SCSP Chair Dr. Eric Schmidt delivered a harsh truth to the software industry: the AI revolution is fundamentally changing how we build applications. It is no longer about writing every single line of code yourself. The new era of software engineering is about harnessing and managing the power of AI tools to build at scale. This shift is so massive that it's already shaking up the stock market and forcing traditional Software-as-a-Service (SaaS) companies to adapt or face obsolescence. Watch the full conversation with Dr. Eric Schmidt and Thom Shanker at The 2026 Exchange - #SoftwareEngineering# #Coding# #FutureOfWork# #ArtificialIntelligence# #Innovation# #ai# #ainews#

Traditional software engineering optimized for creation velocity. AI-native engineering now requires understanding velocity. This is not “technical debt.” It is: comprehension debt You will have post-build operational comprehension issue.

AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to many other changes in how we operate. For example, some great engineers now play broader roles than just writing code. They are partly product managers, designers, sometimes marketers. Further, small teams who work in the same office, where they can communicate face-to-face, can move incredibly quickly. Because we can now build fast, a greater fraction of time must be spent deciding what to build. To deal with this project-management bottleneck, some teams are pushing engineer:product manager (PM) some teams are pushing engineer:product manager (PM) ratios downward from, say, 8:1 to as low as 1:1. But we can do even better: If we have one PM who decides what to build and one engineer who builds it, the communication between them becomes a bottleneck. This is why the fastest-moving teams I see tend to have engineers who know how to do some product work (and, optionally, some PMs who know how to do some engineering work). When an engineer understands users and can make decisions on what to build and build it directly, they can execute incredibly quickly. I’ve seen engineers successfully expand their roles to including making product decisions, and PMs expand their roles to building software. The tech industry has more engineers than PMs, but both are promising paths. If you are an engineer, you’ll find it useful to learn some product management skills, and if you’re a PM, please learn to build! Looking beyond the product-management bottleneck, I also see bottlenecks in design, marketing, legal compliance, and much more. When we speed up coding 10x or 100x, everything else becomes slow in comparison. For example, some of my teams have built great features so quickly that the marketing organization was left scrambling to figure out how to communicate them to users — a marketing bottleneck. Or when a team can build software in a day that the legal department needs a week to review, that’s a legal compliance bottleneck. In this way, agentic coding isn’t just changing the workflow of software engineering, it’s also changing all the teams around it. When smaller, AI-enabled teams can get more done, generalists excel. Traditional companies need to pull together people from many specialties — engineering, product management, design, marketing, legal, etc. — to execute projects and create value. This has resulted in large teams of specialists who work together. But if a team of 2 persons is to get work done that require 5 different specialities, then some of those individuals must play roles outside a single speciality. In some small teams, individuals do have deep specializations. For example, one might be a great engineer and another a great PM. But they also understand the other key functions needed to move a project forward, and can jump into thinking through other kinds of problems as needed. Of course, proficiency with AI tools is a big help, since it helps us to think through problems that involve different roles. Even in a two-person team, to move fast, communication bottlenecks also must be minimized. This is why I value teams that work in the same location. Remote teams can perform well too, but the highest speed is achieved by having everyone in the room, able to communicate instantaneously to solve problems. This post focuses on AI-native teams with around 2-10 persons, but not everything can be done by a small team. I'll address the coordination of larger teams in the future. I realize these shifts to job roles are tough to navigate for many people. At the same time, I am encouraged that individuals and small teams who are willing to learn the relevant skills are now able to get far more done than was possible before. This is the golden age of learning and building! [Original text: ]

Welcome Neo-cypherpunk Summit speaker: Simone Robutti from @TechWorkersBER (TWC). Simone is an organization designer with a software engineering background, currently focusing on empowering small-size distributed organizations through cybernetic process design, democratic leadership development, and custom software. He engages in Tech Unionism, Algorithmic Accountability, Common Cybernetics, and Democratic Organizational Design. Recently he co-created Cables Of Resistance conference: 14th June, Berlin:

Conviction 2026 welcomes Tianwei Liu (@Tian_wei) - Co-Founder & CEO, @StraitsX. With a background in software engineering, Tianwei has led StraitsX’s evolution into a stablecoin-native settlement layer powering global payments. Under his leadership, StraitsX has become a first-mover in regulatory-first stablecoin infrastructure. As the issuer of XSGD and XUSD, fully reserved stablecoins recognised by the Monetary Authority of Singapore (MAS) as substantively compliant with the upcoming Single-Currency Stablecoin (SCS) regulatory framework, StraitsX is enabling stablecoin-backed card issuance across Asia, advancing seamless payment interoperability, and driving real-world utility for institutions and fintechs. Tianwei is a leading voice in regulated stablecoin infrastructure, where compliance, institutional capital, and digital finance converge. Stay tuned to hear from StraitsX’s Co-Founder at Conviction 2026. ------- 👉 Grab your Early Bird tickets now to unlock exclusive insights from top-tier speakers at Conviction 2026: The largest Blockchain & Digital Asset event in Vietnam 📍 Location: Ho Chi Minh City, Vietnam 📅 Date: August 14–15, 2026 🌐 Website:

