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EigenLayer’s Sreeram Kannan: King of the Professor Coins

Kannan may have played a larger role than any other entrepreneur in revitalizing DeFi on Ethereum. But not everything went according to plan.

For a crypto founder who’s attracted so much controversy, Sreeram Kannan is surprisingly sanguine.

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In a wide-ranging interview after his selection as one of CoinDesk’s “Most Influential” figures in crypto for 2024, the EigenLayer founder was generous with his time, chatting more than an hour beyond our scheduled slot. I was surprised at his openness because the last time we spoke, a colleague and I had just published an investigation into potential conflicts of interest at his company, Eigen Labs, and in the interim Kannan had disavowed our reporting point-by-point on a Blockworks podcast.

This time, Kannan emerged in a different light. Whatever his misgivings about CoinDesk’s past coverage, they didn’t seem top-of-mind.

What emerged wasn’t the portrait of a defensive tech founder, but rather that of a driven, thoughtful academic-turned-entrepreneur still adjusting to a spotlight few in this industry ever enjoy. Instead of bitterness or evasion, I found ambition, reflection and a quiet kind of excitement.

Kannan seemed as astonished as anyone by how swiftly EigenLayer had transformed from a concept into one of crypto’s most talked-about experiments, telling CoinDesk that he continued to view EigenLayer as a “scrappy startup.”

Over the past 12 months, EigenLayer — which allows emerging blockchain applications to borrow Ethereum’s robust security — went from a relative unknown to an industry heavyweight. The platform raised more than $100 million from venture firms including Andreessen Horowitz and, before even fully launching, drew hundreds of millions of dollars in deposits from crypto users seeking extra yield. Many were incentivized by a viral points program that investors hoped would translate into a lucrative future token airdrop.

EigenLayer’s success during the bear market was striking, and Kannan may have played a larger role than any other entrepreneur in revitalizing decentralized finance on Ethereum. But not everything went according to plan.

Industry critics took issue with the EIGEN token distribution plan — which locked up tokens for months and barred claimants from certain geographies — as well as the platform’s slower-than-expected feature rollout and concerns about “rehypothecation,” or the reuse of collateral for multiple purposes. In August, the CoinDesk investigation (that Kannan disputed in the podcast) raised questions about EigenLayer’s conflict-of-interest policies, which may have allowed employees preferential access to tokens powered by its platform.

None of this seemed to derail Kannan’s intellectual ascent. Beyond running Eigen Labs, he still holds a position as an associate professor of electrical and computer engineering at the University of Washington, though he is currently on leave, and his theory of “restaking” — letting people reuse staked Ethereum assets to secure other networks — has sparked a wave of innovation and copycats. He’s become a familiar face on the conference circuit, where he unpacks his vision of blockchains as tools for solving humanity’s endless “coordination problems.”

Blockchains, Kannan says, “are the biggest upgrade to human civilization since the U.S. Constitution.”

Academia

Kannan grew up in Chennai, in southern India. At first, he was drawn to pure math, staying in India for his undergraduate and master’s degrees. He studied telecommunications, a discipline that would later prove relevant to crypto’s distributed systems.

In 2008, he moved to the United States to earn another master’s in mathematics at the University of Illinois at Urbana-Champaign, followed by a Ph.D. in entrepreneurship. Then, postdoctoral stints at Berkeley and Stanford opened his eyes to new academic frontiers.

At Berkeley, a lecture on “Synthetic Genomics” lured Kannan into the intricate realm of reprogramming living systems. “I said, ‘Okay, that seems much more fun than trying to get people to download more and more data on their phones,’” Kannan remarked.

Computational biology became Kannan’s specialty. As an associate professor at the University of Washington, he worked with his students to develop complex mathematical models to study the structure of DNA. Then advances in artificial intelligence blindsided him. One of Kannan’s students proposed using AI for a particularly tricky DNA sequencing problem, and Kannan balked — surely a neural network couldn’t outperform his finely tuned equations. Yet, in just two weeks, the AI beat Kannan’s best benchmarks.

