes, Perplexity's current valuation is as high as $20 billion, and Johnny Ho's personal wealth has reached $2.1 billion.

Jesse Zhang: AI Startup Valued at $4.5 Billion in Three Years

Jesse Zhang was also born in 1997 and grew up in the San Francisco Bay Area. From high school, Jesse Zhang was a typical "competition fanatic" — selected twice for the Mathematical Olympiad Program (MOP), a finalist in the Intel STS, and participated in MIT's RSI research program. After entering Harvard, Jesse Zhang completed the four-year university curriculum in just three years.

In 2018, Jesse Zhang co-founded the game highlight sharing platform Lowkey with friends. The project received seed funding from Y Combinator and Series A funding from a16z. In 2021, Lowkey was acquired by Pokémon GO developer Niantic for an undisclosed amount.

In 2023, Jesse Zhang co-founded Decagon with partner Ashwin Sreenivas, focusing on automating enterprise customer service using AI Agents to solve the problems of high labor costs and low efficiency in call centers.

In June 2024, shortly after its founding, Decagon quickly raised $35 million in funding, including a $5 million seed round led by a16z and a $30 million Series A led by Accel; Four months later, Decagon raised $65 million in a Series B round; In June 2025, a Series C round raised $131 million, pushing the valuation to $1.5 billion; In January 2026, a Series D round raised $250 million, skyrocketing the valuation to $4.5 billion... In line with the rising valuation was Decagon's revenue capability. By the end of 2025, the company disclosed an annual revenue capability exceeding $30 million.

Demi Guo: Hangzhou '95er, Pioneer in AI Video Generation

Demi Guo was born in 1999 in Hangzhou, China, and moved to Silicon Valley with her family during childhood.

Demi Guo won a silver medal at the 2015 International Olympiad in Informatics. She graduated from Harvard University with a bachelor's degree in mathematics and a master's degree in computer science, later dropping out of a Stanford PhD program to focus on entrepreneurship in generative AI video content creation.

In April 2023, Demi Guo co-founded Pika with Chenlin Meng, with Demi Guo serving as CEO. Pika focuses on developing video generation AI technology. Its core products include the Pika 1.0 and Pika 2.0 models, which generate 3D animation, anime, cartoon, and cinematic styles, offering features like video extension, canvas expansion, and element replacement.

Regarding funding, Pika completed a $20 million seed round before its official launch; It then completed a $35 million Series A round in November 2023, led by Lightspeed Venture Partners; In June 2024, Pika completed an $80 million Series B round at a $470 million valuation, led by Spark Capital, with participation from Greycroft, Lightspeed Venture Partners, and actor Jared Leto, among others.

Steven Hao: AI Tech Guru with a Net Worth Over $1 Billion

Steven Hao, a graduate of MIT's mathematics department, also won a gold medal in the International Olympiad in Informatics (IOI). He was a partner of Alexandr Wang at Scale AI and has now joined Scott Wu's Cognition as CTO. Both companies have been详细介绍 earlier, so we won't elaborate further here.

Forbes data shows that the 30-year-old Steven Hao's personal wealth is estimated to have reached $1.3 billion.

Epilogue: We Might Be Witnessing a New Legend

I thought about giving this small circle a new name similar to the "PayPal Mafia," like calling them the "Hudson River Mafia," or the broader "Olympiad Mafia"... Although the era and story trajectory are completely different, they seem to share the same core spirit as the previous generation's "PayPal Mafia" — behind the camaraderie of high-level competition, what truly connects them is a shared pursuit of intellectual density, engineering efficiency, and system re-architecting capabilities, along with a profound judgment about where the future begins.

A new generation of entrepreneurs has stepped onto the stage. Before them lies the question, "How will AI reshape the world?" — a problem far more difficult than any Olympiad. This is their battlefield and their stage.

Related Questions

QWho are the key members of the 'Olympiad gold medalist' group mentioned in the article, and what are their notable achievements in the AI industry?

AThe key members include Jeff Yan (founder of Hyperliquid), Alexandr Wang (head of Meta's AI division, founder of Scale AI), Scott Wu (CEO of Cognition, creator of Devin AI), Johnny Ho (co-founder of Perplexity, valuation $20B), Jesse Zhang (CEO of Decagon, valuation $4.5B), Demi Guo (CEO of Pika, AI video generation pioneer), and Steven Hao (CTO of Cognition, ex-Scale AI). Their achievements range from leading billion-dollar AI companies to developing groundbreaking AI models and products.

QWhat was the significance of the Hudson River Trading (HRT) internship program in connecting these individuals?

