Personalization is the Killer App. The model’s breakthrough feature was zero-shot character consistency, creating an emotional connection that drove viral adoption. It proves utility is unlocked when technology feels personal.
Focus on the Floor, Not the Ceiling. The next wave of value will come from improving the worst-case outputs, not just the best. This "lemon picking" is essential for building trust and enabling reliable, real-world applications beyond creative tinkering.
Art is Intent; Models are Tools. AI’s role is to automate tedium, not replace creativity. The most compelling work will continue to come from skilled artists who use models to execute a specific vision, proving that the human with the idea remains irreplaceable.
AI's Blind Spot is Unwritten Knowledge. The biggest barrier for AI in advanced problem-solving is accessing the "folklore" knowledge and intuition that experts build over a career but never write down.
The Future of Math is a Promotion, Not Obsolescence. AI will act as a powerful assistant that handles rote tasks, pushing mathematicians to focus exclusively on creative and abstract thinking.
The Next Revolution is AI-Powered Verification. Automated formal proof systems like Lean have the potential to eliminate errors from research papers, transforming peer review from a check on correctness to a judgment on a paper's novelty and impact.
AI's Blind Spot is "Folklore": The next great challenge for AI isn't raw calculation, but acquiring the unwritten, intuitive "folklore knowledge" that separates experts from students.
Mathematicians Become Creative Directors: As AI handles the technical grind, the human role in mathematics will shift from execution to creative direction—formulating novel problems and abstract models.
The End of Errors: Formal verification tools like Lean, powered by AI translators, are on the verge of revolutionizing math by creating a fully verifiable, error-free database of human knowledge, changing how proofs are published and reviewed.
AI Needs a Referee. Agents are programmed to win, not necessarily to follow the rules. Their tendency to "game the system" makes external, on-chain verification protocols essential for alignment and trust.
Trading is Just the Tip of the Spear. Crypto trading is the perfect initial use case due to its clear, objective metrics. The real goal is a decentralized "skill marketplace" where any organization can fund a competition to find the best agent for any task.
The Platform War is Here. A battle is unfolding between closed ecosystems like OpenAI, which aim for platform lock-in, and an open, decentralized future. This creates a massive opportunity for neutral evaluation layers to become the definitive source of truth for AI performance.
AI's Blind Spot is "Folklore Knowledge." AI excels at digesting published literature but fails on problems requiring unwritten, community-held intuition, which remains a key human advantage for now. Jitomirskaya predicts her problem will take AI 10-20 years to solve.
Mathematicians Won't Be Replaced, They'll Be Upgraded. The future role of a mathematician is less about routine work and more about creative problem formulation. AI tools like Lean will handle verification, shifting peer review from "Is it correct?" to "Is it interesting?"
Math May Become a Sport. If AI eventually masters creativity, the human practice of mathematics may persist like chess—an activity pursued for its intrinsic value and intellectual challenge, even if a machine is the undisputed world champion.
Folklore Knowledge is AI’s Next Frontier. The true test for advanced AI in abstract fields is not solving problems from a textbook but acquiring the unwritten, intuitive knowledge that experts possess.
Automated Proof Verification Will Reshape Research. Within years, tools like Lean, powered by AI translators, will create a verifiable database of all mathematics, fundamentally changing how papers are published and refereed.
Human + Machine is the New Paradigm. AI will become an indispensable assistant, automating routine work and pushing mathematicians to focus on what humans still do best: true creativity and formulating entirely new models.
**The "Folklore" Bottleneck:** AI's primary limitation isn't complex logic but its inability to access uncodified, expert intuition—the "folklore" that guides human problem-solving.
**Automation Breeds Creativity:** As AI handles routine calculations and arguments, the value of human mathematicians will shift entirely to creative and abstract thinking, raising the bar for what constitutes a meaningful contribution.
**Proof Verification Is the Next Revolution:** The most immediate and profound change in mathematics will be AI-driven, formally verified proofs, which will guarantee correctness and reshape the entire publishing and peer-review landscape.
AI's Next Frontier is Unwritten Knowledge. AI has mastered logic, but its true test is acquiring the implicit, "folklore" knowledge that experts use intuitively but rarely write down.
Human Mathematicians Will Become Purely Creatives. As AI automates routine calculations and arguments, the role of a mathematician will shift entirely to high-level abstraction, creative problem formulation, and intuition.
