Sharplink CEO: Ethereum's Future is Unfolding Now

marsbitPublished on 2026-05-31Last updated on 2026-05-31

Abstract

In an article titled "Sharplink CEO: Ethereum's Future is Unfolding," Joseph Chalom, a former BlackRock executive and current Sharplink CEO, argues that the current debates surrounding the Ethereum Foundation (EF) and ETH price miss the bigger picture. He asserts that Ethereum's long-term institutional adoption is secured by its foundational strengths: trust, security, and liquidity. Chalom highlights Ethereum's dominance in settling stablecoin value, tokenizing real-world assets (RWA), and facilitating high-value DeFi transactions as evidence of its winning position. He defends the Ethereum Foundation's focus on rigorous protocol development and a decade-long track record of major upgrades (The Merge, EIP-1559, Dencun, etc.), viewing its upcoming technical roadmap as the most ambitious in the industry. Contrary to critics, Chalom posits that Ethereum's decentralization and reliable neutrality are core strengths for institutional adoption, not weaknesses, as they prevent control by any single entity. Drawing a parallel to Amazon's early days, he suggests that ETH's intrinsic value is tied to the expansion of its network, which is poised for a step-change in transaction volume across stablecoins, RWAs, DeFi, and agentic finance. Chalom advocates for a "be greedy when others are fearful" approach, citing historical examples from Warren Buffett and his own experience at BlackRock during the crypto winter. He concludes that while the EF should remain focused on core protocol a...

Author: Joseph Chalom

Translation: Jiahuan, ChainCatcher

The current drama surrounding the Ethereum Foundation (EF) and the debate over the price of ETH are missing the bigger picture. I fully understand this debate, but it does not determine who will lead the financial infrastructure for the next decade.

This is the perspective of just one stakeholder. Before leading Sharplink, I spent two decades as a senior executive at BlackRock, responsible for fintech business and digital asset strategy.

That experience taught me what institutions genuinely value before committing capital to a new set of infrastructure.

I want to step back, avoid the noise, and offer a different assessment of where Ethereum is today and where it's headed.

The Ethereum Foundation is Doing Its Job

Take a step back and look at the outcomes delivered over the past decade. On the three attributes most critical for institutional adoption—trust, security, and liquidity—Ethereum has secured a winning hand. It is winning, and by a significant margin.

Look at the track record. The settlement of most stablecoin value globally occurs on Ethereum. Its scale of tokenized real-world assets (RWA) far exceeds any other blockchain, and it remains the default venue for high-value DeFi transactions.

No competing chain comes close on these dimensions.

This is no accident; it's the result of the Ethereum Foundation's years of rigorous protocol development. Ethereum is the only blockchain with a ten-year record of major upgrades at its base layer.

The Merge, EIP-1559, Dencun, Pectra, Fusaka—the journey continues. The upcoming Glamsterdam upgrade will bring step-function scaling, and the Foundation is leading the path toward post-quantum security. This is the most ambitious technical roadmap in the industry.

Decentralization is a Feature, Not a Bug

Some of the fiercest criticism against the Foundation frames decentralization as a weakness. This gets the institutional logic precisely backwards. The Ethereum ecosystem has the largest developer community of any chain, and the vast majority of those developers do not work inside the Foundation.

No single foundation should control a chain. Institutions will not abandon their current systems just to lock themselves into another proprietary setup.

They need confidence that the foundational properties they rely on cannot be arbitrarily altered by a small group of controllers. In fact, no chain should rely on any single participant.

Ethereum's credible neutrality and decentralization are precisely why it can serve as the future settlement layer for finance. These are not bugs.

If I had to choose between a foundation focused on security, privacy, post-quantum readiness, and core protocol, versus one that just services short-term marketing, I'd pick the former every single time.

Analogizing ETH's Value Using Amazon

History is full of examples where foundational innovation is dismissed by naysayers, overshadowed by flashier newcomers, only for the naysayers to be proven completely wrong. Amazon offers the clearest case study.

Early on, the market consensus on Amazon was: an unprofitable online bookseller propped up by the dot-com bubble. Bears fixated on the P&L, missing Bezos's long-term ambition.

He was building an entirely new online commercial market structure. Its total addressable market wasn't books; it was the entire retail economy, later expanding to cloud computing and media. Analysts focused on short-term prices missed the larger opportunity.

Ethereum and ETH today are in the same position. Its total addressable market is not crypto trading; it's the entire global financial system. ETH's intrinsic value is tied directly to the network's expansion.

And that network stands on the precipice of a step-change in transaction volume, spanning stablecoins, tokenized real-world assets, DeFi, and the emerging wave of agentic finance.

To secure such enormous transaction volume, ETH will become the essential incentive layer, the ultimate bearer instrument of trust, and its monetary premium will rise accordingly.

No ETH, no Ethereum. The asset and the network are inseparable.

Be Fearful When Others Are Greedy

In almost every market cycle, the moment when retail capitulates and sentiment hits rock bottom is precisely when disciplined capital has the opportunity to enter.

Warren Buffett built Berkshire Hathaway by buying quality assets at the market's worst moments: from GEICO in the 1970s to Bank of America and Goldman Sachs during the 2008 financial crisis.

For much of the past year, the Fear & Greed Index has shown extreme fear. The smartest investors buy quality assets during maximum fear. They act counter-cyclically, not in tandem with the crowd.

During the crypto winter post-FTX, most institutions chose to avoid Bitcoin and ETH exposure, or shelved product launches. At BlackRock, we did the opposite.

We doubled down, investing in infrastructure, building ecosystem partnerships, and launching products to connect TradFi with crypto. There's a lesson from both Buffett and BlackRock that we should all heed.

Raising New Voices for Ethereum

The Ethereum Foundation is doing its job. Moving forward, it will focus even more on the CROPS properties—Censorship-Resistance, Capture-Resistance, Open-Source, Privacy, Security.

For most, the question is clear: at a time when institutions are eager to embrace Ethereum, there is a leadership gap in the go-to-market effort.

I have a strong belief: stakeholders and participants within the ecosystem need to play a more prominent role in Ethereum's narrative and institutional adoption.

Since last summer, Digital Asset Treasury companies and core Ethereum stewards have already played a crucial part in this effort.

This includes ecosystem participants like Sharplink, Tom Lee of BitMine, Joe Lubin of Consensys, Etherealize, Nethermind, Aave, Morpho, EEA, and others. We also collaborate closely with the small team inside the Foundation focused on institutional education and adoption.

Sharplink itself is investing in this ecosystem. We were among the first to stake billions in ETH, deployed hundreds of millions into quality DeFi protocols, and recently co-launched a $125 million DeFi yield fund with Galaxy Digital to support existing and emerging protocols.

Even so, we can do more, and we will: be outspoken advocates for Ethereum, actively supporting the impending supercycle of institutional adoption.

Ethereum's future is unfolding now.

Related Reads

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

Three Years Later: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's launch, I made 20 predictions about its future. Now, in mid-2026, I've used AI agents to fact-check each one against the latest data. Overall, most major directional forecasts were correct, with only one outright error (incorrectly stating GPT-4 had 100 trillion parameters). Key successes included predicting that RAG and retrieval architectures would become the standard for handling knowledge and hallucinations, that natural language interfaces (LUI) would create a massive new industry layer beyond the models themselves, and that China would develop viable large language models, significantly closing the performance gap with Western counterparts within about three years. Predictions about the absence of mass unemployment, the rise of a new "robot network" for agent communication, and ChatGPT not possessing consciousness also held true in their core arguments. However, the "devil was in the details." Errors frequently involved specific numbers, timelines, or overlooking distributional effects. I tended to overestimate the speed of adoption (e.g., for agent networks) while underestimating the ultimate scale of capabilities or costs (e.g., AI winning IMO gold without tools, or the extreme capital required for frontier models). Other misjudgments included: underestimating how AI would reinforce, not dissolve, information filter bubbles; incorrectly assuming AI-generated content would easily circumvent copyright (it has instead triggered record-breaking settlements); and misidentifying where value would be captured (it accrued overwhelmingly to the compute layer, like Nvidia, not just the application or model layers). Key lessons from reviewing these predictions are: 1) Directional and mechanistic insights are far more reliable than precise numbers or absolute statements. 2) There's a consistent bias to overestimate short-term speed but underestimate long-term magnitude. 3) Errors often lie in missing distributional impacts within a generally correct aggregate trend. 4) Predictions phrased with nuance and caveats aged the best. 5) Some fundamental debates (e.g., on machine consciousness or the ultimate value chain) remain unresolved even after three years. This exercise is less about scoring the past and more about establishing rules for clearer thinking about the next three years of AI.