AThe HRT internship program served as a pivotal networking point where these individuals first collaborated professionally. It included future AI leaders like Alexandr Wang, Scott Wu, and Jeff Yan, strengthening their existing relationships from high school Olympiad competitions and laying the foundation for their collective impact in Silicon Valley's AI sector.

QHow did Alexandr Wang transition from founding Scale AI to leading Meta's AI efforts?

AAlexandr Wang founded Scale AI, which grew to a $7.3B valuation by 2021. In 2025, Meta acquired 49% of Scale AI for $14.3B, with the condition that Wang join Meta to lead its AI development team, the Meta Superintelligence Labs (MSL). Under his leadership, MSL launched the advanced multimodal AI model Muse Spark in 2026.

QWhat are the core innovations or products developed by Cognition, Perplexity, and Pika as highlighted in the article?

ACognition created Devin, the first autonomous AI software engineer capable of coding, testing, and deploying tasks. Perplexity developed an AI-powered conversational search engine with citation sources. Pika focused on AI video generation tools, including models for 3D animation and style transfer, with features like video extension and element replacement.

QWhat common traits or backgrounds unite this group of AI leaders, according to the article?

AThey share a background in elite academic competitions (e.g., International Olympiads in math, computing, or physics), attendance at top universities like Harvard and MIT, early careers in quant trading or tech, and a focus on high-impact AI entrepreneurship. Their bond is rooted in intellectual density, engineering efficiency, and a shared vision for AI-driven transformation.

Related Reads

Has the 'Digital Gold' Narrative for BTC Failed?

**Title: Has the "Digital Gold" Narrative for Bitcoin Failed?** The article argues that Bitcoin's "digital gold" narrative remains valid despite a recent sharp price decline (from a peak near $126k in Oct 2025 to briefly under $61k in Feb 2026). It presents a long-term investment framework based on three core points: **1. Viewing Bitcoin as an Asset:** Bitcoin is presented as a superior potential store of value compared to gold. Key arguments are its absolute scarcity (21 million cap), superior portability, and transparent auditability via its public ledger. While acknowledging its current use in early, volatile stages (~3-4% global adoption), the author draws parallels to the early, disruptive phases of the internet and e-commerce. **2. Understanding the Recent Downturn:** The current ~50% correction is framed as a predictable, consensus-driven cycle following its post-halving peak (the 2024 halving preceded the Oct 2025 high). A crucial factor is a historic "changing of hands": the influx of new institutional buyers via ETFs allowed early, low-cost holders (miners, OG believers) to take profits. The author notes that while severe, Bitcoin's historical drawdowns (e.g., 93% in 2011, 77% in 2021-22) have been progressively smaller, suggesting maturing holder structure and decreasing volatility over time. **3. The Long-Term Perspective:** The long-term thesis hinges on Bitcoin capturing a portion of gold's market value. With Bitcoin's market cap at ~$1.4 trillion (at $70k) versus gold's ~$20 trillion, significant upside potential exists if the "digital gold" narrative is partially realized. However, the author strongly cautions that short-term risks remain, the bottom is unpredictable, and high volatility is inherent. The real risk is not Bitcoin failing but poor personal position management (over-leverage, wrong capital) and a lack of deep understanding, which can force investors out during severe downturns. The conclusion uses Amazon's 95% crash post-2000 dot-com bubble and subsequent 42x recovery as an analogy. The ultimate question is not if Bitcoin's price will rise, but if an investor's strategy and conviction can withstand the volatility to see the long-term play out. The recent divergence (gold up, Bitcoin down) is posed not as a narrative failure, but as potential evidence of this ongoing, painful transition from a speculative asset to a mainstream allocation.

marsbit1h ago

Has the 'Digital Gold' Narrative for BTC Failed?

marsbit1h ago

Has BTC's 'Digital Gold' Narrative Failed?

The article discusses Bitcoin's "digital gold" narrative, its recent price drop, and long-term outlook through the perspective of "Jason". It argues the narrative is not a failure but that Bitcoin represents a superior, new asset class due to its fixed supply (21 million), portability, and auditability. The piece compares its current ~3-4% global adoption rate to early internet/e-commerce, suggesting significant growth potential. Regarding the 2025-2026 price decline (from ~$126k to briefly under $61k), the author views it as a predictable, consensus-driven sell-off within Bitcoin's ~4-year cycle post-halving, exacerbated by a major "handover" from early, low-cost holders to new institutional buyers via ETFs. A key observation is that historical peak-to-trough drawdowns have lessened over time (e.g., 93% in 2011 to ~50% in 2026), indicating maturing volatility as holder structure changes. For the long term, the author uses a simple framework: Bitcoin's total market cap (~$1.4T at $70k) is only about 7% of gold's (~$20T). Even capturing 30-50% of gold's value would imply substantial upside. However, the article strongly cautions against viewing this as investment advice, emphasizing extreme volatility and the critical importance of risk management, position sizing, and deep fundamental understanding to survive severe drawdowns. It concludes by drawing a parallel to Amazon's 95% crash in 2000 and subsequent 42x recovery, stressing that the key is surviving market cycles to realize long-term potential.