Formal Verification Will Revolutionize Publishing. The combination of AI translators and formal proof assistants like Lean will soon make it possible to automatically verify all mathematical papers, fundamentally changing how research is validated and published.
Life is a Process, Not a Substance. Stop defining life by its carbon-based hardware. The most fundamental properties of life are functional and informational processes that can manifest on any substrate, including human minds (culture) or silicon (AI).
Physics is the Great Equalizer. While the universe may teem with diverse biochemistries, all life is governed by the same physical constraints. These universal laws make life predictable at a macro level, creating evolutionary targets and forcing convergent solutions.
Evolutionary Leaps Aren't Random. Major transitions in life’s complexity, like the emergence of multicellularity, are often responses to hitting a hard physical wall, frequently triggered by radical environmental change. Evolution innovates most profoundly when its back is against the wall.
The commodification of AI compute, driven by decentralized networks, is shifting power from centralized data centers to globally distributed, incentive-aligned miners. This creates a more efficient, resilient, and cost-effective foundation for intelligence.
Explore building AI agents and applications on Shoots' expanding platform, leveraging their TEEs and end-to-end encryption for privacy-sensitive use cases. The "Sign in with Shoots" OAuth system offers a compelling way to integrate AI capabilities without upfront compute costs.
Shoots is not just an inference provider; it's building the foundational infrastructure for a truly decentralized, private, and intelligent internet. Over the next 6-12 months, expect to see a proliferation of sophisticated AI agents and applications built on Shoots, driven by its unique blend of incentives, security, and global compute.
The Macro Shift: Ethereum pivots from a "rollup-centric" vision to a multi-faceted approach: a powerful, ZKVM-scaled L1 coexists with a diverse "alliance" of specialized L2s. This adapts to technical realities and renews L1's core focus.
The Tactical Edge: Builders should prioritize differentiated L2 solutions or contribute to L1's ZKVM scaling. Investors should evaluate L2s based on distinct utility and symbiotic relationship with Ethereum.
The Bottom Line: Ethereum's market leadership remains, but this pivot signals a pragmatic roadmap. The next 6-12 months will see rallying around L1 ZKVM scaling and clearer L2 roles, demanding sharper focus on where value accrual and innovation occur.
Global liquidity is high, but capital is reallocating from speculative crypto to traditional stores of value and, paradoxically, to DeFi platforms offering RWA exposure. This signals a maturation where utility and transparency are gaining ground over pure hype.
Identify protocols with demonstrable revenue generation from real-world use cases, like Hyperliquid, as potential outperformers. Focus on platforms that offer transparency and accountability, as market structure shifts towards more regulated and predictable venues.
The crypto market is undergoing a structural reset, moving away from a retail-driven, speculative cycle. Investors must adapt to a landscape where fresh capital is scarce, institutional flows favor gold, and DeFi's next frontier involves real-world assets.
The convergence of AI agents and programmable money is creating a new frontier for digital commerce and liability. This shift demands a proactive re-evaluation of regulatory frameworks, moving beyond human-centric definitions of accountability and transaction.
Builders should design AI agent systems with cryptographically embedded controls, allowing for granular policy enforcement (e.g., spending limits triggering human review) and leveraging stablecoins for microtransactions in decentralized agent-to-agent economies.
The next 6-12 months will see increasing pressure to define AI agent liability and payment rails. Investors should prioritize projects building infrastructure for secure, auditable agent commerce, while builders must integrate compliance and control mechanisms from day one to navigate this evolving landscape.
The economy is shifting from human-centric labor and scarcity to AI-driven abundance, where machine intelligence itself becomes the primary unit of economic exchange, challenging traditional monetary and employment structures.
Investigate and build "proof of control" solutions using crypto primitives (like ZKPs, TEEs, decentralized compute/storage) to secure AI agents and data.
The next 6-12 months will see increased demand for verifiable control over AI systems. Understanding how crypto enables this, and how human value shifts from transactional jobs to unique human interaction, is crucial for navigating this new economic reality.
AI's productivity boom is redirecting capital from financial engineering (buybacks) in large-cap tech to physical infrastructure (data centers, hardware).
Reallocate capital from over-concentrated, buyback-dependent large-cap tech into AI infrastructure plays (hardware, energy), commodities, and potentially regional banks, while actively managing duration risk in bonds.
The market's underlying structure is cracking. Passive investment in broad tech indices will likely yield poor real returns.
