Investing FAQ

The legal status is genuinely unsettled. SEC Rule 10b-5 does not apply because prediction market contracts are swaps, not securities. CFTC Rule 180.1 prohibits trading on material nonpublic information but requires proof of a breached pre-existing duty, which maps awkwardly onto prediction markets. The strongest enforcement tool may be criminal wire fraud (18 U.S.C. § 1343). At the Securities Enforcement Forum in February 2026, SDNY U.S. Attorney Jay Clayton said prediction market participants are not beyond fraud statutes and to expect enforcement actions.

A wallet called AlphaRacoon deposited $3 million on December 3, 2025, went 22 for 23 on Google Year in Search predictions, and made $1.15 million in under 24 hours. The same wallet previously made over $150,000 correctly predicting the exact launch window of Google's Gemini 3.0. The wallet was later renamed to 0xafEe but was re-identified by Compound AI's detection system.

In February 2026, Israeli authorities indicted two people, a military reservist and a civilian, for using classified intelligence to bet on Polymarket during Israel's 12-day war with Iran in June 2025. An account called ricosuave666 placed seven bets on strike timing and got every one correct, earning approximately $150,000. This is the first criminal prosecution tied to prediction market insider trading.

A February 2026 FEDS working paper by Diercks, Katz, and Wright titled 'Kalshi and the Rise of Macro Markets' found that Kalshi's macro markets perform as well as or better than traditional forecasting tools like fed funds futures and professional surveys, with a perfect modal forecast record on the day before every FOMC meeting since 2022. The paper describes prediction markets as a complement to existing tools, not a replacement, and does not represent official Federal Reserve policy.

Insider trading creates adverse selection: if insiders consistently win, uninformed participants recognize they are systematically losing to better-informed counterparties and withdraw. Market makers widen spreads or exit. This reduces liquidity, which reduces the market's ability to aggregate information. The accuracy gain from one insider's trade is more than offset by the participation loss from the traders who leave. This is George Akerlof's 'market for lemons' dynamic applied to financial markets.

DRW is building a dedicated prediction markets desk with base salaries of $175,000-$200,000. Susquehanna became Kalshi's first official market maker. Jump Trading is taking equity stakes in both Kalshi and Polymarket for liquidity provision. Goldman Sachs CEO David Solomon disclosed meeting leadership of both platforms. Tyr Capital, a Swiss crypto hedge fund, is hiring prediction market traders.

On January 2, 2026, an account called Burdensome-Mix, created less than a week earlier, placed $33,934 across 13 bets that Maduro would be removed from power. Less than an hour after the final bet, Trump ordered a military strike. The account returned $436,759. Chainalysis found the trader cashed out through mainstream U.S. exchanges with no effort to hide their identity. The trader has never been identified.

The CFTC has Rule 180.1, modeled on the SEC's Rule 10b-5, which prohibits trading on material nonpublic information. But it requires proof of a breached pre-existing duty, which maps awkwardly onto prediction markets. The CFTC has brought zero enforcement actions for prediction market insider trading. Chairman Michael Selig announced new rulemaking in January 2026 but has not addressed insider trading specifically.

The immediate catalyst was Anthropic releasing 11 open-source plugins for Claude Cowork on January 30, 2026, covering Legal, Sales, Marketing, Finance, and other departments. Thomson Reuters fell 16% and LegalZoom fell 20% in a single session. The broader IGV software ETF dropped 32% from its September 2025 peak to a low of $79.65, with roughly $2 trillion in market cap destroyed.

Bank of America's Vivek Arya identified a logical inconsistency where investors are simultaneously punishing hyperscaler stocks because AI capex might generate weak returns, while destroying software stocks because AI will be so pervasive it replaces all existing software. Both cannot be true. If AI tools are not generating ROI, they are not replacing enterprise software. If they are replacing enterprise software, the hyperscalers are earning extraordinary returns.

The Russell 1000 Software subsector trades at 32.4x forward earnings versus 43.6x for Semiconductors, an 11.2x gap that has not persisted historically. Recurring-revenue businesses with 90%+ gross margins and 95%+ renewal rates now carry a lower multiple than cyclical chipmakers with 40-60% margins and concentrated customer bases.

Only Microsoft generates cash from operations (net of dividends and buybacks) in excess of capital expenditure in FY2026, with roughly $110B in cash versus $105B in capex. Alphabet, Amazon, Meta, and Oracle are all capex-negative, with Oracle showing the widest gap at $20B cash versus $50B capex.

No. The risk varies widely by category. Deterministic, mission-critical systems like ERP, cybersecurity, and observability face low disruption risk and will likely absorb AI as additive capability. Probabilistic workflow tools like content creation, tier-1 support, and basic analytics face genuine existential risk. The market is pricing the entire software stack as if every category faces the same threat, which is a category error.

No. Q4 2025 earnings showed resilient or accelerating growth across major software names. ServiceNow grew subscription revenue 21%, Palantir grew 70.5%, Datadog grew 29%, and the sector is delivering 17% aggregate earnings growth in 2026. Every major name beat consensus estimates.

Goldman Sachs Research projects the application software market growing to $780 billion by 2030 at a 13% CAGR, with agents accounting for over 60% of the total. a16z argues the addressable market expands from roughly $350 billion in enterprise software spend to the $6 trillion white-collar services market if AI transitions from productivity tools to completing work itself, a roughly 20x TAM expansion.

The answer depends on which metrics you prioritize. a16z points to 80% GPU utilization and $40.6 billion in annual revenue from top AI companies as evidence of real demand. AQR counters that the U.S. CAPE ratio sits at the 96th percentile since 1980, historically associated with low future returns. Both have data supporting their view.

The CAPE ratio measures price relative to 10-year average inflation-adjusted earnings. At current levels around 40x, it suggests U.S. large cap equities may return roughly 3.9% annually over the next 5-10 years, well below the long-term average of 5% since 1900, according to AQR's capital market assumptions.

Yes, this is one way to reconcile the bullish and bearish views. Individual AI winners generating real revenue and high utilization could deliver spectacular returns even as broad market indices produce below-average results due to compressed risk premia. The question becomes whether you're buying the index or picking the winners.

Both sources have financial incentives influencing their perspectives. a16z manages billions in venture capital and growth equity, so bullish AI narratives support their portfolio valuations. AQR runs systematic strategies that benefit when investors diversify away from concentrated U.S. tech exposure toward international equities and alternatives.

Tech P/E multiples currently sit around 30-35x, elevated but nowhere near the 70-80x of 2000. More importantly, GPU utilization runs at 80% compared to just 7% for fiber optic cables during the dotcom era. The fundamentals appear stronger, though valuations remain historically high.