Rosa Del Mar

Issue 33 2026-02-02

Rosa Del Mar

Daily Brief

Issue 33 2026-02-02

Crypto Policy Posture: Alleged Debanking And Shifting Legislative Landscape

Issue 33 Edition 2026-02-02 9 min read
General
Sources: 1 • Confidence: Medium • Updated: 2026-02-06 16:59

Key takeaways

  • Ben Horowitz identifies the “Clarity Act” as a pending crypto market-structure bill intended to define how different token types are classified under rules.
  • David Solomon says Goldman spent about $6B on technology last year and could not spend $8B without reducing returns, implying efficiency savings are needed to increase investment while maintaining performance.
  • Ben Horowitz flags copyright treatment for AI training—whether models may learn from copyrighted works without reproducing them—as a key upcoming policy issue affecting U.S. AI strength versus China.
  • Ten years ago Goldman Sachs was the largest wholesale funder in the world and has since prioritized moving away from reliance on wholesale funding toward more stable sources.
  • David Solomon warns that the shift back toward multipolarity increases the risk of a geopolitical shock that slows growth compared to the post–Cold War era.

Sections

Crypto Policy Posture: Alleged Debanking And Shifting Legislative Landscape

The corpus contains allegations that crypto was suppressed through informal executive/regulatory pressure and debanking tactics, and reports Wells notices received by portfolio companies. It also asserts that two bills are now law and flags a separate market-structure bill as pending amid political “drama.” These are high-salience deltas but remain unresolved/disputed in this corpus because no bill text, enactment confirmation, or countervailing evidence is provided.

  • Ben Horowitz identifies the “Clarity Act” as a pending crypto market-structure bill intended to define how different token types are classified under rules.
  • Ben Horowitz claims the prior U.S. administration effectively banned parts of the crypto industry via executive pressure rather than legislation or formal legal process, including the use of debanking tactics.
  • Ben Horowitz asserts the prior U.S. administration effectively banned crypto via non-legislative regulatory pressure (including debanking and enforcement actions) and frames this as abusive use of government power.
  • Ben Horowitz argues the Biden administration treated essentially all tokens and even some NFTs as securities, citing enforcement actions he describes as extreme.
  • Ben Horowitz says there has been “drama” around the Clarity Act and that it is currently being pushed for passage.
  • Ben Horowitz states that the Genius Act and a Stablecoin Bill passed and are now law.

Enterprise Ai Adoption In Regulated Institutions: Experimentation Vs Process Reengineering Under Constraints

The corpus distinguishes two enterprise AI paths at Goldman: distributing tools to employees for experimentation and reengineering core processes for automation with reinvestment of savings. It adds a binding constraint (regulatory clearance slows deployment) and a governance constraint (process change threatens “empires,” requiring top-down drive). A concrete economic boundary is provided via tech spend (~$6B) and the claim that moving to ~$8B would reduce returns absent efficiencies.

  • David Solomon says Goldman spent about $6B on technology last year and could not spend $8B without reducing returns, implying efficiency savings are needed to increase investment while maintaining performance.
  • David Solomon says Goldman launched “1GS 3.0” to reimagine six specific firm processes and describes the resulting capacity impact as significant but not publicly quantified.
  • David Solomon says Goldman’s first AI focus is broadly distributing tools and models to employees so they can experiment and find productivity gains in client work.
  • David Solomon says the more consequential AI opportunity is reimagining core enterprise operating processes for automation and efficiency and then reinvesting savings into growth areas.
  • David Solomon says large-scale process reimagination is difficult because it threatens existing organizational “empires” and therefore must be driven top-down.
  • David Solomon says regulatory clearance requirements significantly slow Goldman’s ability to deploy AI tools compared with companies that can “just try it.”

Ai Policy Design Constraints: Application Regulation, State Patchwork, And Copyright

The corpus frames AI regulation as a competitiveness lever, advocating application-level regulation rather than regulating model development, and warning that 50-state fragmentation could be prohibitive for new entrants. It also identifies copyright rules for training as a key upcoming uncertainty affecting AI capability development. These claims describe constraints and proposed policy principles rather than documenting enacted rules.

  • Ben Horowitz flags copyright treatment for AI training—whether models may learn from copyrighted works without reproducing them—as a key upcoming policy issue affecting U.S. AI strength versus China.
  • Ben Horowitz warns that heavy-handed AI regulation or bans could cause the U.S. to lose the AI race to China with long-term strategic consequences.
  • Ben Horowitz says a patchwork of 50 state-level AI laws would make it effectively impossible for new companies to comply and innovate.
  • Ben Horowitz advocates regulating AI applications rather than regulating model development itself, summarized as “don’t regulate math.”
  • Ben Horowitz warns that banning AI or restricting the underlying mathematics would cause the U.S. to lose the AI race to China with century-scale implications.

Banking Resilience And Competitive Position: Scale And Funding-Base Shift

The corpus asserts scale as a strategic requirement in mature financial services and describes a large structural funding change: moving away from wholesale funding toward deposits (quantified at ~$500B, ~40% funding). The expectation that further balance-sheet scale is needed is present but not substantiated with a specific plan or external evidence in this corpus.