链捕手1h ago

Has BTC's 'Digital Gold' Narrative Failed?

链捕手1h ago

From Code to Cognition: A Ten-Thousand-Word Guide to the Evolution of the Robot Brain

"From Code to Cognition: The Evolution of Robot Brains" The journey of robotic intelligence has shifted dramatically from manually coded systems to AI-driven brains. For decades, robots relied on layered software stacks—perception, state estimation, planning, control—each handcrafted. While predictable, they lacked adaptability. The 2010s saw deep learning revolutionize perception (e.g., object detection) and control (via reinforcement learning), but learned skills remained narrow. The arrival of Large Language Models (LLMs) marked a turning point. LLMs acted as high-level planners, interpreting natural language instructions and generating sequences of actions for traditional robotic systems to execute. However, true integration came with Visual-Language-Action (VLA) models, which fused vision, language, and motion prediction into a single network. Pioneered by models like RT-2 and open-source projects like OpenVLA, VLAs enable robots to reason and act directly from visual input and commands. The most advanced humanoid robots now employ a "dual-brain" architecture: a slow-thinking, large VLA (System 2) for reasoning and planning, and a fast-reacting, small network (System 1) for high-frequency motion control, sometimes with an even lower-level System 0 for balance. This split balances cognition with the physics of real-time movement. Computation is split between onboard hardware (e.g., NVIDIA Jetson) for safety-critical control loops and cloud/edge servers for non-critical tasks like learning and interfaces. A crucial driver is the open-source ecosystem—models like GR00T and OpenVLA allow startups to build upon pre-trained brains and fine-tune them with their own data, accelerating development. Despite progress, current systems struggle with recovery from errors, sample inefficiency, and long-horizon tasks. This has spurred the rise of **World Models**—neural networks that predict the consequences of actions. By simulating possible futures before acting (like NVIDIA Cosmos or Meta V-JEPA), robots can plan, recover, and generalize better. This represents the next frontier: shifting intelligence from learned reactions to an internal model of physics and cause-and-effect. The field is rapidly evolving. While not yet at its "ChatGPT moment," the convergence of cheaper hardware, scalable simulation, and world models points toward robots that are increasingly capable, adaptive, and useful. The question is shifting from "what can robots do?" to "what *should* they do?"

marsbit2h ago

From Code to Cognition: A Ten-Thousand-Word Guide to the Evolution of the Robot Brain

marsbit2h ago

AI Bubble Is Bursting

The AI Bubble is Bursting: A Necessary Purge on the Path to Ubiquitous Intelligence Market volatility has reignited debates about an AI bubble, with figures like Ray Dalio pointing to high valuations. However, this parallels the dot-com bubble, which, despite its crash, laid the physical infrastructure for today's internet era. The current AI investment frenzy, with tech giants planning trillions in infrastructure spending far outstripping current AI application revenues, appears similarly imbalanced. This 'bubble' is seen as an inevitable phase for a disruptive technology, paying the "innovation tax." Critically, AI inference costs have plummeted over 99.7% since 2023, making intelligence nearly free at the margin. This hasn't reduced spending but has instead unlocked massive new demand, as seen in enterprise AI cloud expenditure tripling. This follows the Jevons Paradox: efficiency gains lead to greater total consumption. The market is now entering a cleansing phase, weeding out speculative ventures lacking real moats. The deeper shift is a move from capital expenditure (CapEx) on hardware to value creation in operational expenditure (OpEx) through AI applications that solve real industry problems. While infrastructure valuations are high, rapid earnings growth from widespread AI adoption across sectors—from manufacturing and finance to law and healthcare—may digest these valuations over time. Ultimately, this creative destruction will leave behind robust infrastructure and optimized models, cheaply powering an AI-augmented future for all industries, much as the internet became indispensable after its own bubble burst. The core productive potential remains undiminished.

链捕手2h ago

AI Bubble Is Bursting

链捕手2h ago

Trading

Spot
Futures

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of S (S) are presented below.

活动图片