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.
The Capability-Productivity Gap. We are entering a period where model intelligence outpaces our ability to integrate it into high stakes production.
Audit your stack. Identify tasks where "good enough" generation is a win versus high context tasks where AI is currently a net negative.
Do not mistake a climbing benchmark for a finished product. For the next year, the biggest wins are not in smarter models but in better verification loops.
The transition from simple Large Language Models to Reasoning Models marks the end of the stochastic parrot era.
Build agentic workflows that utilize high-context windows for recursive problem solving.
We are moving toward a world where intelligence is a commodity. Your value will shift from knowing things to directing outcomes over the next 12 months.
The erosion of the American dream, fueled by inflationary policies and monopolistic capitalism, is driving a political shift towards candidates who advocate for transparent, common-sense economic policies and modern regulatory clarity for emerging technologies like crypto.
Support political candidates who champion clear, updated regulatory frameworks for digital assets and advocate for increased market competition across industries.
The fight for crypto clarity is now intertwined with broader economic and political reform. Understanding this intersection is crucial for investors and builders navigating a landscape where policy decisions directly impact market viability and individual prosperity over the next 6-12 months.
Politically influenced central banking is returning, making monetary policy an electoral tool. Fed decisions will reflect political priorities, potentially leading to aggressive rate cuts.
Re-evaluate portfolio sensitivity to political intervention. Position for lower long-term rates, but prepare for increased market volatility.
The incoming Fed chair signals a re-alignment of monetary policy with executive branch goals. Expect policy to prioritize affordability and electoral success.
The US is pivoting from a QE-fueled, government-led economy to a "free market" model under the new Fed Chair, Kevin Warsh. This means a potential reduction in the Fed's balance sheet (QT) and lower rates without yield curve control (YCC), leading to decreased US dollar liquidity.
Adopt a phased, data-driven allocation strategy. Michael Nato recommends an 80% cash position, deploying first into Bitcoin (65% target) at macro lows (around 65K-58K BTC, MVRV < 1, 200WMA touch), then into high-conviction core assets (20%), long-term holds (10%), and finally "hot sauce" (5%) during wealth creation.
The current "wealth destruction" phase, while painful, presents a rare opportunity to accumulate assets at generational lows, provided one understands the macro shifts and adheres to a disciplined, multi-stage deployment plan.
The financial world is splitting into two parallel systems: opaque TradFi and transparent onchain finance. Value is migrating to platforms that can simplify and distribute onchain financial products globally.
Invest in or build applications that prioritize mobile-native experiences, abstract away crypto complexities (like gas fees), and offer tangible real-world utility for onchain assets.
The future of finance is onchain, and "super apps" like Jupiter are building the necessary infrastructure and user experiences to onboard the next billion users.
Crypto's initial broad vision has narrowed to specific financial use cases, while AI and traditional markets capture broader attention. This means builders must focus on tangible value and investors on proven models.
Identify projects with novel token distribution models (like Cap's stablecoin airdrop) or those building consumer-friendly applications within new ecosystems (like Mega ETH) that address past tokenomics failures.
The industry is past its naive, speculative phase. Success hinges on practical applications, robust tokenomics, and competing with traditional finance, not just abstract ideals.
The Macro Shift: From unbridled, community-driven idealism to a pragmatic, business-focused approach. Early crypto imagined a world where "everything is a thing on Ethereum," but reality has narrowed its primary use cases to finance and trading, forcing a re-evaluation of tokenomics and community models. This shift is also driven by AI capturing mindshare and traditional finance co-opting blockchain tech.
The Tactical Edge: Re-evaluate token distribution models. Instead of relying on inflationary yield farming that creates sell pressure, explore innovative approaches like Cap's "stable drop" (airdropping stablecoins, then inviting participation in a token sale) to align incentives and attract long-term holders. Focus on building real products with defensible business models, even if they lean more "business" than "protocol."
