AI that can actually reason is the next big race

AI that simply predicts the next word is useful. AI that can reason  plan, break down problems, check its own work, and use tools  is transformational for business, research, and everyday productivity. According to a new report, Google is building software to strengthen this kind of reasoning in its models, taking a page from OpenAIs o1 reasoning models. If true, it signals an industry-wide shift toward AI systems that think before they answer.

What the report says

Business Standard reports that Google is working on software to boost the reasoning capabilities of its AI models, in a way comparable to OpenAIs o1 approach. While Google hasnt publicly detailed such a system, the effort would fit its broader push to make Gemini and related research better at multi-step problem solving.

Reasoning AI, explained

Traditional large language models (LLMs) are trained to predict the next token. That alone can mimic reasoning, but it often falls short on tasks that require planning, multi-step logic, or verification.

Reasoning-focused models add mechanisms that help them think before answering. Common ingredients include:

• Deliberation: Generating hidden intermediate steps (sometimes called thought tokens) before producing a final answer.
• Process supervision: Training models using feedback on how they solved a problem, not just whether the final answer was right.
• Tool use and verification: Calling calculators, code interpreters, search, or separate verifier models to check work.
• Long context and memory: Holding more information in mind to plan and verify steps.

Research from Google and others showed years ago that prompting models to show their work improves accuracy on math, logic, and planning tasks, an approach known as chain-of-thought prompting and follow-ons like self-consistency.

What OpenAIs o1 changed

OpenAIs o1 family (introduced in 2024) made think-then-answer a first-class capability: models spend extra compute time reasoning privately, then deliver a concise answer. Two notable ideas from the o1 playbook:

• Learning from process, not just outcomes. OpenAI has highlighted process supervision  rewarding the steps a model takes, not only the end result  to reduce errors and hallucinations.
• Separate verification and tool use. o1-style systems often pair a reasoner with code execution, search, or a checker model to catch mistakes before an answer is returned.

OpenAI has also emphasized that o1 models typically do not reveal chain-of-thought steps to users by default, a safety and privacy choice that still preserves the benefits of internal reasoning.

How Google has been approaching reasoning

Google has a long track record in this area:

• Foundational prompting research. Google researchers introduced chain-of-thought prompting, and follow-up work like self-consistency showed that sampling multiple reasoning paths boosts accuracy.
• Geminis long-context planning. Gemini 1.5 introduced million-token context windows, enabling more complex, multi-step workflows, code analysis, and retrieval-augmented reasoning.
• Real-time, multimodal reasoning. At Google I/O 2024, Project Astra illustrated how an assistant could perceive, plan, and respond in real time across modalities.

DeepMind has also explored specialized reasoners that solve formal problems (like geometry or coding) using search, verification, and symbolic tools, a pattern that often outperforms plain next-token prediction on hard tasks.

What Googles reported software could look like

Business Standard doesnt provide technical details, but based on public research trends, a Google system similar to o1 would likely combine:

• Deliberate reasoning passes: The model thinks internally before answering, allocating more compute to tricky questions and less to easy ones.
• Process-aware training: Rewarding good intermediate steps using curated traces, synthetic data, or verifier feedback.
• Verifier and tool orchestration: Integrating calculators, code execution, search, and fact-checkers to reduce logical and factual errors.
• Selective disclosure: Returning concise answers to users while keeping raw chain-of-thought private for safety and IP protection.

Whether this lands as a new Gemini capability, a developer option (e.g., a Thinking mode), or specialized models for coding, math, and analysis will depend on performance, cost, and safety trade-offs.

Why this matters for entrepreneurs and professionals

Reasoning models are especially valuable when tasks are complex, high-stakes, or multi-step. Practical wins include:

• Analysis and decision support: Summarizing messy inputs, evaluating trade-offs, and proposing step-by-step plans with transparent assumptions.
• Technical workflows: Debugging code, writing tests, and verifying outputs by running snippets or checking specs.
• Operations and compliance: Cross-referencing policies, contracts, and logs to flag risks or inconsistencies before they escalate.
• Customer and sales: Drafting proposals or replies that incorporate calculations, product constraints, and a customers history.

How to prepare and get value today

You dont need to wait for a new release to benefit from reasoning AI. Start with these steps:

• Pick the right model for the job. Use strong, general-purpose models for complex tasks; use faster, cheaper models for routine classification and extraction. If available, enable reasoning or thinking modes only where they add value.
• Prompt for structure. Ask the model to think in stages: Outline your plan, list assumptions, compute, then answer. Even without revealing chain-of-thought, many models perform better when given a staged scaffold.
• Add verification. Pair the model with tools: calculators, code runners, retrieval, and validators. Ask it to show the checks you performed (without exposing raw chain-of-thought).
• Measure what matters. Track exact-match accuracy on ground-truth tasks, unit-test pass rates for code, latency, and cost per solved task. A slower thinking mode is worth it only if it meaningfully improves outcomes.
• Protect sensitive reasoning. Avoid logging raw chain-of-thought. Store short rationales or verdicts instead, and enforce least-privilege access to prompts, traces, and data.

Risks and open questions

• Reliability and verifiability: Reasoning models can still make confident mistakes. Build checks and second opinions into critical workflows.
• Latency and cost: More thinking means more tokens and compute. Use it selectively.
• Evaluation is hard: Benchmarks can be gamed. Favor task-specific, real-world tests and hidden evals.
• Safety and privacy: Chain-of-thought traces may expose sensitive data or internal methods. Keep them private by default.

Bottom line

If Google is indeed building o1-style reasoning software, it confirms where AI is headed: systems that plan, verify, and use tools to produce more trustworthy answers. For teams, the playbook is clear. Start adopting reasoning patterns now, layer in verification, and measure gains against cost and latency. When new thinking capabilities arrive, youll be ready to plug them in.

FAQs

What is 1creasoning AI1d in simple terms?

Its AI that breaks problems into steps, checks its work, and uses tools before answering. Think of it as moving from guess the next word to plan, verify, and then reply.

How is OpenAIs o1 different from regular LLMs?

o1 allocates compute to internal thinking and is trained with feedback on the process of solving problems, not just the final answer. It also uses tools and verification to reduce errors.

Is Google already doing this?

Google has published core research on prompting and long-context reasoning and has showcased real-time, multimodal assistants. The Business Standard report suggests Google is building software to push this further, in a way comparable to o1.

Will reasoning modes slow my application down?

Yes, typically. The model thinks more, which costs time and tokens. Use them where the accuracy boost pays for itself.

How can I try reasoning patterns today?

Use staged prompts, add tool use (like code execution or calculators), and evaluate with unit tests or exact-match scoring. Keep chain-of-thought private; store brief rationales instead.

Sources

1. Business Standard: Google working on software for reasoning AI, similar to OpenAIs o1
2. OpenAI: Reasoning models (o1)
3. OpenAI: Process supervision
4. Google AI Blog: Chain-of-thought prompting elicits reasoning in LLMs
5. Wang et al., Self-Consistency Improves Chain of Thought Reasoning
6. Google: Introducing Gemini 1.5
7. Google I/O 2024: AI keynote highlights