Preparing for Radical Transformation ✍ Leonard Tang🕐 2026-04-18📦 6.3 KB 🟢 已读 𝕏 文章列表 本文探讨了超级智能已至的背景下，AI 发展的加速及其带来的挑战与机遇。作者指出 AI 将重塑高 prestige 的白领行业，同时也面临持续学习、安全和验证等难题。文章建议通过自动化低垂果实、拥抱开源权重模型、利用隐性知识及建立安全基线来应对。 AIAgent转型超级智能企业落地安全模型验证开源模型 # Preparing for Radical Transformation **作者**: Leonard Tang **日期**: 2026-04-17T16:35:29.000Z **来源**: [https://x.com/leonardtang_/status/2045179350547612043](https://x.com/leonardtang_/status/2045179350547612043) ---  Distilled from a Night with Haize Labs: Preparing for Radical Transformation Delmonico's April 16, 2026  The title of this dinner is Preparing for Radical Transformation. With the rate of progress of this technology, a more apt title would have been Embracing Radical Transformation. Though it has not yet fully dispersed throughout the economy, by many sensible metrics super intelligence has already arrived. It is an incredible inning of history we are in. In the same hour, I experience both incredible hope and fear for what is to come. AI will enable us to solve the most persistent of challenges in medicine, materials, physics, the sciences, engineering. AI will also rapidly rip through the fabric of white collar services, most immediately upending high prestige professions like software engineering, consulting, legal, and financial services. It is not my intention to be a pessimist. But we must be sober about the reality we face: if we do not proactively shape how AI is deployed in our businesses, our businesses will very much be left behind. I selected this group of technology, risk, and AI leaders tonight to plan how we chart the path forward, stay ahead of the technology curve, and enable radical transformation. I want all of us to learn from each other. My goal is to speak as little as possible tonight and learn from you. ## This Inning of History I have been researching AI since 2020. Never has AI progressed so rapidly. Models can now trivially handle many-hour long verifiable tasks and perform work at parity or better than the majority of the junior white collar workforce. 3 years ago the models struggled at basic arithmetic.  The models will only continue to get better on anything that can be readily hillclimbed. Too, it is an open secret that all the frontier labs are investing heavily into Recursive Self Improvement: the ability for AI to improve itself. Most immediately, this means AI experimenting with new architectures, training algorithms, and data curation recipes to train future generations of itself. This implication is that AI progress will only continue to accelerate, at least on a subset of verifiable + economically valuable work. ## Challenges at the Frontier In spite of all this progress, three enduring challenges remain. Continual Learning. AI cannot continuously adapt to the ever-changing world and sources of information around us at the same rate at which humans can. This would require a breakthrough in the ability to bake actions and metrics the model experiences back into the weights of the model. Unlike the rapid capabilities improvement in the last 3 years, this requires a deeper insight than brute scaling of parameters, compute, and data. There is no obvious timeline on which this challenge will be resolved. Security. The power of agents is that they can explore solution spaces with creativity. This is also a security nightmare. For agents to be adopted with confidence in the enterprise, clear trust boundaries must be drawn, rigorous red-teaming must be performed, and ongoing monitoring must be enabled. Verification. The following statement has always been true in the post-LLM era: if you could measure the correctness of the model when performing task family X, you could improve capabilities on X. The next major frontier is in how to improve model capabilities on tasks where one cannot easily, via programs or enumerative rules, measure correctness. This is the verification problem: the ability to verify the correctness of machine output. There is no shortage of difficult-to-verify work within an enterprise context, and AI cannot reliably be of use in these settings until the verification problem is solved. ## Enabling Radical Transformation: Your Opportunity Automate the Low Hanging Fruit. While AI currently struggles on ambiguous, difficult-to-verify tasks that require expert judgement, it can be immediately beneficial in automating work where there is a very clear set of processes, procedures, and definitions of success. If you have well-articulated SOPs already available, it is a no-brainer to turn that into an agent. Be Open to Open Weight Models. Many enterprise tasks are narrow enough to fine-tune (via SFT and RL) a custom model for. This can save you orders of magnitude in cost and latency. This, of course, also prevents the frontier labs from training their models and harnesses on your proprietary business context and data, which is the ultimate enduring moat for your institution. It will prevent your entire workforce from being automated. Bring Tacit Knowledge to Bear. Design agent experiences in a way that incentivizes domain experts to articulate their thought process towards producing an output. Use this to create verification signal in the form of reward models and judges. This ultimately enables you to improve model performances in domains that were previously unsolvable. Set Groundwork for Safe, Secure, Reliable Agents. Conduct testing in sandboxes and simulation to understand the threat surface and trust boundaries of your agent. Use semantic observability and clustering to understand common vulnerability patterns. Leverage AI for joint red-teaming and blue-teaming exercises to automatically harden your system. ## Thank You Thank you to our friends at Nomura, Amex, UBS, Macquarie, Columbia, TriSpan for the spirited conversation and company on this topic last night.  ## 相关链接 - [Leonard Tang](https://x.com/leonardtang_) - [@leonardtang_](https://x.com/leonardtang_) - [4.7K](https://x.com/leonardtang_/status/2045179350547612043/analytics) - [Upgrade to Premium](https://x.com/i/premium_sign_up) - [12:35 AM · Apr 18, 2026](https://x.com/leonardtang_/status/2045179350547612043) - [4,738 Views](https://x.com/leonardtang_/status/2045179350547612043/analytics) --- *导出时间: 2026/4/18 13:53:21*
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