As AI agents accelerate coding, what is the future of software engineering? Some trends are clear, such as the Product Management Bottleneck, referring to the idea that we are more constrained by deciding what to build rather than the actual building. But many implications, like AI’s impact on the job market, how software teams will be organized, and more, are still being sorted out. The theme of our AI Developer Conference on April 28-29 in San Francisco is The Future of Software Engineering. I look forward to speaking about this topic there, hearing from other speakers on this theme, and chatting with attendees about it. We’re shaping the future, and I hope you will join me there! It is currently trendy in some technology and policy circles to forecast massive job losses due to AI. Even if they have not yet materialized, these losses certainly must be just over the horizon! I have a contrarian view that the AI jobpocalypse — the notion that AI will lead to massive unemployment, perhaps even rioting in the streets — won’t be nearly as bad as dire forecasts by pundits, especially pundits who are trying to paint a picture of how powerful their AI technology is. Among professions, AI is accelerating software engineering most, given the rise of coding agents. According to a new report by Citadel Research, software engineering job postings are rising rapidly. So if software engineering is a harbinger of the impact AI will have on other professions, this expansion of software engineering jobs is encouraging. Yes, fresh college graduates are having a hard time finding jobs. And yes, there have been layoffs that CEOs have attributed to AI, even if a large fraction of this was “AI washing,” where businesses choose to attribute layoffs to AI, even though AI has not changed their internal operations much yet. And yes, there is a subset of job roles, such as call center operator, that are more heavily impacted. Many people are feeling significant job insecurity, and I feel for everyone struggling with employment, whether or not the cause is AI-related. And many other factors, such as over-hiring during the pandemic and high interest rates, have contributed to the slowdown in the labor market, and the notion that AI is leading to unemployment is oversimplified. In software engineering, I see a lot of exciting work ahead to adapt our workflows. It is already clear that: (i) As AI makes coding easier, a lot more people will be doing it. (ii) Writing code by hand and even reading (generated) code is not that important, because we can ask an LLM about the code and operate at a higher level than the raw syntax (although how high we can or should go is rapidly changing). (iii) There will be a lot more custom applications, because now it’s economical to write software for smaller and smaller audiences. (iv) Deciding what to build, more than the actual building, is becoming a bottleneck. (v) The cost of paying down technical debt is decreasing (since AI can refactor for you). At the same time, there are also a lot of open questions for our profession, such as: - In the future, what will be the key skills of a senior software engineer? And for junior levels, what should be the new Computer Science curriculum? - If everyone can build features, what skills, strategies, or resources create competitive advantage for individuals and for businesses? - What are the new building blocks (libraries, SDKs, etc.) of software? How do we organize coding agents to create software? - What should a software team look like? For example, how many engineers, product managers, designers, and so on. What tooling do we need to manage their workflow? - How do AI agents change the workflow of machine learning engineers and data scientists? For example, how can we use agents to accelerate exploring data, identifying hypotheses, and testing them? I’m excited to explore these and other questions about the future of software engineering at AI Dev. I expect this to be an exciting event. Please join us! [Original text: The Batch newsletter.]

Dario Amodei’s recent thesis is more than a forecast; it is a blueprint for the Great Compression. As the traditional software lifecycle from requirements to deployment collapses into a seamless dialogue between human intent and machine execution, we are witnessing the Death of the Coder and the Birth of the Architect. Programming is being subsumed because code is the ultimate structured data, but Software Engineering is not dying; it is ascending into high-level orchestration. Beyond the technical shift, Amodei outlines five pillars of AI-driven human progress: 1. Biological Compression AI will condense a century of biomedical progress into a decade. By mastering complex biological systems, AI will move us beyond treating symptoms to curing cancer, infectious diseases, and aging—drastically extending the human lifespan. 2. Neuroscience & Mental Health By unlocking the brain’s "black box," AI will replace trial-and-error treatments with precise neuromodulation and pharmacology. This revolution aims to eradicate depression and Alzheimer’s while optimizing human cognition and emotional resilience. 3. Global Equity & Leapfrog Governance AI serves as the ultimate equalizer. By optimizing resource allocation and providing universal access to elite education and medicine, it enables developing nations to bypass industrial hurdles and achieve rapid, "leapfrog" development. 4. Post-Scarcity Economics In a world where labor is no longer a prerequisite for survival, we face a transition to a post-scarcity society. Economic value will shift from routine output to human-centric creativity, deep interpersonal connection, and complex problem-solving. 5. Democratic Resilience AI will act as a bulwark for the rule of law. By neutralizing misinformation and enhancing judicial transparency, AI can strengthen democratic institutions and provide a powerful systemic check against the rise of authoritarianism.

New on the Anthropic Engineering Blog: How we use a multi-agent harness to push Claude further in frontend design and long-running autonomous software engineering. Read more: