Matt Liston (Augur co-founder, Gnosis' first CSO) pointed out fundamental economic structure problems with prediction markets through a Twitter thread.

In the 2024 U.S. presidential election, Polymarket predicted Trump at 60% when media and polls were showing 50/50, and it turned out to be correct. This proved that markets aggregate information better than experts. However, paradoxically, the bettors who provided billions of dollars in liquidity to generate this accurate signal were not properly compensated.

The problem lies in the value distribution structure:

• Bettors provided billions in liquidity and bore actual risk, but outcomes were simply profit or loss

• Hedge funds observed Polymarket prices for free, obtaining alpha signals at no cost

• Campaign teams developed probability-based strategies for free

Since anyone can view prices for free the moment they form, bettors are essentially subsidizing a global public good. Private data providers charge hedge funds millions because competitors can't see that data, but Polymarket prices are instantly visible to everyone.

As a result, prediction markets only work in areas where people want to gamble for entertainment (elections, sports, meme events). They don't work in areas where decision-making actually matters (probability of China invading Taiwan, FDA drug approval, supply chain collapse likelihood). Nobody bets on these topics for entertainment purposes, so there's no liquidity, and without liquidity, there are no useful prices.

Matt Liston's proposed solution is 'Cognitive Finance,' with the core idea being to make signals private.

1. Private Markets: Market state is maintained inside a TEE (Trusted Execution Environment), with only paid subscribers receiving signals through encrypted channels. Currently Polymarket follows "bet → price formation → anyone can view for free," but Cognitive Finance would be "bet → price formation → only paid subscribers can view."

2. Combinatorial Markets: Instead of individual markets, maintain one massive probability distribution across all outcomes. Currently "Will the Fed cut rates in March?" and "Will Q2 inflation exceed 3%?" are separate markets. In combinatorial markets, a single trade updates the entire probability distribution. Betting on Middle East war probability would automatically adjust defense stocks, shipping insurance, inflation expectations, and Fed policy expectations.

3. Agent Ecosystem: The key is separating agent types. Trading agents and sourcing agents are separated because if sourcing agents can see prices, they can infer "information X is valuable" from price movements and front-run. With information walls, agents are only rewarded for providing genuinely new signals.

Of course, this vision has many challenges including multi-agent coordination, enforcing information walls, and cold start problems. However, if prediction markets are to become actual decision-making infrastructure beyond gambling, an economic structure that captures the value of signals seems essential, so the direction itself appears valid.

Apewood argues in this post that the 'attention-as-currency economy' has been exhausted.

Looking at history, there were four stages:

• 2004-2016: Social graphs prioritized personal connections over metrics

• 2016-2020: Algorithmic feeds emerged, first incentives for viral content

• 2020-2025: TikTok's interest graph completely solved the optimization game

• Post-2024: Market saturation, signs of exhaustion appearing

The reason 'performative' and 'slop' became buzzwords in 2025 isn't because performance is new, but because the limits of the game have been reached. Previously, only insiders knew the formula, but now everyone knows the hooks, templates, and formats. When everyone optimizes the same way, the edge disappears.

Apewood proposes 'Gusto,' a concept from William Hazlitt, as the solution. If Literalism is following templates exactly (output-focused), Gusto is "the power or passion defining any object" (input-focused).

Specifically comparing:

• Literalism: "What content performs best?" / Gusto: "What do I actually want to create?"

• Literalism: Posting because the algorithm rewards it / Gusto: Posting because you have something to say

• Literalism: Reading to meet annual goals / Gusto: Reading because you can't put it down

The core test is simple: Would you still do it if nobody was watching? If yes, that's Gusto. YouTubers uploading 40-minute deep dives when the algorithm rewards 8-minute videos, engineers refactoring working code because it's messy, people cooking elaborately alone without Instagram—all of these qualify.

Meanwhile, the advice "just be authentic" is a trap. The word 'Person' itself derives from persona (mask), and all self-expression is inherently performance. Chasing authenticity can itself become another optimization. The real question isn't "Am I being authentic?" but "Does this performance contribute to my growth, or is it just capturing attention?"

Dylan Bane analyzed Helium's buyback strategy changes in a Twitter thread.

Helium's buyback timeline:

• August 2025: Started using 100% of Helium Mobile revenue for HNT buybacks

• Late 2025: Doubled down by switching from treasury burns to open market purchases

• January 2026: Suddenly stopped Helium Mobile buybacks (Carrier Offload buybacks continue)

Numbers:

• Total annual revenue: ~$21M

• Helium Mobile revenue: ~$11M (55%)

• Carrier Offload revenue: ~$10M (5x growth in 2025)

• HNT price in 2025: Dropped 80%+ despite buybacks

Helium did buybacks for two main reasons: 1) Regulatory—unlike stocks, token holders have no economic rights, so buybacks are the only legal method of "returning value," and 2) Hyperliquid's success ($1B+ annual buybacks) made buybacks a hot meta.

However, Helium's buybacks ultimately failed. Helium's $21M revenue is too small compared to Hyperliquid's $1B+, and in a bear market, selling pressure overwhelmed the buyback effect.

Dylan Bane's core argument is that buybacks don't make sense for early-stage DePIN. In TradFi terms, early startups reinvest everything into growth, and only mature companies do buybacks/dividends. Amazon's first buyback came in 2022, 25 years after IPO. Helium is #1 in DePIN revenue but only at $21M annually, which is Series B/C traction from a traditional startup perspective, not mature company territory.

Ultimately, many problems stem from the fact that token holders, unlike shareholders, have few ways to share in a company's growth upside. Buybacks briefly gained attention as a forced solution, but considering that no startup outside crypto does buybacks, it's difficult to expect buybacks to serve as anything more than a spark for short-term price increases.

