NEW POD @vitalikbuterin on the @greenpillnet podcast for a year-end deep dive into public goods funding in the @Ethereum ecosystem. Thx @devanshmehta for co-hosting. We discuss how the landscape has shifted from “vibes-based” funding to verifiable, dependency-driven mechanisms, and why this is the best moment to reform PGF using new tools like programmable cryptography, AI-assisted evaluation, and deep funding models. Vitalik shares how he thinks about dependencies, credible neutrality, open-source licensing, pluralism, accountability, ethereum localism, and what builders should prioritize in the coming year. 00:00 – Welcome to the Greenpill Podcast 01:50 – Vitalik joins: why public goods funding matters 02:19 – Why PGF is essential for decentralization 04:18 – The crypto spirit: censorship resistance, institutional design & funding 06:42 – The shift from vibes-era PGF to verifiable mechanisms 08:25 – Why 2026 is the best moment to reform PGF 10:19 – Where does PGF money actually come from? 12:45 – Open-source licensing, taxes & funding dependencies 17:34 – “Fund your dependencies” as a stable mechanism 19:35 – Why general-purpose QF doesn’t work in a chaotic world 21:59 – Bottom-up vs top-down: polycentric PGF 25:29 – How to create accountability loops in public goods 27:22 – Funding open-source as an Ethereum priority 29:31 – Privacy as a public good & why it’s upstream of PGF 31:54 – What OSS developers really think about crypto 33:52 – Mixing social outreach with financial support 35:56 – What should PGF builders focus on in 2026? 38:13 – Work with new projects, not legacy ones 39:44 – Ecosystem cycles & “layers of sediment” 41:39 – Yield-based funding (Octant) & treasury strategies 43:40 – Accountability: from vibes to rigorous mechanisms 47:35 – Motivation, feedback & the psychology of public goods 50:43 – Profit sharing licenses & sustainable PGF pools 53:46 – Security, issuance & public goods 56:12 – Technology, democracy & long-term risks 58:31 – How PGF relates to DIAC (Defensive/Decentralized Acceleration) 01:00:05 – Solving the free-rider problem without coercion 01:02:12 – Mechanisms vs coercion: credible neutrality 01:04:16 – Institutions, power & capture risks 01:06:16 – Individuals vs institutions in PGF 01:08:41 – Why PGF is more error-tolerant than governance 01:11:01 – Pluralism: many funders, many mechanisms 01:13:14 – Why diversity of funders is healthy 01:15:17 – What Vitalik wants built next 01:17:12 – Ethereum localism & real-world experiments 01:19:28 – What success in PGF looks like by end of 2026 01:24:28 – Closing thoughts

Loved the 4 takeaways by @metaproph3t on decision markets in practice They started by doing decision markets for logos but realized quickly that founders don't like giving up control So they then became a launchpad where founders give IP and power to spend investor money over to futarchy After achieving PMF, they have learnt 4 key lessons that he was generous enough to share 1. Decision markets are good at reducing fraud Ranger was a fraudulent raise on metadao. Once investors realized, they made a proposal to reclaim all their money and it passed On the flipside, Solomon raised 8 million. When they asked for 4.5 million to use as liquidity, some investors thought it was fraud and traded against. But other traders saw it was genuine, leading to it passing after $2.68 million in trading volume 2. You can still play funny games in decision markets ZFKG founders wanted to rug their investors, but didn't have the token supply to trade for it. So they created one obviously bad proposal, which traders went against. As their token supply got used up in this proposal, they then created another malicious one that then passed. This happened yesterday and they are figuring out what to do about it 3. Markets don't like issuing new tokens at a discount to its market price This is quite a contrast to normal web3 projects where tokens are regularly sold below spot to get cash 4. Decision markets don't like when token is trading below NAV If the value of assets in the treasury is greater than its mcap, futarchy has always approved buybacks till it becomes equal. This is in contrast to tokenvoting wher GNOSIS voted against Overall, they are now focusing on fundraising for vibe coders as the crypto fundraising scene is down bad. They called themselves 'founder rigor' rather than 'founder friendly', since investors can recall their money and also trade to make major decisions. Their hope is proving higher RoI for such futarchy orgs so investors insist on this structure like how they do with delaware c corp Going into the future, they have 3 new initiatives in the works; 1. $2 million is approved for activist investor type personas that make proposals. Improving the pipeline of high quality proposals & increasing engagement is something they are looking to address through this structure 2. A whitelist for only good investors to participate, eliminating those who are toxic and dunk on you if token is down 3. A research fund to bring academic rigor into decision markets and whether it makes good decisions They also have 3 unsolved questions to tackle; A. Which decisions should go to the market in the first place? B. An inherent issue in decision markets where traders have a free call option on a proposal succeeding (didn't understand this point fully) C. Giving up control still sux esp at the early stage, so how do we make it more founder friendly Early ethereans spent a lot of their wealth effect on tokenvoting via open zeppelins governor contract. I'm glad that solana is now spending it's wealth on properly testing out decision markets so we can learn about the messy details on their dime 😆

