The Macro Shift: From Model-Centric to Eval-Centric. The value is moving from the LLM itself to the proprietary evaluation loops that keep the LLM on the rails.
The Tactical Edge: Export production traces and build a "Golden Set" of 50 hard examples. Use these to run A/B tests on every prompt change before hitting production.
The Bottom Line: Reliability is the product. If you cannot measure how your agent fails, you haven't built a product; you've built a demo.
The transition from chatbots with tools to agents that build tools marks the end of the manual integration era.
Stop building custom model scaffolding and start building on top of opinionated agent layers like the Codex SDK.
In 12 months, the distinction between a coding agent and a general computer user will vanish as the terminal becomes the primary interface for all digital labor.
The Capability-Utility Gap is widening. We see a divergence where models get smarter but the friction of human-AI collaboration keeps productivity flat.
Deploy AI for mid-level engineers or low-context tasks. Avoid forcing AI workflows on your top seniors working in complex legacy systems.
The next year will focus on reliability over raw intelligence. The winners will have models that require the least amount of human babysitting.
The Macro Shift: Scaling laws are hitting a diminishing return on raw data but a massive acceleration in reasoning. The shift from statistical matching to reasoning agents happens when models can recursively check their own logic.
The Tactical Edge: Build for the agentic future by prioritizing high-context data pipelines. Models perform better when you provide massive context rather than relying on zero-shot inference.
The Bottom Line: We are 24 months away from AI that makes unassisted human thought look like navigating London without a map. Prepare for a world where the most valuable skill is directing machine agency rather than performing manual logic.
The transition from model-centric to loop-centric development. Performance is now a function of the feedback cycle rather than just the weights of the frontier model.
Implement an LLM-as-a-judge step that outputs a "Reason for Failure" field. Feed this string directly into a meta-prompt to update your agent's system instructions automatically.
Static prompts are technical debt. Teams that build automated systems to iterate on their agent's instructions will outpace those waiting for the next model training run.
The Macro Shift: The transition from writing to reviewing as the primary engineering activity. As agents generate more code, the human role moves from creator to editor.
The Tactical Edge: Build CLIs for every internal tool to give agents a native text interface. This increases accuracy and speed compared to visual automation.
The Bottom Line: Developer experience is the infrastructure for AI. Investing in clean code and fast feedback loops is the only way to ensure AI productivity gains do not decay over the next 12 months.
REV is a starting point, not the finish line: It's a useful, objective measure of immediate user willingness to pay for blockspace but doesn't encompass all value drivers of an L1.
App-layer eats L1 lunch (eventually): Expect applications to get better at internalizing value (like MEV), potentially reducing direct REV flow to L1s, making app success crucial for the L1 ecosystem.
Narrative & adoption still trump pure metrics: For now, perceived momentum, user growth, and developer activity (like on Solana) can heavily influence L1 valuations, often overshadowing strict adherence to metrics like REV multiples.
Investing in specialized crypto treasury vehicles offers exposure not just to the underlying asset but also to a strategy of active accumulation and yield enhancement. These companies argue their NAV premiums are justified by their operational capabilities and future growth prospects.
NAV Premiums Signal Future Growth: Market premiums on crypto-holding companies often reflect expectations of continued asset accumulation, not just current asset values.
Expertise Drives Alpha: Specialized operational capabilities, like in-house validator management, can generate significantly higher yields (20-40% more) than readily available retail options.
Sophisticated Strategies Outperform Simple Holding: For investors seeking optimized exposure, vehicles offering complex, managed strategies for asset accumulation and yield can provide an edge over direct, passive investment.
Beyond ETFs: These treasury vehicles offer a more dynamic, potentially higher-return (and higher-risk) path to crypto exposure than standard ETFs, focusing on active accumulation and yield enhancement.
Volatility as a Tool: For certain crypto-native companies, extreme stock volatility is actively cultivated to unlock unique capital market opportunities and attract specific investor demographics.
The Solana "MicroStrategy" Model is Live: Companies like DeFi DevCorp are demonstrating that the playbook of leveraging public markets for aggressive, single-asset crypto accumulation can be replicated beyond Bitcoin, with Solana as a prime new candidate.
Tariffs Trump Tranquility: A 10% tariff floor could trigger summer stagflation, disrupting current Goldilocks market pricing.
Stablecoin Bill is Bellwether: The fate of the "Genius Act" will significantly impact the trajectory of broader US crypto regulation and investor confidence.
Institutional Crypto Evolves: Coinbase's S&P 500 nod and the push for diverse crypto ETFs (like Solana) underscore the sector's maturation, even as regulatory hurdles for features like staking persist.
LP Scrutiny Intensifies: Expect smaller fundraises for many VCs, especially in crypto, as LPs demand real returns (DPI) and, for crypto, regulatory certainty.
Endowment Exodus Looms: Yale's $6B private equity sale signals a potential LP supply shock as other endowments may follow suit due to tax changes and liquidity needs.
Elite VCs Consolidate Power: Capital will increasingly flow to the top 5-10 VC firms, particularly those with AI expertise, hastening the decline of underperformers.
ETH's Valuation Paradigm Shift: Value ETH based on Total Value Secured (TVS), not diminishing transaction fees, as it aims to secure trillions in global assets.
L1+L2 is the Winning Formula: Ethereum's strategy of scaling L1 alongside a diverse L2 ecosystem (offering political/business model diversity) is designed to onboard the world.
Coordination & BD are Crucial: Renewed focus on cohesive narrative, business development (like Etherealize), and community alignment are vital to executing Ethereum's ambitious roadmap.