Kannan came to a disturbing realization: “In five or ten years, all the stuff I was doing — the mathematical algorithms — is all gone,” he said. “AI will do everything.”

Pathfinding

Confronted with AI’s relentless rise, Kannan saw two paths: go deeper into AI-driven computational biology or try something new. He chose the latter.

In 2017, a call from his Ph.D. advisor alerted him to Bitcoin’s meteoric rise. Kannan began dabbling in crypto, and a reading of Yuval Noah Harari’s “Sapiens” offered deeper inspiration. Kannan’s takeaway from the bestseller was that “the reason why humans are special is not that we are intelligent,” or “can innovate.” Instead, humanity’s strength comes from our ability to coordinate at scale.

“Coordination is communication plus commitments,” Kannan said, explaining that while the internet had solved global communication, there was still no digital-native way to ensure trust. To Kannan, the trustless architecture of blockchains could fill that void. “If you don’t trust somebody, you’re not going to be able to coordinate,” he said, framing blockchains as the next evolutionary leap in human cooperation.

He dove deeper into Bitcoin, noting its low throughput and inefficiencies. That felt oddly familiar. “This is what I had studied in my PhD: How do you optimize a peer-to-peer wireless network?” Crypto’s bottlenecks and scaling issues seemed like the perfect place to apply his telecommunications expertise.

By early 2018, Kannan had found his purpose in crypto: not just to tinker, but to use his academic experience to address fundamental human coordination and scaling problems. He was ready, as he put it, “to go all in.”

Founding EigenLayer

Kannan’s early path through crypto founderdom included a few less-than-successful pit stops, among them building a short-lived NFT marketplace. “I realized I can only really build things for which I, or some core team members, are also the consumers,“ Kannan said. He shuttered the project in under a year.

He then began shopping around ideas for new blockchain security models, including one that he proposed to Cardano, the blockchain project helmed by Ethereum co-founder Charles Hoskinson. Kannan’s work in this area eventually culminated in an idea that stuck: “restaking” — the technology that would eventually underpin EigenLayer.

Ultimately, Kannan focused on Ethereum, the most widely used smart-contract blockchain, and he formed Eigen Labs, the company behind EigenLayer. The new platform’s goal was straightforward: let emerging blockchain projects “borrow” Ethereum’s security through restaking.

Ethereum is secured by a system in which users “stake” ether (ETH) as collateral, effectively earning interest in return for helping validate the network. Misbehavior – such as misreporting transactions or going offline – risks having collateral slashed.

EigenLayer builds on that structure, allowing stakers to earn additional returns by “restaking” their ETH pledged on the main chain to secure other networks, known as “actively validated services” or AVSs.

It’s unlikely most stakers (or restakers) really understand how this all works under the hood. Most investors stake ETH because they want to earn interest. EigenLayer promised to boost yields with its restaking.

For AVS developers, EigenLayer provides an easy way to tap into Ethereum’s collateral reserves without building a new security framework from scratch. This concept of “shared security” resonated widely and helped propel EigenLayer’s sudden rise.

“It’s a crazy, 100-year project, and it upgrades the human species,” Kannan told CoinDesk.

Growing pains

As EigenLayer soared, the bright lights brought scrutiny. “There was a lot of uncomfortable attention,” recalls Kannan. The attention was “positive, initially,” but it eventually began to sour in some corners.

“I think the first time the negativity hit was after the token launch,” reflected Kannan.

Before announcing the EIGEN token, EigenLayer gave “points” to depositors, a common tactic in crypto to spark early interest. Officially, the points are just an informal tally meant to gamify the system. But people mainly racked up points because they assumed they’d eventually be able to cash them in for EIGEN crypto tokens — speculation that EigenLayer did little to quell.

Entire markets emerged around these points, even though they were not meant to hold intrinsic value and EigenLayer never directly confirmed that it would release a token.

Early enthusiasm surrounding EigenLayer points turned into disappointment once the EIGEN token details finally emerged in April. People who expected easy liquidity chafed at EigenLayer’s plan to lock tokens for several months. Some felt excluded by geography-based restrictions, which Eigen Labs imposed to avoid violating U.S. securities laws. Others criticized EigenLayer’s slow feature rollout and fretted over conflict-of-interest issues, including (but not limited to) those raised by CoinDesk’s investigation.