As a conference focused on prediction markets, great start seeing @robinhanson get to the messy details of futarchy in and putting prices against decision One issue was CEOs not wanting to give up decision making power to markets, so they GTM with multi stakeholder decisions

Came back to manifest this year, and it's as well organized as ever! Their debate format is like chess where each speaker has a clock that ticks when they are speaking 😆 Prevents one person from talking too much & makes it more engaging too

manifest is officially the best organized conference i have ever attended, narrowly beating out the @PluralityInst conference from 2022 they brought together a very particular set of people: those who read @slatestarcodex , listen to @dwarkesh_sp podcast, follow @Aella_Girl for romantic advice, were early on AI risk becoming a big deal, make bets on prediction markets for fun, frequent LessWrong , EA and other rationality forums, know about impact certificates & desire funding or recognition from @open_phil what were some of the things that organizers @akrolsmir , @saulmunn & rachel weinberg did right? 1. talks were strictly on time so that people could follow the schedule. they delay the start by 5 minute but end promptly by moving speakers to a side room for office hours where attendees engage in deeper discussion 2. high price for attendees ($400-$650) , fully complementary to invited speakers 3. insanely good lunch and dinner that prevented us from ever having to leave the venue. have seen too many hungry people leave crypto conferences becoz there's free alcohol but no food 😅 this one had the full works, from electrolytes, vitamin c powder and nutribars to soft drink cans, popcorn, lindor truffle chocolates, and potato chips 4. understanding that the purpose of events is not to give a lecture but bring together people in the same subject area. most talks were highly interactive as a result and there were even sessions for getting people to dance & wrestle with each other 😲 on the 1st day, @mishaglouberman facilitated conversations by having us randomly speak to each other in groups of 3 & then hold up signboards of topics we like so we move around & self-organize by interests 5. lighthaven campus on berkeley was simply the best venue ever (photo below), with talks nestled into various nooks & crannies throughout the venue really memorable experience, manifest has taught me what it takes to run a good event with attention to detail. feel lucky i was in california the same time this was taking place, gonna try making it an annual pilgrimage if it continues!

Recently I have been starting to worry about the state of prediction markets, in their current form. They have achieved a certain level of success: market volume is high enough to make meaningful bets and have a full-time job as a trader, and they often prove useful as a supplement to other forms of news media. But also, they seem to be over-converging to an unhealthy product market fit: embracing short-term cryptocurrency price bets, sports betting, and other similar things that have dopamine value but not any kind of long-term fulfillment or societal information value. My guess is that teams feel motivated to capitulate to these things because they bring in large revenue during a bear market where people are desperate - an understandable motive, but one that leads to corposlop. I have been thinking about how we can help get prediction markets out of this rut. My current view is that we should try harder to push them into a totally different use case: hedging, in a very generalized sense (TLDR: we're gonna replace fiat currency) Prediction markets have two types of actors: (i) "smart traders" who provide information to the market, and earn money, and necessarily (ii) some kind of actor who loses money. But who would be willing to lose money and keep coming back? There are basically three answers to this question: 1. "Naive traders": people with dumb opinions who bet on totally wrong things 2. "Info buyers": people who set up money-losing automated market makers, to motivate people to trade on markets to help the info buyer learn information they do not know. 3. "Hedgers": people who are -EV in a linear sense, but who use the market as insurance, reducing their risk. (1) is where we are today. IMO there is nothing fundamentally morally wrong with taking money from people with dumb opinions. But there still is something fundamentally "cursed" about relying on this too much. It gives the platform the incentive to seek out traders with dumb opinions, and create a public brand and community that encourages dumb opinions to get more people to come in. This is the slide to corposlop. (2) has always been the idealistic hope of people like Robin Hanson. However, info buying has a public goods problem: you pay for the info, but everyone in the world gets it, including those who don't pay. There are limited cases where it makes sense for one org to pay (esp. decision markets), but even there, it seems likely that the market volumes achieved with that strategy will not be too high. This gets us to (3). Suppose that you have shares in a biotech company. It's public knowledge that the Purple Party is better for biotech than the Yellow Party. So if you buy a prediction market share betting that the Yellow Party will win the next election, on average, you are reducing your risk. Mathematical example: suppose that if Purple wins, the share price will be a dice roll between [80...120], and if Yellow wins, it's between [60...100]. If you make a size $10 bet that Yellow will win, your earnings become equivalent to a dice roll between [70...110] in both cases. Taking a logarithmic model of utility, this risk reduction is worth $0.58. Now, let's get to a more fascinating example. What do people who want stablecoins ultimately want? They want price stability. They have some future expenses in mind, and they want a guarantee that will be able to pay those expenses. But if crypto grows on top of USD-backed stablecoins, crypto is ultimately not truly decentralized. Furthermore, different people have different types of expenses. There has been lots of thinking about making an "ideal stablecoin" that is based on some decentralized global price index, but what if the real solution is to go a step further, and get rid of the concept of currency altogether? Here's the idea. You have price indices on all major categories of goods and services that people buy (treating physical goods/services in different regions as different categories), and prediction markets on each category. Each user (individual or business) has a local LLM that understands that user's expenses, and offers the user a personalized basket of prediction market shares, representing "N days of that user's expected future expenses". Now, we do not need fiat currency at all! People can hold stocks, ETH, or whatever else to grow wealth, and personalized prediction market shares when they want stability. Both of these examples require prediction markets denominated in an asset people want to hold, whether interest-bearing fiat, wrapped stocks, or ETH. Non-interest-bearing fiat has too-high opportunity cost, that overwhelms the hedging value. But if we can make it work, it's much more sustainable than the status quo, because both sides of the equation are likely to be long-term happy with the product that they are buying, and very large volumes of sophisticated capital will be willing to participate. Build the next generation of finance, not corposlop.