marsbit1h ago

Three Years Later: Looking Back at My Predictions About ChatGPT in 2023

marsbit1h ago

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

Looking Back After Three Years: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's debut and before GPT-4's release, I made over twenty predictions about AI's future based on limited information and intuition. Now, in May 2026, I revisited those forecasts using an AI-driven analysis with 41 Opus 4.8 agents to cross-reference them with the latest data. The assessment used symbols: ✅ Correct, 🟢 Mostly Correct, 🟡 Partially Correct, ❌ Incorrect. Overall, the directional judgments held up well, with only one major factual error regarding GPT-4's rumored parameter size (incorrectly cited as 100T). However, nuances and degrees of accuracy revealed more. **What Was Largely Correct:** Predictions about mechanisms and directions proved accurate. The rise of RAG (Retrieval-Augmented Generation) as the standard architecture for combating AI hallucination was confirmed, as was the transformative potential of LUI (Language User Interface) in creating a new industry layer atop GUIs. The emergence of "robot networks" (agent-to-agent communication protocols) and China's rapid catch-up in developing capable large models (closing the performance gap with top models to ~2.7%) were also on point. The analysis affirmed that LLMs lack consciousness and that the Turing Test merely measures perceived intelligence. **What Was Off Target:** Errors often involved specific numbers, over-optimistic timelines, or misjudged distributions. The prediction that value would primarily accrue to the application layer was half-right but missed NVIDIA's dominance as the profitable infrastructure layer. Forecasts about AI circumventing copyright issues and fostering a "global common ground" by averaging human viewpoints were incorrect; instead, major copyright settlements occurred and AI personalization is increasing. Estimates for model training costs ("$5-10 billion cap") were significantly off, underestimating frontier costs and overestimating replication costs. The notion that LLMs could never do complex math without tools was disproven by later models winning IMO gold. **Key Patterns from the Review:** 1. **Direction over precision:** Judgments about mechanisms and trends were more reliable than specific numbers or definitive statements. 2. **Timing bias:** There was a tendency to overestimate short-term speed but underestimate long-term magnitude and transformation. 3. **The distribution blind spot:** Aggregate-level correctness often masked uneven impacts (e.g., on young professionals' employment). 4. **The value of qualifiers:** Predictions framed with caution (e.g., "reportedly," "for now," "prototype in 2-3 years") aged better. 5. **Some debates continue:** Issues like the nature of "emergent abilities" or machine consciousness remain unresolved. This three-year review highlights that while seeing the big picture is crucial, humility regarding specifics, timelines, and disparate impacts is essential for future forecasting.

