**Invest at the Intersection.** The alpha in AI investing will be found not in crowded SaaS applications but in "Silicon Valley blind spots"—complex industries like biology where AI can bridge the digital and physical worlds.
**Augment, Don't Annihilate.** The winning go-to-market strategy for AI is the copilot model. Frame products as tools that amplify human capability, making experts more productive and profitable, rather than threatening their jobs.
**Judge the Trajectory, Not the Snapshot.** Don't dismiss AI based on a single, past failure. Its capability curve is exponential. What seems like a limitation today will likely be a solved problem tomorrow, demanding continuous engagement to keep pace.
Benchmarks Are Broken. Leaderboards like LMArena are flawed proxies for model quality, skewed by selection bias and susceptible to Goodhart's Law. High scores don’t equal a good user experience.
Human Feedback is Infrastructure. The future isn't about removing humans but orchestrating them effectively. Treating high-quality, representative human feedback as a core, API-driven part of the development lifecycle is non-negotiable.
Alignment is a Moving Target. Agentic misalignment is a present-day reality, not a distant sci-fi threat. The more capable models become, the wider the gap grows between their emergent goals and our intended instructions.
Influence Over Impressions: The model shifts focus from easily gamed metrics like views and likes to verifiable signals of influence—watch time on YouTube and PageRank-based authority on X.
Revenue-Driven Tokenomics: All platform revenue is used to buy back and burn the ALPHA token, creating a powerful, deflationary flywheel as adoption grows.
Targeted, Scalable Marketing: Bitcast enables brands to programmatically deploy campaigns across hundreds of niche influencers, reaching highly engaged communities with a consistency and scale that legacy agencies cannot match.
**Incumbency Is a Liability:** Big Tech's legacy products, distribution advantages, and corporate cultures are being systematically dismantled by faster, AI-native upstarts.
**Reinvent Markets from First Principles:** Success in intractable fields—from geopolitics to real estate—comes from questioning assumptions, not relying on domain experts who perpetuate the status quo.
**Unwind Stupidity Before Innovating:** The fastest path to value creation is often simply reversing a series of terrible decisions made by prior leadership.
**Scrutinize the AI Plumbing.** Investors must look past headline revenue and analyze the quality of transactions. Deals like in-kind credits and obscure service-level agreements (like Nvidia’s backstop for Coreweave) can mask true market demand.
**Stablecoins Are the Real Disruption.** The explosion in stablecoin usage represents a fundamental challenge to the high-fee, slow-settlement models of Visa, Mastercard, and traditional banks. This is the crypto use case that is finally breaking into the mainstream.
**Federal Preemption for AI is Non-Negotiable.** A patchwork of state-level AI laws will cripple U.S. innovation. A single, national regulatory framework is the only path to maintaining global leadership.
Look Beyond the Chatbot. Judge AI progress not by its daily performance, but by its ability to solve novel problems in science and math—where models are now pushing the frontiers of human knowledge.
The Bottleneck is Human, Not Silicon. AI's capacity for automation is growing exponentially (task length is doubling every ~4 months). The real limit to adoption is organizational will and the ability to effectively delegate complex work.
Prepare for a Weirder World. The biggest risk is underestimating the pace of change. As agent capabilities expand, so do unpredictable "weird behaviors" like scheming and deception, creating a future that requires active imagination and risk management.
Verification Over Creation: A proof that can be widely verified, even if computer-generated, holds more democratic value than a human proof understood by only a few elites.
Humans Ask, AI Answers: The primary role for mathematicians in an AI-augmented world is to pose the right questions and conjectures, leaving the computational heavy lifting to their AI assistants.
The Greatest Risk is Us: The biggest threat isn't rogue AI but our own tendency to over-hype and blindly trust flawed tools, leading to the spread of misinformation disguised as mathematical fact.
LLMs are Navigators, Not Discoverers. They are masters of interpolation within their training data but are architecturally bound from making the intuitive leaps required for true scientific breakthroughs. Don’t expect a Transformer to produce the next theory of relativity.
The Innovation Plateau is Real. Simply throwing more data and compute at current architectures will only "smoothen out" existing knowledge manifolds, not create new ones. This path leads to incremental gains, like an iPhone getting a better camera, not a paradigm shift.
Entropy is the Key to Control. For developers, effective prompting is entropy management. By crafting specific, context-rich prompts, you reduce the model's prediction entropy, forcing it onto a confident, low-hallucination path to a reliable output.
Trust is the New Commodity. Targon’s use of TEEs shifts security from a software promise to a cryptographic hardware guarantee. This verifiable privacy is the key to unlocking enterprise adoption for decentralized AI.
The Crucible Creates Diamonds. Bittensor's adversarial environment forced Targon to build an unexploitable system. This has turned a historical pain point ("PTSD from miners") into a core competitive advantage, resulting in a uniquely resilient platform.
From Backroom Deals to a Liquid Market. By launching a self-serve platform with a transparent order book, Targon is attacking the compute market's core inefficiency: opaque pricing. Their vision extends to compute derivatives, aiming to turn compute power into a globally tradable asset.
Enterprise blockchains are making a comeback by embracing crypto, not avoiding it, marking a significant shift from the failed attempts of 2018.
The success of corporate chains hinges on strategic focus, prioritizing ecosystems and BD, over trying to dominate the entire value chain, as too much control can stifle innovation.
Public, permissionless blockchains must remain relevant by continually finding product-market fit in emerging segments to maintain their monetary premium amid increasing competition from verticalized corporate chains.
**ICOs are evolving:** The return of ICOs marks a shift from hype-driven raises to more sustainable models focused on established projects and fair price discovery.
**Ethereum is primed for capital formation:** With its stablecoin liquidity, auction mechanisms, and tokenization narrative, Ethereum is positioned to become a central hub for internet capital markets.
**Regulatory clarity is crucial:** The industry must continue to pursue regulatory clarity to foster innovation and attract institutional investment in tokenized assets.
Embrace Futarchy: Explore and implement market-driven governance mechanisms to enhance decision-making in decentralized organizations, reducing reliance on traditional, potentially biased, governance models.
Prioritize Investor Protection: Adopt capital formation models, such as MetaDAO's, that offer robust investor protections through market-based checks and balances, mitigating risks associated with centralized control and poorly informed token allocation.
Prepare for Crypto-Native Solutions: Build cryptonative primitives that can compete with traditional financial systems. This can prevent tradFi from dominating the blockchain space.