  • Ten years ago Goldman Sachs was the largest wholesale funder in the world and has since prioritized moving away from reliance on wholesale funding toward more stable sources.
  • Goldman Sachs shifted from having zero deposits 15 years ago to about $500B in total deposits, including a digital deposit platform with over $200B, and deposits now fund roughly 40% of the firm.
  • In mature financial services businesses, scale provides leverage and latitude during turbulence, making scale a central long-term strategic requirement for Goldman Sachs.
  • Goldman Sachs believes it must continue increasing balance-sheet scale over the next 5–15 years because it is currently far smaller than JPMorgan and organic scale-building is difficult in mature businesses.

Macro/Capital-Markets Outlook: Stimulus Mix, Concentration, And Geopolitical Risk

The corpus presents a coherent macro mechanism (multiple stimulus channels making growth hard to slow) and an additional claim about concentrated capex contributing materially to GDP growth. It also flags geopolitics/multipolarity as a key downside risk that could disrupt the favorable setup. Several elements are presented as assessments or quantified assertions without corroboration inside the corpus.

  • David Solomon warns that the shift back toward multipolarity increases the risk of a geopolitical shock that slows growth compared to the post–Cold War era.
  • David Solomon argues a “cocktail of stimulus” (fiscal stimulus, a rate-cutting cycle, a capital investment supercycle, and deregulatory unwind) makes the U.S. economy hard to slow.
  • David Solomon states that last year the four largest companies contributed about 1% to U.S. GDP growth through roughly $400B of spending.
  • David Solomon views the current U.S. macro setup for investable and financial assets as the best “sweet spot” he has seen in decades despite substantial global complexity.

Watchlist

  • David Solomon warns that the shift back toward multipolarity increases the risk of a geopolitical shock that slows growth compared to the post–Cold War era.
  • Ben Horowitz flags ongoing uncertainty and aggressiveness from the FTC (including toward smaller tech deals) as a potential constraint that could shift M&A toward IP-style transactions rather than traditional acquisitions.
  • Ben Horowitz identifies the “Clarity Act” as a pending crypto market-structure bill intended to define how different token types are classified under rules.
  • Ben Horowitz says there has been “drama” around the Clarity Act and that it is currently being pushed for passage.
  • Ben Horowitz warns that heavy-handed AI regulation or bans could cause the U.S. to lose the AI race to China with long-term strategic consequences.
  • Ben Horowitz flags copyright treatment for AI training—whether models may learn from copyrighted works without reproducing them—as a key upcoming policy issue affecting U.S. AI strength versus China.
  • David Solomon questions whether models trained on widely available information can produce differentiated investment outperformance.

Unknowns

  • What are the specific six processes targeted by Goldman’s “1GS 3.0,” and what measurable capacity, cost, error-rate, or cycle-time changes have resulted?
  • How stable are Goldman’s deposits under stress, what is the cost of those deposits relative to wholesale funding, and what portion of deposits are operationally sticky versus rate-sensitive?
  • Is the claim that a16z raised ~18.3% of all U.S. venture capital in 2025 accurate under a consistent definition of “venture capital raised,” and what is the denominator/source?
  • Which four companies are referenced in the claim about ~$400B spend contributing ~1% to U.S. GDP growth, and what accounting links that spending to GDP contribution?
  • What concrete regulatory changes or clearance processes are the main blockers for Goldman’s AI deployment, and what is the typical approval timeline?

Investor overlay

Read-throughs

  • If the Clarity Act advances, token classification could become more predictable, potentially shifting compliance and product planning for crypto platforms and service providers; current uncertainty and political drama keeps timelines and outcomes unclear.
  • If Goldman can fund higher tech investment mainly via efficiency savings and process reengineering, operating leverage could improve; deployment is constrained by regulatory clearance and internal governance resistance.
  • If AI copyright rules allow training on copyrighted works without reproducing them, US model development could face fewer constraints; restrictive rules could raise costs and slow capability progress versus global competitors.

What would confirm

  • Public legislative movement on the Clarity Act such as committee actions, floor scheduling, or enacted definitions of token categories and agency jurisdiction that reduce classification ambiguity.
  • Evidence of measurable productivity from specific reengineered processes at Goldman such as reduced cycle times, errors, or unit costs alongside steady or rising tech spend without return compression.
  • Policy outcomes or court rulings clarifying whether AI training on copyrighted works is permitted under specified conditions, reducing legal uncertainty for model developers and enterprise adopters.

What would kill

  • The Clarity Act stalls, is materially diluted, or triggers conflicting interpretations that preserve uncertainty about token classification and enforcement scope.
  • Goldman cannot obtain timely regulatory clearance for AI in core processes or cannot realize efficiency savings, forcing higher tech spend to pressure returns or limiting reinvestment capacity.
  • Copyright policy or rulings restrict training on copyrighted works broadly, increasing licensing burden or litigation risk and slowing model development and enterprise deployment.

Sources