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(Sundar Pichai discusses the software reconstruction trend)
On April 7, 2026, Google CEO Sundar Pichai stated in a recent interview that almost all software will face a fundamental rebuild.
The core reason lies in the changing nature of user interaction. Search is evolving from "returning results" to "executing tasks." Users no longer need to manually operate software step-by-step; instead, they directly state their objectives, and the system autonomously completes them.
This transformation is inseparable from the participation of AI agents. Currently, some teams within Google have already begun using agent collaboration tools.
Pichai predicts that 2027 will be the inflection point of this revolution.
So, how will this software reconstruction unfold? What specific obstacles has Google encountered in practice? How long will this reshaping process last? We will explore these questions in depth below.
Section 1 | Deconstructing the Interaction Paradigm
Sundar Pichai mentioned a key concept in the interview: future search will evolve into an Agent dispatch hub.
The core of this statement lies not in the search business itself, but in the fundamental shift in how users interact with systems.
Traditional search follows a "retrieve-filter" model: enter keywords, get ranked links, and then read and operate yourself. This is a one-way, fragmented process of "you issue an instruction, it responds passively, and you continue operating."
But current search has begun to take over long-cycle, multi-step complex tasks. What you get is no longer a pile of web links, but a visible "research process" in progress. Take Google's internal Antigravity system as an example: it can simultaneously dispatch multiple Agents to work in parallel, each responsible for different modules of the task, ultimately converging for delivery.
With fundamental changes in underlying hardware form factors and input/output (I/O) methods, the original interaction logic of software must be rewritten.
Past UI interfaces were designed for "human manual operation": buttons to guide clicks, menus to provide options, and paths relying on humans to advance step-by-step. When tasks are entirely handed over to Agents for execution, these designs become redundant.
You no longer need to closely monitor where each operation step occurs, only focusing on whether the final goal is achieved. Software interfaces will transform from "manual operation tools" to "task monitoring dashboards."
Workflows also shift from "static and fixed" to "dynamically generated." Traditional software workflows are preset because machines need to guide human operations; whereas Agent-driven software can generate optimal execution paths in real-time based on current task context. This means that even for the same goal, the implementation method could be completely different each time.
This brings a disruptive shift: software is evolving from "a tool operated by humans" to "a digital employee working on behalf of humans." When this "intent-driven" model becomes mainstream, existing software design principles will undoubtedly be completely overhauled.
Section 2 | "Reconstruction" Is Already Happening Inside Google
This fundamental "overhaul" of interaction logic is not just停留在预测层面. Inside Google, old workflows have already been broken.
Currently, Google DeepMind and some software engineering teams have fully adopted the Antigravity system. As mentioned earlier, this is an Agent dispatch hub where engineers run various workflows, handing tasks over to Agents for automatic completion. Last week, this system was also officially rolled out to Google's core search team.
However, this leap into the Agent era has not been smooth sailing. They encountered four specific implementation obstacles:
First is the barrier of prompt engineering. Engineers need time to adapt to how to issue precise commands to AI. This is not just general conversational skills, but involves deep "internal enterprise knowledge": how to make AI accurately call internal tools, and how to clearly describe complex requirements of internal systems to AI.
Second is the conflict in code collaboration methods. After AI intervention, code iteration and renovation frequency is extremely high, with vast modification scopes. A person can even have AI rewrite code several times before release. This causes the rate of change in the codebase to exceed traditional expectations, making traditional multi-person collaboration exceptionally difficult.
Third is the barrier of data and permissions. Solving complex problems often requires calling internal enterprise data, but existing IT permission systems are designed for "humans," not "Agents." How to define Agent access levels? How to control their permission boundaries? These security mechanisms must all be reconstructed.
Finally is the blurring of organizational roles. Engineers, product managers, designers... these functional boundaries were all built on past industrial collaboration models. When AI can simultaneously handle code writing, product logic sorting, and interface design, the original role walls begin to collapse.
In response, Sundar's answer is very pragmatic: the Gemini team, Gemini Enterprise team, and Antigravity team are committed to tackling these pain points one by one. And these internal pitfalls and solutions are exactly their future product roadmap.
In other words, Google is not just painting a vision of software reconstruction, but practicing it internally. They first encounter problems during internal use, develop solutions, and then turn these solutions into products for the market.
This transformation process is particularly difficult for large organizations.
Because the biggest obstacle to technology implementation is often the organization itself.
Section 3 | Timeline and Industry Span
How long does it take to cross the deep waters of organizational change? Sundar's timeline anchor is: 2027 will welcome the true industry inflection point.
He mentioned that by then, profound changes will occur in certain vertical sectors. Taking business data forecasting as an example, practitioners will adopt a completely new workflow based on Agents. But this is necessarily a gradual transition period: for a long time, enterprises may adopt a "new-old parallel" model. First using traditional systems to verify AI results, and after establishing trust, gradually completing a full switch.
But why has this transformation only now begun to possess the conditions for large-scale advancement in 2026?
Sundar admitted that many previous ideas could not be implemented because the underlying technology fault tolerance was too low. It's like seeing a hopeful new world, but its infrastructure is extremely unstable. By 2026, a qualitative change occurred: the technology curve saw a leap, and system stability finally reached the passing line for external promotion.
However, even with technology ready, the transformation pace of different enterprises also has huge differences.
Startups transform more easily. These AI-native teams can build organizational structures according to the new Agent logic from day one. They screen for talent with AI collaboration capabilities through recruitment and operate directly on new workflows, without bearing the costs of transforming old systems and retraining existing employees.
Large companies face heavy historical baggage. They must "change the engine while flying the plane" while ensuring the smooth operation of massive businesses. This means tens of thousands of employees, intricately intertwined old systems, and solidified approval processes all need to be adjusted in stages and gradually. Startups can complete system switches overnight, while giants can only advance step by step.
This destines software reconstruction to be a "layered advancement" campaign. Some frontier enterprises will complete the transformation of core businesses in 2027, while more traditional enterprises may need several years.
From Google's advancement path, we can see: new methods are first verified in small ranges, and after confirming feasibility, gradually spread. GDM and software engineering teams have already transformed, the search team has just begun, and other teams are still waiting.
Software rebuilding is not a technological switch point, but a continuously advancing process.
The direction is set; speed becomes the only difference.
Conclusion
"Almost all software will be rebuilt once."
Software is evolving from passively operated tools to systems that actively execute tasks. The interaction paradigm has changed, development processes must change, and organizational structures must be reshaped accordingly.
2027 will become an important inflection point for the software industry. Although in this long transformation, the implementation pace of different companies is destined to have significant time lags.
But ultimately, what needs to be redone is not just a few lines of software code, but the entire underlying logic of work and collaboration.
📮 This article is produced by AI Deep Research Institute. The content is compiled from public online materials including Google CEO Sundar Pichai's latest interview on April 8, 2026, and is of a commentary analysis nature. The content represents distilled viewpoints and reasonable citations, not verbatim copies of original interview materials. Reproduction without authorization is prohibited.
References:
https://www.youtube.com/watch?v=bTA8sjgvA4c
https://www.searchenginejournal.com/pichai-says-ai-could-break-pretty-much-all-software/571387
Source: Official media/online news
Layout: Atlas
Editor: Shen Si
Editor-in-Chief: Turing
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