Google's AI Monopoly: Is It Inevitable?

The Spaces centers on Tig’s strategy to onboard top innovators and the emerging risk of a data monopoly around Google DeepMind’s Alpha Evolve. Host Sparta and John outline two innovator classes—code optimizers and mathematical method creators—and report that Tig has now met strict academic standards, enabling outreach via elite experts’ networks. They discuss Macrocosmos/APEX’s open implementation of automated innovators on Bittensor and a potential collaboration, favoring division of labor over Tig building a subnet itself. The core theme is Alpha Evolve: a new category of AI that develops novel algorithms and, per a recent Terrence Tao study, materially boosts frontier mathematics through interactive prompting. John warns that Alpha Evolve interactions elicit uncodified “inventive know‑how,” creating a data lock‑in dynamic akin to Google Search and potentially yielding a decisive, durable monopoly over the means of scientific knowledge production. The conversation argues for a decentralized, open protocol (like Tig) to keep algorithmic innovation accessible, using Linux as an exemplar of an open monopoly that benefits society, and emphasizes urgency, potential point‑of‑no‑return, and forthcoming detailed analysis from John.

Tig Twitter Space: Algorithmic Innovation, Google’s Alpha Evolve/Revolve, and the Stakes for Open Science

Participants and Roles

• Host: Sparta (Speaker 1). Facilitated the session, framed topics, handled community updates, and ran the giveaway.
• Guest: John (Speaker 2). Core strategist/technical lead for Tig; presented the conceptual and strategic analysis on innovator onboarding, automated innovators, and the Google/Alpha Evolve-Revolve landscape.
• Referenced individuals and entities:
  • Tibo (Montreal Polytechnic): contributing algorithms to Tig.
  • Terrence Tao: widely considered the world’s top mathematician; co-authored a recent paper leveraging Alpha Revolve via API access.
  • Stefan (CTO at Meta/Macro Cosmos; ex-physicist): working on an open “evolve” subnet on Bittensor; meeting scheduled.
  • Phil: collaborating with John on the forthcoming article.
  • Community mentions: Alex (thumbs up), and lottery winners wfgdt and r0.

Innovator Onboarding: Strategy, Roles, and Protocol Readiness

• Two primary classes of innovators Tig is onboarding:
  • Code optimizers (software engineers): specialize in speeding up and refining implementations (currently Rust is supported in the Innovation Game). Historic analog: John Carmack as a C++ optimizer. These innovators iteratively squeeze performance gains, often revisiting and surpassing others’ recent improvements.
  • Mathematical method inventors (scientists/mathematicians): create new low-level approaches that software engineers later implement; typically operate in academia or top-tier tech firms.
• Engagement dynamics:
  • The first group (code optimizers) joined early and continually improves algorithmic implementations.
  • The second group (mathematical inventors) has been a slower burn due to natural skepticism toward crypto and higher standards; Tig invested heavily in meeting those standards (documentation quality, benchmarking rigor, ranking criteria, protocol refinement).
• Outcome: Tig has now “met the bar” for top academics and domain experts. Some have already contributed domain-specific challenges and advised on protocol design/incentives. Tig is poised to leverage their networks for broader outreach—targeting quality over quantity.
• Key distinction in crypto context: Unlike typical crypto projects that engage blockchain-native experts (cryptographers, distributed systems, ZK) to solve crypto-internal problems (e.g., privacy invented to fix crypto’s public-by-default issue), Tig brings in domain experts from fields like optimization and imaging. These experts contribute within Tig to advance their fields directly—not to retrofit crypto’s own shortcomings.

Marketing Focus: Why Tig Targets Top Experts, Not Broad Retail

• Sparta emphasized the deliberate focus on an extremely narrow and elite audience: innovators capable of producing the next wave of state-of-the-art algorithms.
• Reason: Tig’s mission depends on algorithmic frontier progress; retail/DAO outreach is secondary to establishing credibility and utility for top-tier contributors.
• Analogy: Tiger Woods endorsing a superior golf club—one trusted signal to elite peers can catalyze adoption far more effectively than broad marketing.
• John concurred: One credible post/email from respected experts can have disproportionate impact if Tig continues meeting their standards.

Automated Innovators: Open Evolve vs Alpha Evolve/Revolve, and the APEX Subnet

• Sparta: Cosmos-linked team (Meta/Macro Cosmos) has launched “APEX,” essentially an open version of an automated innovator agent akin to Alpha Evolve/Open Evolve on Bittensor.
• John’s view:
  • The project was “begging to be launched” and appears to make everything open by default (details to be confirmed).
  • Macros: Tig itself was never going to become a Bittensor subnet; the intention was that someone should build an open-evolve-like subnet. If no one did, Tig might have done it as a separate project/token—not Tig shifting itself.
  • Collaboration: John already knows some team members; plans to speak with Stefan next week. Better that subnet specialists lead this; Tig focuses on its core.
• Sparta: This division of labor is beneficial—dedicated teams handle the Bittensor implementation while Tig concentrates on its protocol and incentives, collaborating where aligned.

