Pichai Says Google Is ‘A Bit Behind’ on Agentic Coding: the Practical Angle

There is a specific kind of tension that comes with watching a tech giant navigate a paradigm shift. For years, we viewed Google as the definitive architect of the modern web, but the arrival of generative AI has shifted the goalposts. When the CEO of such a company admits they are "a bit behind," it is more than just a humble admission—it is a signal that the nature of the competition has changed.

The shift we are seeing isn't just about who has the largest model or the most parameters. It is about the move from "chatbots" that answer questions to "agents" that actually do work. For developers, this is the difference between an AI that can write a function and an AI that can manage a repository. Sundar Pichai recently addressed this gap during an appearance on the New York Times Hard Fork podcast, and his transparency provides a rare window into how the industry's most powerful players are actually thinking about the "agentic" future.

Defining the Gap in Agentic Capability

During the conversation, Pichai was careful to distinguish between where Google is leading and where it is trailing. He noted that Google's current models are highly capable in several core areas: text generation, multimodality, voice, audio, and general reasoning. In these domains, the technology is mature and integrated.

However, the "frontier" has moved toward agentic coding. Pichai admitted that Google is currently behind in areas like tool use, strict instruction following, and "long-horizon tasks." To put this in practical terms, he drew a line between "single-shot" outputs and complex workflows. Google has been successful at helping developers create a single-shot web front end—essentially a one-off piece of code that looks and works well in isolation.

The real gap, however, exists when the task extends beyond a single prompt. Long-horizon tasks involve working across complex, existing codebases where the AI must maintain context over a long period, make iterative changes, and understand the ripple effects of a single edit across multiple files. This is the essence of agentic coding: the ability to act as a collaborator who understands the system, not just a calculator that produces snippets.

Expert Interpretation: The distinction between "single-shot" and "long-horizon" is the most critical takeaway here. Most developers have already experienced the frustration of a model that writes a perfect function but fails to understand how that function fits into a 10,000-line project. The tradeoff here is between breadth of knowledge and depth of context. When choosing your current AI tooling, you should inspect whether the tool is merely a "copilot" (suggesting the next line) or an "agent" (suggesting the next architectural move). If your work involves legacy codebases, the "single-shot" capability is almost irrelevant; the long-horizon capability is everything.

The Data Surface Problem

One of the most interesting parts of Pichai's admission was his explanation of why this gap exists. He didn't attribute it to a lack of raw computing power or a failure of research, but rather to a lack of "surface area."

In the world of AI, data is the fuel, but the type of data matters more than the volume. Pichai pointed out that Google lacked a dedicated, external coding product surface that could generate the specific kind of developer interaction data needed to train agentic models. He specifically referenced the relationship between Anthropic and Cursor as a benchmark. By having a dedicated environment where developers interact with the AI in real-time within their IDE, competitors are capturing a feedback loop of how developers actually solve complex problems.

Google, by contrast, didn't have that same direct pipeline of external developer behavior. To bridge this, Google recently introduced Antigravity 2.0, a standalone desktop application designed specifically for agent-based coding workflows. Pichai noted that internal adoption of Antigravity is growing rapidly—doubling every week—which is providing the "hill climb" necessary to improve the models through actual usage data.

Expert Interpretation: This highlights the "Flywheel Effect" in AI development. The model improves the product, the product attracts users, the users generate interaction data, and that data improves the model. The tradeoff Google faced was between maintaining a general-purpose ecosystem and building a specialized tool. By launching Antigravity, they are admitting that a web-based chat interface is insufficient for high-level coding. For the reader, the decision to inspect here is your own workflow: are you using a tool that is merely a wrapper for an API, or are you using a tool integrated into the environment where the data loop is strongest? The tool that "sees" your whole project is the one that will evolve the fastest.

The Friction of Gemini 3.5 Flash

The timing of these comments is notable, as they followed the launch of Gemini 3.5 Flash, which became the default model for AI Mode globally. While the launch was a major milestone, it wasn't without friction. Pichai acknowledged that users had expressed frustration regarding pricing, usage limits, and overall model quality.

Regarding the usage limits, Pichai explained that Google intentionally tightened them at launch to prevent system outages, though he admitted this was a source of rightful frustration for developers. He indicated that progress on these limits would be made shortly. More importantly, he conceded that the new model might have "regressions" in certain areas—meaning it might perform worse than previous versions in specific tasks. He noted, however, that many of these issues are "easy to address" through post-training techniques and would be resolved quickly.

Expert Interpretation: This is a classic example of the tension between stability and velocity. Google chose stability (preventing outages) over user experience (generous limits). From a technical perspective, the mention of "regressions" is a reminder that AI development is not a linear path of improvement; adding a new capability often breaks an old one. When integrating a "Flash" or "Lite" model into your production pipeline, the tradeoff is usually latency versus reliability. You must decide if the speed of a model like 3.5 Flash is worth the risk of these regressions, or if you should stick to a more stable, albeit slower, Pro version.

The Strategic Shift in Messaging

There is a stark difference between the messaging at Google's I/O developer conference and the tone of this interview. At I/O, the narrative was one of confidence, focusing on the capabilities of Gemini 3.5 Flash and the promise of Antigravity. The podcast interview, however, offered a more candid assessment of the competitive landscape.

Pichai's admission reveals that Google views the coding gap as a feedback loop problem. The company has realized that being "smart" isn't enough; they need to be "integrated." By building Antigravity, they aren't just releasing a product; they are building a data collection engine. They are attempting to create the same symbiotic relationship that other AI labs have established with specialized IDEs.

Expert Interpretation: When a company as large as Google shifts from "marketing mode" to "candid mode," it usually indicates a strategic pivot. They are moving away from the idea that a single, massive model can solve everything and toward a strategy of specialized "surfaces." The tradeoff here is between a unified user experience and a fragmented, tool-specific one. As a developer, you should inspect whether you prefer a "one-stop-shop" ecosystem or a "best-of-breed" stack. Google is betting that by creating specialized surfaces, they can eventually reclaim the lead in agentic coding.

The Path Forward and Gemini 3.5 Pro

Looking ahead, Pichai described the agentic coding space as "very dynamic," suggesting that the lead can shift quickly. While the current gap is acknowledged, Google is already deploying Gemini 3.5 Pro internally. This model is expected to roll out to the public next month.

The central question remains whether Gemini 3.5 Pro will be the tool that finally closes the gap in long-horizon tasks and agentic behavior. While Google hasn't explicitly stated that this specific model will solve the "agentic" problem, the internal usage patterns and the data being gathered via Antigravity suggest that the next iteration of their coding tools will be more focused on system-wide autonomy than simple code generation.

Expert Interpretation: In a "dynamic" space, the most dangerous move is to wait for the "perfect" model. The pace of iteration is so fast that by the time a model is "perfect," the definition of the task has usually changed. The tradeoff is between loyalty to an ecosystem and tooling agility. My advice is to remain tool-agnostic. Use the best agentic tools available today—regardless of the provider—while keeping a close eye on the rollout of Gemini 3.5 Pro. The decision to switch should be based on empirical evidence of "long-horizon" success in your specific codebase, not on the brand of the model.

Practical next steps

The useful part is not only the idea itself, but the operating habit behind it. Use it as a checklist for decisions: what deserves attention now, what should be monitored, what needs a stronger evidence base, and what can wait until the system has more scale.