Amid Venezuela's suspicious @Polymarket win, Rep. Ritchie Torres is fast-tracking his bill to bar officials from trading on nonpublic info. Here's why getting prediction market rules right could shape their future as trusted tools — or exploits for our political elites.👇 ~~ Analysis by @sachitakamura ~~ The Political Case The argument for restricting public officials is straightforward: if politicians can legally profit from bets on outcomes they directly influence or have advance knowledge of, it twists incentives and erodes the already near-record low public trust in the U.S. government. This dynamic already plays out in traditional securities markets, where the STOCK Act of 2012 was supposed to address congressional insider trading. The results have been underwhelming. Despite the law's existence, examples of suspicious trading by members of Congress have continued to surface with regularity: - Senator Richard Burr sold $1.7M in stock immediately following a classified COVID-19 briefing; the DOJ later dropped the investigation without charges. - Senator Kelly Loeffler offloaded millions in assets after the same confidential pandemic warning, yet faced no legal consequences when federal probes concluded. - Senator Tommy Tuberville traded millions in defense contractor stocks and violated the STOCK Act's reporting deadline 132 times, yet faced no significant consequences. Since the STOCK Act passed in 2012, not a single member of Congress has been prosecuted under its provisions, while the penalty for concealing trades is a trivial $200 fee, which ethics committees routinely waive. Prediction markets present an even more direct temptation. Unlike stock trading, where connections between policy decisions and price movements can be complex and deniable, prediction markets offer explicit bets on government actions. Will a military intervention occur? Will a bill pass? The path from insider knowledge to profit proves incredibly clear. While the specifics are still unclear, @RitchieTorres bill reportedly extends STOCK Act principles to prediction markets, hopefully with greater, more meaningful enforcement. Legal frameworks matter and must be established. Without clear rules explicitly covering prediction markets, prosecuting suspicious trades becomes even harder. Why It Matters for Prediction Markets The broader issue extends way beyond politicians. Prediction markets generated over $44B in combined trading volume in 2025. They've proven their value as information aggregation tools — Polymarket's accuracy during the 2024 election cycle demonstrated what these platforms can do when they function properly. Functionally, insider participation doesn't necessarily break these markets. The transparency of blockchain-based platforms means suspicious positions are visible. Traders can tail wallets showing unusual activity. Information still gets priced in, even if the source is questionable. But reputation is a different matter. Prediction markets are still fighting for legitimacy with regulators, institutions, and the broader public. If the prevailing narrative becomes that these platforms are just another vehicle for connected insiders to profit from privileged information, the policy progression and mainstream adoption get harder when every major market move triggers headlines about who knew what and when. There's also a pragmatic concern: if today's broadly crypto-friendly regulators don't work with platforms to address these issues, hostile administrations of the future could do so with a much heavier hand. The window for self-regulation and productive collaboration is now. The Path Forward None of this means prediction markets need heavy-handed regulation across the board. Skepticism toward regulatory overreach is warranted. But there's a meaningful difference between resisting regulatory capture and acknowledging that certain narrow restrictions serve everyone's interests. Legally barring public officials from betting on outcomes they can influence falls squarely in the latter category. Few believe politicians should have new avenues to monetize their positions. The broader crypto community, which arose in part as a check against establishment abuse, has reason to support exactly this kind of accountability. We don't yet know the full details of Torres's bill. The specifics will matter. But the direction is right. Prediction markets work because they aggregate dispersed information into prices, and that function can survive some insider activity. The bigger risk is reputational: repeated incidents of apparent insider trading invite the kind of regulatory scrutiny that could constrain the industry far more than targeted rules around public officials ever would. The honest reality is that this behavior will likely continue regardless of what rules get passed. Enforcement is hard. Proving intent is harder. But there's much to be said for establishing clear norms and for the transparency that blockchain-based markets provide. Every trade on Polymarket is visible. Wallet activity can be tracked. The same infrastructure that enables suspicious trades also enables scrutiny of them. Researchers and journalists can monitor for patterns. Communities can call out suspicious activity in real time. These are formative years for these technologies, which, if stewarded well, will reshape how we aggregate information about uncertain futures. Getting the foundations right matters. Ensuring that government officials can't exploit these tools for personal profit seems like a reasonable place to start.

“What if AI gives us a once-in-a-generation chance to rebuild the internet from the ground up?” @kenzi_mori sits down with Karan Sirdesai @karansirdesai, Founder of @miranetwork, to unpack the founder journey behind Mira and the early conviction that AI would become far more than another tech cycle. They trace Karan’s path from teaching himself to build, sending cold DMs, experimenting with crypto arbitrage, and working alongside figures like Balaji Srinivasan and Sandeep Nailwal, to spotting the AI shift before it became obvious to the broader market. The conversation also explores why Karan has consistently chosen the unconventional route, how his time at Accel exposed him to frontier AI companies early, and why the pace of AI progress made one thing clear: this wouldn’t just change startups or software, it would reshape how humans work, think, create, and interact with the internet itself.

“What if the most important AI infrastructure isn’t the biggest model — but the one you can actually trust?” @sachitakamura sits down with Karan Sirdesai @karansirdesai, Founder of @miranetwork, to explore how Mira is approaching decentralized AI from a more focused angle: making machine intelligence reliable enough for real-world use. They discuss why Mira is not trying to become a full-stack AI protocol, how its approach differs from networks like Bittensor, Ritual, and Sahara, and why focusing on trust and verification could make Mira a stronger fit inside the wider AI infrastructure stack. The episode also breaks down Mira’s reliability architecture, from its core verification layer to node-level consensus, and why the next evolution of AI APIs may not be about simply accessing more models, but about knowing whether the answers you receive can actually be trusted.

“What happens when AI stops being a tool and becomes the operating layer of the internet?” @dikshananduri sits down with Karan Sirdesai @karansirdesai, Founder of @miranetwork, to explore a future where AI is no longer just answering questions, but taking action, making decisions, and handling large parts of our cognitive workload. They discuss the upside of this shift: the rise of “infinite hires,” a world where most knowledge work can be automated or amplified, and a productivity wave that could completely reshape how companies, teams, and individuals operate. But the conversation also gets into the darker side of that future. As AI becomes more capable, the risks grow with it: blind dependence, high-stakes failures, malicious autonomous agents, and the possibility of humans sharing the world with a new kind of intelligence we are not fully prepared to control.

This week on the podcast, we’re joined by Karan Sirdesai @karansirdesai, Co-Founder and CEO of Mira Network (@miranetwork). We cover Karan’s path from his university days and early crypto experiments to building Mira, an infrastructure layer focused on making AI more reliable, verifiable, and safe to use at scale. The episode goes deep into one of AI’s biggest unsolved problems: hallucinations. Karan breaks down why unreliable outputs are such a major blocker for real-world adoption, especially in high-stakes areas like finance, healthcare, and other trust-sensitive industries. We also discuss the early “aha” moment behind Mira, shaped by experiments with GPU rentals, AI workflows, and the insight that multiple models could work together to verify outputs through consensus. Karan also shares how working with Balaji Srinivasan, his unconventional founder journey, and Mira’s AI + crypto-native team influenced the company’s mission: building decentralized infrastructure that helps make artificial intelligence more trustworthy, dependable, and useful in the real world.