链捕手3h ago

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

链捕手3h ago

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

The article issues a stark warning about a potential AI investment bubble. It notes that while the AI boom shares similarities with the TMT bubble of the late 1990s, its scale is vastly larger, currently driving 93% of U.S. GDP growth. Major hyperscale cloud providers like Microsoft, Alphabet, Amazon, Meta, and Oracle are planning to invest trillions in AI data centers over the coming years. However, calculations based on analyst projections for 2025-2030 reveal a concerning math problem: expected capital expenditure growth far outpaces projected revenue growth. Even under an extremely optimistic scenario of zero costs, the implied return on investment for most of these tech giants (except Amazon) is deeply negative. This suggests that the current trajectory could lead to one of history's largest shareholder value destruction events. The piece outlines two potential escapes: AI generating vastly more revenue than currently anticipated—a near-impossible task—or a significant cutback in the planned investment splurge. The latter scenario could trigger a domino effect, severely impacting the entire tech supply chain (from Nvidia to TSMC), potentially pushing the U.S. economy into recession, and causing a major stock market downturn. The author suggests upcoming high-profile IPOs by companies like OpenAI and Anthropic might represent a transfer of risk from early investors to public market participants. While the peak of the hype cycle might sustain investment through 2026, the fundamental financial dilemma remains unresolved, setting the stage for a potential market correction in 2027 or 2028, similar to the years following Alan Greenspan's "irrational exuberance" warning.

marsbit4h ago

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

marsbit4h ago

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

The article "From Token to Machine Labor: AI is Evolving from Tool to 'Worker'" argues that the business model for AI is shifting beyond simply selling computational resources (tokens, GPU hours) or model access. Instead, a new "machine labor market" is emerging, where the core economic transaction is the purchase of economically useful work directly performed by software. The central thesis is that AI pricing will evolve through four stages: 1) raw tokens, 2) standardized LLM capabilities (e.g., text generation), 3) industry-specific labor markets (e.g., legal review, radiology), and finally 4) a programmable results market where tasks like resolving a support ticket are bid on and priced based on outcome. In this future, buyers will care less about *which* model or GPU completes a task and more about whether the work meets specified standards for accuracy, latency, and cost. This transition reframes the impact of AI on human labor. Rather than simple replacement, it suggests a re-coordination where machines handle standardized, verifiable work, freeing humans for roles involving oversight, context management, responsibility, and final judgment. In some cases, this "last 1%" of human input becomes more valuable as it enables the other 99% to be automated. Furthermore, as AI reduces the cost of work, demand may expand, creating larger markets (e.g., 24/7 customer service) rather than just cheaper versions of existing ones. The article concludes that while infrastructure (GPUs, models, tokens) remains crucial upstream, the market is converging on a simpler, tradeable unit: machine labor that can be defined, measured, priced, and procured based on contractible specifications.

marsbit4h ago

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

marsbit4h ago

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

The price of Xiaomi's MiMo-V2.5 series API has been permanently reduced by up to 99%, specifically for the "Input (Cache Hit)" cost, which covers users re-reading historical context in long conversations. MiMo's head, Luo Fuli, published a detailed technical blog to clarify that this drastic price cut stems from genuine engineering breakthroughs, not a marketing stunt or a simple price war. The core of the achievement lies in six key engineering optimizations. First, the model architecture adopts a Hybrid Sliding Window Attention (SWA), reducing the memory footprint (KVCache) to 1/7th of a traditional model. Second, a dual-pool memory management system actually utilizes these savings, allowing a single GPU to handle over 5 times more concurrent users. Third, an upgraded prefix caching mechanism achieves a cache hit rate of 93-95% for repeated reads, meaning most such requests bypass GPU computation entirely. Fourth, a self-developed distributed cache (GCache) utilizes idle SSD space on existing GPU servers, eliminating additional storage costs. Fifth, an intelligent scheduling system (LLM-Router) efficiently routes requests to maximize cache reuse and performance. Sixth, Multi-Token Prediction (MTP) accelerates the model's text generation ("output") side. Together, these systemic optimizations dramatically lower the real computational cost per request, enabling the 99% price reduction for cached inputs while reportedly maintaining positive gross margins. Luo Fuli's disclosure aims to shift the narrative from "price war" to a demonstration of substantive AI engineering progress.

marsbit6h ago

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

marsbit6h 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

Top Questions

Hot Categoriesright-arrow

Hot Tagsright-arrow