AI’s next big leap isn’t just about sounding smarter—it’s about truly thinking through problems. According to a new Bloomberg report, Google is building a dedicated “reasoning AI” effort to close the gap with OpenAI’s latest advances. If you’re a founder, leader, or curious observer, this shift matters: reasoning capabilities could turn today’s chatty assistants into dependable problem-solvers for work and life.

Why this matters now

In the past year, the frontier of AI has moved from fluent conversation to deeper reasoning—planning, multi-step problem solving, tool use, and more reliable answers. OpenAI’s o3 family of reasoning models put a spotlight on this trend. Bloomberg now reports Google is accelerating its own reasoning push to compete—an important signal for the entire industry and anyone betting on AI to drive productivity.

What Bloomberg reported

Bloomberg’s piece says Google is working on a new AI effort focused on reasoning, positioning it to better match or surpass OpenAI’s recent gains. While specifics are still under wraps, the message is clear: Google wants models that can handle complex tasks with fewer mistakes and more trustworthy step-by-step thinking. That means better performance on tasks like analysis, planning, and code—areas where reasoning matters more than pure word prediction.

Source: Bloomberg via Google News.

What is “reasoning AI,” in plain English?

Reasoning AI aims to do more than autocomplete the next word. It tries to break a task into steps, check its own work, and use tools or external knowledge when needed. Think of the difference between:

• Fluency: answering quickly with plausible text.
• Reasoning: planning, calculating, verifying, and revising to reach a correct outcome.

The field has rapidly evolved—from early research like Google’s chain-of-thought prompting (which showed that models do better when they “show their work”) to today’s specialized reasoning models that optimize how LLMs plan and self-check without exposing sensitive intermediate steps.

Where Google stands today

Gemini models and long-context understanding

Google’s Gemini line has steadily improved at handling long documents, codebases, and multimodal inputs (text, images, audio, video). Long context is a building block for reasoning because it lets models consider more evidence before answering. See Google’s recent update on Gemini 1.5 for details on context length and capabilities.

DeepMind’s research track record

• Structured problem-solving: DeepMind has repeatedly advanced machine reasoning in science and math. Notable examples include AlphaFold 3 (biological structure prediction) and follow-on work tackling formal and geometric reasoning, which shows the team’s depth in stepwise problem-solving.
• Agentic assistants: Google’s Project Astra previewed an always-on, multimodal assistant that can perceive, reason, and respond in real time—another key ingredient for practical, reasoning-first AI.

All of this sets the stage for a dedicated reasoning push: stronger models, real-time perception, and a research culture that values scientific rigor.

The competitive landscape: OpenAI, Google, Anthropic

• OpenAI: The o3 series emphasizes deliberate reasoning, with models that take extra “thinking time” to improve correctness on tough tasks. Expect more features aimed at analysis, planning, and tool use.
• Google: Building on Gemini, long context, and agentic systems like Astra, Google’s reported project seeks comparable or better performance on complex reasoning benchmarks and real-world tasks.
• Anthropic: Claude has been strong on reliability and cautiousness. Claude 3.5 Sonnet marked a step up in reasoning, coding, and tool use—underscoring that multiple labs are converging on similar goals.

For customers, this competition is healthy. It should deliver better accuracy, lower latency, more transparent safety controls, and a wider range of price/performance options.

What reasoning AI could unlock

• Operations and planning: From forecasting and supply chain what-ifs to meeting summaries that produce action plans, reasoning models can reduce manual coordination.
• Data analysis: Instead of one-off answers, expect models that examine multiple sources, justify conclusions, and flag uncertainties.
• Customer support: Multi-step troubleshooting that adapts to the user’s context and uses your knowledge base, CRM, and logs.
• Software engineering: Beyond code completion: refactoring plans, test strategy, root-cause analysis, and step-by-step debugging.
• Research and strategy: Structured literature reviews, pros/cons analysis, and scenario planning with citations.

How to evaluate reasoning models (practical checklist)

1) Fit to your task

• Define specific reasoning workloads: planning, math/logic, data synthesis, code, or tool use.
• Start with 3–5 high-value use cases and measurable success criteria.

2) Accuracy and reliability

• Ask for evidence: performance on public benchmarks and your private evals.
• Test multi-step tasks where mistakes compound; require citations or intermediate checks where possible.
• Use adversarial prompts relevant to your domain to probe brittleness.

3) Cost, latency, and scale

• Reasoning often trades speed for accuracy. Measure user impact and pick the right tier (fast vs. deliberate modes).
• Simulate peak loads and long-context prompts to avoid surprises in production.

4) Safety and governance

• Ensure content moderation, red-teaming, and incident response plans are in place.
• Protect sensitive data; consider on-prem or VPC deployment for regulated workflows.
• Align with emerging standards (e.g., the NIST AI Safety Institute) and your internal AI policies.

5) Tooling and integration

• Evaluate tool use and function-calling: can the model reliably orchestrate databases, search, and internal services?
• Look for observability: traces of model steps, tool calls, and outcomes to support debugging and audits.

Open questions and risks

• Faithful reasoning vs. fluent guesswork: Even “reasoning” models can sound confident while being wrong. Treat them like junior analysts—review outputs.
• Privacy and IP: Long-context prompts may contain sensitive data. Use strict access controls and retention limits.
• Safety trade-offs: More capable planning can increase misuse risk if not sandboxed and monitored.
• Evaluation gaps: Public benchmarks can lag behind real-world tasks. Maintain a private eval suite that mirrors your workflows.

Bottom line: “Reasoning” is becoming a product feature, not a research demo. Expect rapid iteration—and plan your adoption with guardrails.

What to watch next

• Google’s upcoming model releases and any dedicated “reasoning mode” or product line.
• OpenAI, Anthropic, and others shipping faster deliberate modes, better tool use, and stronger evals.
• Enterprise features: audit logs, private deployment options, and domain-tuned versions for code, analytics, and support.

Conclusion

Bloomberg’s reporting suggests Google is treating reasoning AI as a top priority—one that could reshape how Gemini competes with OpenAI’s o3 and Anthropic’s Claude. For businesses, the smart move is to pilot now, evaluate rigorously, and choose the right tier of speed vs. accuracy for each workflow. As competition heats up, the winners will be those who combine capable models with careful integration, governance, and continuous evaluation.

FAQs

What is the difference between a chat model and a reasoning model?

Chat models focus on fluent conversation. Reasoning models are optimized to plan, self-check, and solve multi-step tasks—often taking extra “thinking time” to improve correctness.

Will reasoning models replace human analysts or developers?

They’ll augment them first. Expect productivity gains on routine analysis, planning, and debugging. Humans still set goals, review outputs, and handle ambiguity.

How do I know if I need a reasoning model vs. a standard LLM?

If errors are costly, tasks are multi-step, or you need tool use and structured plans, try a reasoning model. For quick drafts or brainstorming, a fast standard model may suffice.

Are there privacy risks with long-context reasoning?

Yes. Long prompts can contain sensitive data. Use data minimization, role-based access, and enterprise features like encryption, redaction, and strict retention policies.

Which vendors lead in reasoning today?

OpenAI, Google, and Anthropic are all investing heavily. Performance varies by task, so run your own evaluations before committing.

Sources

1. Bloomberg: Google Is Working on Reasoning AI, Chasing OpenAI’s Efforts
2. OpenAI: Introducing o3 (reasoning models)
3. Google AI Blog: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
4. Google DeepMind: Introducing Project Astra (real-time, multimodal assistant)
5. Google DeepMind: AlphaFold 3—Accurate predictions of biological structures
6. Anthropic: Claude 3.5 Sonnet
7. NIST AI Safety Institute Consortium