Matt Liston (Augur co-founder, Gnosis' first CSO) pointed out fundamental economic structure problems with prediction markets through a Twitter thread.

In the 2024 U.S. presidential election, Polymarket predicted Trump at 60% when media and polls were showing 50/50, and it turned out to be correct. This proved that markets aggregate information better than experts. However, paradoxically, the bettors who provided billions of dollars in liquidity to generate this accurate signal were not properly compensated.

The problem lies in the value distribution structure:

• Bettors provided billions in liquidity and bore actual risk, but outcomes were simply profit or loss

• Hedge funds observed Polymarket prices for free, obtaining alpha signals at no cost

• Campaign teams developed probability-based strategies for free

Since anyone can view prices for free the moment they form, bettors are essentially subsidizing a global public good. Private data providers charge hedge funds millions because competitors can't see that data, but Polymarket prices are instantly visible to everyone.

As a result, prediction markets only work in areas where people want to gamble for entertainment (elections, sports, meme events). They don't work in areas where decision-making actually matters (probability of China invading Taiwan, FDA drug approval, supply chain collapse likelihood). Nobody bets on these topics for entertainment purposes, so there's no liquidity, and without liquidity, there are no useful prices.

Matt Liston's proposed solution is 'Cognitive Finance,' with the core idea being to make signals private.

1. Private Markets: Market state is maintained inside a TEE (Trusted Execution Environment), with only paid subscribers receiving signals through encrypted channels. Currently Polymarket follows "bet → price formation → anyone can view for free," but Cognitive Finance would be "bet → price formation → only paid subscribers can view."

2. Combinatorial Markets: Instead of individual markets, maintain one massive probability distribution across all outcomes. Currently "Will the Fed cut rates in March?" and "Will Q2 inflation exceed 3%?" are separate markets. In combinatorial markets, a single trade updates the entire probability distribution. Betting on Middle East war probability would automatically adjust defense stocks, shipping insurance, inflation expectations, and Fed policy expectations.

3. Agent Ecosystem: The key is separating agent types. Trading agents and sourcing agents are separated because if sourcing agents can see prices, they can infer "information X is valuable" from price movements and front-run. With information walls, agents are only rewarded for providing genuinely new signals.

Of course, this vision has many challenges including multi-agent coordination, enforcing information walls, and cold start problems. However, if prediction markets are to become actual decision-making infrastructure beyond gambling, an economic structure that captures the value of signals seems essential, so the direction itself appears valid.

Apewood argues in this post that the 'attention-as-currency economy' has been exhausted.

Looking at history, there were four stages:

• 2004-2016: Social graphs prioritized personal connections over metrics

• 2016-2020: Algorithmic feeds emerged, first incentives for viral content

• 2020-2025: TikTok's interest graph completely solved the optimization game

• Post-2024: Market saturation, signs of exhaustion appearing

The reason 'performative' and 'slop' became buzzwords in 2025 isn't because performance is new, but because the limits of the game have been reached. Previously, only insiders knew the formula, but now everyone knows the hooks, templates, and formats. When everyone optimizes the same way, the edge disappears.

Apewood proposes 'Gusto,' a concept from William Hazlitt, as the solution. If Literalism is following templates exactly (output-focused), Gusto is "the power or passion defining any object" (input-focused).

Specifically comparing:

• Literalism: "What content performs best?" / Gusto: "What do I actually want to create?"

• Literalism: Posting because the algorithm rewards it / Gusto: Posting because you have something to say

• Literalism: Reading to meet annual goals / Gusto: Reading because you can't put it down

The core test is simple: Would you still do it if nobody was watching? If yes, that's Gusto. YouTubers uploading 40-minute deep dives when the algorithm rewards 8-minute videos, engineers refactoring working code because it's messy, people cooking elaborately alone without Instagram—all of these qualify.

Meanwhile, the advice "just be authentic" is a trap. The word 'Person' itself derives from persona (mask), and all self-expression is inherently performance. Chasing authenticity can itself become another optimization. The real question isn't "Am I being authentic?" but "Does this performance contribute to my growth, or is it just capturing attention?"

Dylan Bane analyzed Helium's buyback strategy changes in a Twitter thread.

Helium's buyback timeline:

• August 2025: Started using 100% of Helium Mobile revenue for HNT buybacks

• Late 2025: Doubled down by switching from treasury burns to open market purchases

• January 2026: Suddenly stopped Helium Mobile buybacks (Carrier Offload buybacks continue)

Numbers:

• Total annual revenue: ~$21M

• Helium Mobile revenue: ~$11M (55%)

• Carrier Offload revenue: ~$10M (5x growth in 2025)

• HNT price in 2025: Dropped 80%+ despite buybacks

Helium did buybacks for two main reasons: 1) Regulatory—unlike stocks, token holders have no economic rights, so buybacks are the only legal method of "returning value," and 2) Hyperliquid's success ($1B+ annual buybacks) made buybacks a hot meta.

However, Helium's buybacks ultimately failed. Helium's $21M revenue is too small compared to Hyperliquid's $1B+, and in a bear market, selling pressure overwhelmed the buyback effect.

Dylan Bane's core argument is that buybacks don't make sense for early-stage DePIN. In TradFi terms, early startups reinvest everything into growth, and only mature companies do buybacks/dividends. Amazon's first buyback came in 2022, 25 years after IPO. Helium is #1 in DePIN revenue but only at $21M annually, which is Series B/C traction from a traditional startup perspective, not mature company territory.

Ultimately, many problems stem from the fact that token holders, unlike shareholders, have few ways to share in a company's growth upside. Buybacks briefly gained attention as a forced solution, but considering that no startup outside crypto does buybacks, it's difficult to expect buybacks to serve as anything more than a spark for short-term price increases.