Learnt about the 3 things institutions want to be sure of before coming on Ethereum @ethconf 1. the counterparty they trade with isn't sanctioned 2. they aren't paying a sanctioned entity (such as N korea validating their blocks) 3. they aren't frontrun via mev There are proposals of separate set of "institutional blocks" that are from attested, US based validators. Apparently adoption of commit boost by validators plays a part in enabling this I'm still unsure how & whether it affects censorship resistance, if users can start to choose the category of validators they want for their blocks

Learnt about the 3 things institutions want to be sure of before coming on Ethereum @ethconf 1. the counterparty they trade with isn't sanctioned 2. they aren't paying a sanctioned entity (such as N korea validating their blocks) 3. they aren't frontrun via mev There are proposals of separate set of "institutional blocks" that are from attested, US based validators. Apparently adoption of commit boost by validators plays a part in enabling this I'm still unsure how & whether it affects censorship resistance, if users can start to choose the category of validators they want for their blocks

how prediction markets should do fees > if you made a loss upon resolution, zero fees > if you made a profit upon resolution, x% of your profit based on traders i spoke with, few mind giving up a portion if they are already in the green esp if those fees go to liquidity

how prediction markets should do fees > if you made a loss upon resolution, zero fees > if you made a profit upon resolution, x% of your profit based on traders i spoke with, few mind giving up a portion if they are already in the green esp if those fees go to liquidity

Gulp we are only now realising that there is no easy way to distribute money to 4k open source repos at one shot The ideal flow would be something like; 1. Inform all repos via email or a gh issue of funds received for being a dependency 2. Get them to complete KYB , upon completion of which they receivd a soulbound token 3. Send money to all repos with the soulbound token, if a repo has not gone thru the KYB process by a cutoff date their money gets redistributed to those that have What we don't have is an entity in the middle that can receive funds from established funders to then allocate to the repos And the tech for distribution of money to repos is itself quite raw & untested

we just posted the data to resolve the level 3 deep funding market! we got 15 open source repos sharing relative value between 685 of their dependencies using prediction markets, we scaled their evaluation to 3,677 dependencies across 98 open source repos how does this work? using leverage through whats called conditional prediction markets traders put money across all repos & their dependencies, but their P&L is based on only the ones that did get evaluated. so if they just trade on 2 markets, one of which is evaluated and one isnt, their leverage is 2x as their entire position is based on the one that did get evaluated so by evaluating 685 dependencies out of 3,677, our conditional prediction market for value of dependencies in an open source repo traded at a leverage of 5.36 the link with all the data is posted in the tweet below, really hope we start to see more conditional prediction markets & not just the bread and butter binary ones that are the current staple of the space ongoing markets for originality (how much stays with repo vs its dependencies) and relative value between the 98 repos still available to trade