Introduction
Artificial General Intelligence (AGI) is often discussed abstractly, but for fields like pharma and biotechnology it has a practical meaning: the ability to reason about new biological problems without relying on prior task-specific training. This is distinct from pattern recognition or recall. It is the difference between recognizing known signatures and forming hypotheses when data are sparse, noisy, or contradictory.
Today’s AI systems excel when problems resemble their training data, but they struggle with novelty. As models have scaled, benchmark performance has improved dramatically, yet it has become increasingly unclear whether these gains reflect deeper understanding or more comprehensive memorization. This tension has pushed the field toward evaluations that probe reasoning rather than familiarity.
Measuring Reasoning Instead of Recall
One such effort is ARC-AGI, developed by the Alignment Research Center. While not a biological benchmark, it is useful precisely because it strips away domain knowledge and tests abstraction and generalization under uncertainty. Recent results, particularly ARC-AGI-2’s explicit accounting of compute cost, reflect a broader shift in AI research: a growing focus on reasoning depth over efficiency.
For scientific users, this direction matters. Many of the hardest problems in biology are not data-limited, but reasoning-limited, and AGI research is increasingly oriented toward that regime.
Why Reasoning-Centered AGI Matters for Bioinformatics
In this article we’ll discuss a new model from Google and how it moves the needle on AGI. Using Gemini 3 Deep Think as a case study, we’ll explore what it looks like when AI systems prioritize reasoning over efficiency, and why that distinction matters for bioinformaticians. This shift hints at new ways AI can support hypothesis generation and interpretation in settings where data are complex, incomplete, or genuinely novel.
Gemini 3 Deep Think and the Re-Emergence of Reasoning
Enter Gemini 3 Deep Think Preview, a new AI model from Google, achieving substantially higher ARC-AGI-2 scores at dramatically higher cost.
What makes Gemini 3 Deep Think interesting is not that it scores higher on a benchmark, it is that it appears to be trying something fundamentally different.
For the past several years, progress in AI has followed a familiar pattern: larger models, more data, faster inference. Gains have been impressive, but increasingly incremental. Performance improves, yet it becomes harder to point to moments where the underlying nature of the system clearly changes.
Gemini 3 Deep Think feels like one of those moments.
Evaluated on ARC-AGI-2, the model does not compete in the same efficiency regime as most frontier systems. Instead, it spends orders of magnitude more computation per task, explicitly trading speed and cost for deeper internal reasoning. That choice matters. It suggests a shift away from the assumption that intelligence emerges primarily from scale, and toward the idea that how a model thinks at inference time may be just as important as how it is trained.
The model’s Preview status reinforces this interpretation. Gemini 3 Deep Think is not presented as a product meant to run everywhere. It is closer to a research instrument, an exploration of what becomes possible when a system is allowed to deliberate, explore alternatives, and reason its way through unfamiliar problems rather than immediately converging on a plausible answer.
From an AGI perspective, this is exciting because it aligns with how humans tackle genuinely hard problems. When the solution is not obvious, we slow down. We test hypotheses. We hold multiple abstractions in mind at once. Gemini 3 Deep Think hints at AI systems beginning to operate in a similar regime.
Implications for Scientific Workflows
For scientific and computational workflows, this opens a compelling possibility. Much of bioinformatics is still throughput-bound, and efficient models will continue to dominate routine analysis. But the most intellectually demanding parts of science (hypothesis generation, mechanistic interpretation, decision-making under uncertainty) are not bottlenecked by compute. They are bottlenecked by reasoning.
What makes Gemini 3 Deep Think exciting is not that it replaces existing tools, but that it suggests a future where AI can be applied selectively to these moments of genuine uncertainty. Not as an automation layer, but as a reasoning partner.
A Shift in How Progress Is Made
Developments like Gemini 3 Deep Think are not signals that AGI has arrived, but they do suggest a change in how progress is being made. As models begin to trade efficiency for deeper reasoning, the implications extend into how scientific insight itself might be augmented. If you are a bioinformatician, it’s time to start thinking about how to apply these capabilities so that your workflows can take advantage of reasoning-centered AI as it becomes more practical.
The question is no longer whether AI belongs in scientific workflows, but where and how it adds the most value. Click here to schedule a free introductory call to learn more about how Bridge Informatics can help your team integrate the right AI tool for your problem.