Google’s Alpha Evolve/Revolve: Capabilities and What’s New

• Historical context: John’s earlier public talk (Cambridge, ~18 months ago) discussed Google’s algorithm-developing agents (e.g., FunSearch, Alpha Tensor) and the trajectory toward an AI-driven algorithm developer.
• Current state: “Alpha Evolve/Revolve” appears to be Google’s most advanced automated innovator/meta-optimizer for algorithm discovery and improvement.
• Terrence Tao study:
  • Google DeepMind granted API access to Alpha Revolve.
  • Tao’s team investigated whether Alpha Revolve could materially boost mathematician productivity on unsolved/blue-sky problems.
  • Results: Across ~64 problems, more than half saw new advances; the process was interactive—humans adjusted prompts/strategies, Alpha Revolve generated ideas, humans iterated.
• Crucial distinction versus LLMs:
  • LLM achievements (e.g., solving International Mathematical Olympiad problems) are significant but operate on known-problem spaces.
  • Frontier research requires navigating unknown territory; Alpha Evolve/Revolve constitutes a new category—a meta-optimizer producing novel algorithms and strategies, not merely interpolating known knowledge.

The “Mother of All Datasets”: Inventive Know-How and Getting Unstuck

• John’s core thesis: The most valuable dataset is the uncodified, context-specific “inventive know-how”—strategies scientists use to get unstuck when pursuing problems with no known solutions.
• Why it’s not in papers:
  • Technical literature presents assumptions-to-results, not the iterative, messy strategy shifts, heuristics, wrong turns, and context-dependent judgments used to find those results.
  • Writing down full strategy traces is laborious, often unintelligible to outsiders, and vastly larger than final results.
  • Consequently, it has never been captured at scale, and thus never ingested by LLMs.
• What’s different now:
  • Alpha Evolve/Revolve can elicit and process this data during human-agent interactions (prompting, strategy adjustment, feedback loops).
  • The “means of production” of knowledge—these strategy traces—is even more valuable than isolated results.

Data Lock-In Dynamics: Why Alpha Evolve/Revolve Can Create a Monopoly

• Analogy to Google Search:
  • Users interacting with the best product generate data that makes it better—attracting more users, creating a feedback loop, and denying competitors the data needed to catch up.
  • Historical misstep: Yahoo and others backend-powered by Google’s algorithm, unintentionally feeding Google the key data while not realizing data was the asset.
• Why LLM prompts didn’t produce similar lock-in:
  • Scientists don’t use LLMs to create new knowledge at the frontier; LLMs excel at interpolation of known domains.
  • Data lock-in emerges in the category that actually produces novel strategies/results: the algorithmic coding agent/meta-optimizer (Alpha Evolve/Revolve).
• Strategic implications:
  • If Google maintains a lead and funnels the world’s top experts through Alpha Evolve/Revolve, it could monopolize the “inventive know-how” dataset.
  • This is the easiest and most imminent route to monopoly in AI because it targets the engine of scientific progress itself.
  • John cautions that the point of no return may be close or already passed—echoing how competitors became “zombies” long before realizing Google had won search via data advantage.

Why Open, Decentralized Algorithmic Research Is Existential

• Sparta’s framing: Algorithms define the ceiling of possibility across science/industry (drug design, materials, propulsion, etc.). Better algorithms shift civilization’s trajectory.
• John’s position:
  • The value at stake here dwarfs consumer ad targeting; it’s the means of producing scientific knowledge.
  • Monopolistic outcomes are likely due to natural data dynamics. The remedy is not “no monopoly” but ensuring any monopoly is open—akin to Linux in operating systems.
  • Linux shows an open collaborative project can outcompete proprietary rivals, achieving a durable lead that cannot be closed retroactively due to license structures.
  • For algorithm production/mining, an open protocol ensures perpetual accessibility and guards against unilateral closure (unlike OpenAI’s path from open to closed).
• Crypto relevance:
  • Sparta rejects decentralization maximalism but argues some domains must be decentralized because societal fate depends on it.
  • Tig’s mission—decentralize algorithmic science and incentives, keep frontier knowledge production open—meets that bar.

Tig’s Role and Urgency

• Sparta: Tig is one of crypto’s most societally consequential missions. If Tig “wins,” an open monopoly best aligned with global benefit could emerge, preventing oligarchic capture.
• John: The decentralized AI community is ideologically aligned, but the time horizon is tighter than many realize. Awareness and action are critical—before data lock-in crystallizes around a single closed entity.

Community Updates and Next Steps

• Collaboration: John will speak with Stefan (Meta/Macro Cosmos) next week regarding the open-by-default “APEX” subnet on Bittensor; no promises, but alignment looks strong.
• Article: John is finalizing an exposé (reviewing with Phil) clarifying the lock-in dynamics and the Alpha Evolve/Revolve strategy; once seen, “you can’t unsee it.” Community is encouraged to read and share widely.
• Giveaway winners: wfgdt and r0. Sparta will coordinate token claims.

Key Takeaways and Action Items

• Frontier algorithmic development is a new AI category distinct from LLMs; Google’s Alpha Evolve/Revolve currently leads.
• The most valuable dataset—the “mother of all datasets”—is inventive know-how captured through human-agent interactions during frontier problem-solving.
• Data lock-in at the meta-optimizer layer can produce a durable monopoly over the means of producing scientific knowledge.
• To avoid dystopian outcomes, a robust open protocol for algorithm production (Tig’s mission) must reach escape velocity—onboarding top innovators, refining incentives, and leveraging open collaborations (e.g., with Bittensor subnets).
• Immediate actions:
  • Read and disseminate John’s upcoming article to raise awareness of the lock-in mechanism.
  • Support Tig’s outreach to top-tier academic networks; credibility and quality standards are now met.
  • Encourage collaboration with specialized subnet teams (Meta/Macro Cosmos APEX) to accelerate open automated innovator tooling while Tig focuses on core protocol and incentives.