“We had these features which were coming up. We had more decentralization coming up,” said Kannan. In the EigenLayer founder’s mind, he was “trying to protect the rights of all the people holding tokens” with his conservative regulatory approach, and by blocking transfers until after the platform was ready to release its main features. But, Kannan admits, ”it just blew up in the most negative possible manner.”

Kannan attributes some of the turbulence to his academic roots. He’d stepped into a world rife with hype cycles, tribal spheres, and financialization, and he was still learning its rhythms.

Early on, he realized that building a crypto startup required a more diverse team and skill set than any academic project. In one of his earlier failed crypto ventures, “everybody was similar,” with PhDs from “Stanford, MIT, and the University of Washington.” With EigenLayer, Kannan knew he needed not just brilliant engineers but also clear communicators, community advocates, and savvy business operators.

But Kannan still had to learn how to turn intellectual rigor into practical progress — and how to communicate that progress to a restless audience. The token fiasco exposed a disconnect between Eigen Labs and its community.

Users and developers wanted more transparency, collaboration, and communication. To Kannan, those demands felt extreme even by crypto’s warped, highly financialized standards. But he eventually understood that his perception of EigenLayer, as a scrappy startup, didn’t match how others saw it, as an industry juggernaut.

Kannan recalls being at a crypto conference and having a stranger ask him how the crypto community should address a concerning trend of over-leverage in crypto markets. Kannan was confused. “That doesn’t have anything to do with EigenLayer,” he recalled thinking. “I asked him, ‘Why are you telling me this?’” The answer: “Because you’re an industry leader.”

It was a turning point. Kannan, who once saw himself as “just some startup guy,” began accepting this new reality. Influence comes with responsibility and complexity.

One EigenLayer investor reminded Kannan that as he charted new territory, he would continue facing unexpected hurdles. In founding a startup, Kannan would be forced to reckon with something he was used to from his research days: trial and error. “You will learn,” the investor told him, “So I’m going to let you make your mistakes.”

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How CoinDesk Will Use Generative AI Tools

Artificial intelligence (AI) tools are changing the way we work – especially the media. Here are the rules of the road for CoinDesk.

New tools driven by artificial intelligence (AI) have been grabbing headlines over the past several months. The basic gist of these tools is that in response to specific prompts, they can “create” content (whether text, imagery or something else) much faster than a human ever could. Once they’ve been “trained” on extensive datasets, these tools can essentially predict what a user wants, often with stunning accuracy.

With the right set of queries, chatbots such as ChatGPT can write entire articles about specific topics in mere seconds. AI-driven image generators can instantaneously produce illustrations to represent abstract topics. Still other tools can synthesize video and audio content from the “raw material” of text and images.

This obviously has massive implications for creative fields, and in particular media organizations like CoinDesk. We’ve been researching AI tools for the past few months, while simultaneously observing how other media companies have been using AI. We want to empower our staff to take advantage of these tools to work more effectively and efficiently, but with a process that safeguards our readers from the well-documented problems that can arise with AI content – as well as the rights of the original content creators on which the generative content is based.

There are several use cases for AI in the process of creating content. This article deals with the main ones that are relevant to CoinDesk’s content team. It does not cover every use case, and does not speak to workflow outside of the process of content generation.

Generative text in articles

Current AI chatbots can create text from queries very quickly. Users can also customize the text with adjustments to the query — complexity, style, and overall length can all be specified.

However, an AI cannot contact sources or triage fast-breaking information reliably. While it performs some tasks extremely well, AI lacks the experience, judgment and capabilities of a trained journalist.

AI also makes mistakes, sometimes serious ones. Generative tools have been known to “hallucinate” incorrect facts and state them with confidence that they’re correct. They have occasionally been caught plagiarizing entire passages from source material. And even when the generated text is both original and factually correct, it can still feel bland or soulless.

At the same time, an AI can synthesize, summarize and format information about a subject far faster than a human ever could. AI can almost instantaneously create detailed writing on a specific subject that can then be fact-checked and edited. This has the potential to be particularly useful for explanatory content.

Given its limitations and the potential pitfalls, the writing of an AI should be seen as an early draft from an inexperienced writer. In more illustrative terms, an AI tool is comparable to an intern who can write really fast. The analogy is apt: Typically, interns need a great deal of supervision in their work. They are often unfamiliar with the area they’re writing about and the audience they’re writing for, occasionally leading to serious errors. The editor assigned to their work needs to edit their work carefully, check the underlying facts and help tailor the article to the audience.

However, with the right editing process, the work of an intern can be made publishable relatively quickly, especially if the intern has command of the English language (something AI excels at). Similarly, with the right safeguards in place that both prioritize a robust editing process and target the specific pitfalls of AI, we believe that sometimes using generative text in articles can help writers and editors publish more information faster than a purely human-driven process.

With that in mind, CoinDesk will allow generative text to be used in some articles, subject to the following rules. The generative text must be:

Given the requirements and the inherent limitations of AI with respect to the primary ingredients of journalism (e.g., talking to sources), the number of use cases for generative text are few. However, we see an opportunity for AI to assist in explanatory content, such as in this article here. In every case where generative text is used in the body of an article – whether in whole or in part – the AI’s contribution will be clear through both a disclosure at the bottom of the article and the AI’s byline: CoinDesk Bot.

Generative images

CoinDesk will immediately discontinue the use of generative images in content. This is due to pending litigation around the use of proprietary imagery as “training” for various AI-driven image generators. We might make an exception in the case when the point of the article is to discuss generative images and the images are used in a way that constitutes fair use, but these would be on a case-by-case basis.

Using a generative image tool to help “inspire” a work of art created by a human is generally OK (this is akin to doodling on scrap paper) with the caveat that the human-created image should not be a de facto copy of the AI-generated image.

Generative voices

AI tools can generate or use human-sounding voices to read copy, effectively turning articles into audio clips or podcasts. Though CoinDesk doesn’t currently employ these tools, we see the practice as an evolution of tools that already exist for the visually impaired. If possible, the use of an AI voice generator will be disclosed in the accompanying show notes.

Social copy

Social copy typically functions as a short summary of an article, crafted for a specific platform. Because of its short length, social copy is relatively easy to fact-check and edit, and some AI text tools may be adept at crafting text in the style of specific platforms. In addition, there is less expectation among social audiences that the text accompanying a linked story is original.

For these reasons, CoinDesk allows AI-generated social copy as long as the person preparing the post edits and fact-checks the copy (which is standard), and for the same reasons we don’t think disclosure is necessary (and would lead to some very clunky tweets). As with use in articles, using generative images in social posts is forbidden.

Headlines

Like social copy, headlines are quickly fact-checked and edited. Because editors will always be directing the process, we view AI-written headlines as suggestions and are thus allowed. Disclosure isn’t necessary because this process does not add any new information, and editors will always check the headlines for accuracy and style. This also applies to subheadings and short descriptions.

Assistance with research

AI may sometimes be able to assist in summarizing long documents such as court filings, research papers and press releases, among others. As long as no part of the text generated is copied to a published article, this is generally allowed with no disclosure needed, with two important caveats:

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AI-generated story ideas

Any ideas generated by an AI will inherently need to be vetted and researched by the reporter or editor, so this is allowed. Unless actual text generated by the AI ends up in the final article, it’s not required to disclose that the idea was originally suggested via AI (although the author still may want to do so).

The future

These are the rules of the road for CoinDesk as we travel forward into an AI-driven future. That road may change direction suddenly, expand to a multi-lane divided highway or perhaps even come to a dead end, so we expect these rules to evolve in the coming months and years. Regardless, we’re determined to tread into this new frontier, but to tread carefully. We want these rules to empower our content team to work smarter, using AI for the very specific tasks that machines are best at, so humans can focus on what they’re best at: journalism.

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