Imagine this: What if the complex challenges plaguing your business could be solved instantly and perfectly by an AI that unravels the world's most difficult mathematical problems?

The news that Google DeepMind's latest AI model, Gemini Deep Think, achieved a gold-medal-level performance at the International Mathematical Olympiad (IMO) under the same criteria as humans suggests that this future is just around the corner.

Advanced version of Gemini with Deep Think officially achieves gold-medal standard at the International Mathematical Olympiad

AI Challenges the Limits of Human Knowledge

The International Mathematical Olympiad (IMO), held annually since 1959, is the most prestigious competition for young mathematicians worldwide. Six elite high school students selected from each country participate and attempt to solve six extremely difficult problems in algebra, combinatorics, geometry, and number theory.

Approximately the top 8% of participants receive the esteemed gold medal. In the world of AI, the IMO has long been considered the ultimate test of advanced mathematical problem-solving and reasoning abilities.

Google DeepMind's Breakthrough: Gemini Deep Think Wins Gold

Google DeepMind announced that an advanced version of Gemini Deep Think officially achieved gold-medal-level performance at the 2025 IMO. This AI completely solved 5 out of the 6 IMO problems, earning a total of 35 points.

This result was officially graded and certified by the IMO coordinators using the same criteria as student answers. Gregor Dolinar, President of the IMO, praised Google DeepMind's achievement, noting that the 35-point score was a gold medal score and that the solutions were "clear, precise, and mostly understandable."

This achievement significantly surpasses Google DeepMind's breakthrough from last year.

At the 2024 IMO, the combination of AlphaProof and AlphaGeometry 2 achieved a silver medal equivalent performance (solving 4 out of 6 problems with 28 points). However, that required translating problems from natural language into specialized formal languages like Lean, and computations took 2 to 3 days.

This year's enhanced Gemini Deep Think, however, completed everything from problem description to generating rigorous mathematical proofs entirely in natural language, and did so within the same 4.5-hour time limit as human contestants.

It's as if AI has truly begun to understand human language and think logically.

OpenAI Also Reports Similar Achievements

Interestingly, OpenAI also claims that an unreleased "experimental reasoning model" achieved a gold medal equivalent score at the IMO. Their model, like Google's, solved 5 out of 6 problems, scoring 35 points.

OpenAI states that their model solved the problems in natural language, under the same rules as human competitors, without relying on other tools or the internet. Their solutions were evaluated internally by three former IMO medalists and unanimously approved.

However, the timing of OpenAI's announcement sparked debate. The IMO had apparently requested AI research institutions to wait one week before announcing results to respect the achievements of human competitors.

OpenAI explained that since they were not officially collaborating with the IMO, they made their announcement independently. However, Google DeepMind CEO Demis Hassabis expressed displeasure at this early disclosure.

This series of events felt to me like a symbol of the intense competition in AI development.

Into the Depths of "Thought": The Evolution of AI Reasoning Capabilities

Why has AI become capable of "thinking" and solving such highly complex mathematical problems, almost like a human? This progress is backed by significant advancements in AI's reasoning mechanisms.

A New Approach: Parallel Thinking

One of the most notable techniques employed by Gemini Deep Think is "parallel thinking." Many previous AIs followed a single, linear thought path when solving a problem.

However, in Deep Think mode, the AI can explore multiple possible solutions simultaneously and combine them to derive the best answer, much like humans do when tackling complex problems.

This allows AI to approach problems from various angles and generate creative proofs that might not be found through a single line of thought.

Vast and Efficient Learning Techniques

Gemini Deep Think's improved performance is supported by advanced learning techniques. Specifically, new reinforcement learning methods were applied, utilizing more multi-step reasoning, problem-solving, and theorem-proving data.

Furthermore, the AI was trained with access to a curated corpus of high-quality mathematical problem solutions and provided with general tips and tricks for tackling IMO problems as instructions.

I interpret this as AI, through such precise and extensive learning, being able to generate something akin to the "flashes of insight" that human experts cultivate over time.

Just as a skilled artisan instantly deduces the optimal procedure from a wealth of past experience, AI enhances the quality of its "thought" through vast data and efficient learning.

The Future of AI and Mathematics

Google DeepMind believes that this achievement is just the beginning of AI's contribution to mathematics. By teaching systems flexible and intuitive reasoning, they are getting closer to building AIs that can solve even more complex and advanced mathematics.

They also believe that AI, combining natural language fluency with rigorous reasoning in formal languages, will become an indispensable tool for mathematicians, scientists, engineers, and researchers, contributing to the advancement of human knowledge.

Reality and Future: Is AI's "Thought" Genuine?

While AI exhibits such advanced reasoning capabilities, there's also a debate about what constitutes AI "thought" and whether it truly mirrors human thinking.

The Claim of "Memorization" and Its Implications

Recent research suggests that "reasoning generative AI" isn't actually "thinking" but rather memorizing patterns contained in its training data and returning results by matching them.

For example, experiments using logic puzzles like the "Towers of Hanoi" and the "Chameleon problem" show that while AI can solve known complex tasks, it suddenly fails when parameters of the task are slightly altered or rules are trivially modified.

This might suggest that while AI excels at "applying knowledge," it still faces challenges in "understanding universal principles" and "adapting to unknown situations."

The observation that even when the answer is correct, the "thought process" can be flawed is a crucial perspective when evaluating AI's capabilities. I myself never blindly accept code generated by AI; I always verify its functionality and scrutinize its content.

That's why I believe that for these IMO results, not just the "correct answer" but also the process and robustness of the solution should be truly evaluated.

New Metrics for Measuring "True Reasoning"

Given this background, there's a growing call for new benchmarks to evaluate "true reasoning ability." One example is "Prover Agent," jointly developed by Kyoto University and the National Institute of Informatics, among others.

This method combines a relatively small generative AI (8 billion parameters) with "Lean," a theorem prover that rigorously guarantees the correctness of calculations and proofs. It has achieved an 80% proof success rate on International Mathematical Olympiad-level problems.

Furthermore, it accomplishes this at approximately one-fourth the computational cost of conventional AI methods and significantly reduces the risk of hallucinations (generating incorrect information).

I believe this indicates the possibility of AI achieving a more reliable form of "thought" that not only "remembers" but also "guarantees logical correctness."

Business Applications: A Future Vision for E-commerce Operations

"How can an AI that solves difficult math olympiad problems affect our business?" you might wonder. However, the progress of this "deep-thinking AI" holds the potential to revolutionize conventional practices, especially in complex business environments like e-commerce operations.

Complex E-commerce Environments and the Potential of AI

Modern e-commerce operations face complexities comparable to "International Mathematical Olympiad problems." For instance, the challenges are diverse: setting optimal prices for hundreds to thousands of products, efficiently allocating inventory across multiple warehouses and sales channels, accurately forecasting demand considering seasonal fluctuations, trends, and competitor movements, providing optimal personalized experiences for each customer segment, and allocating advertising budgets most effectively.

These elements are interconnected, and one decision can have significant ripple effects on others, making it an area that truly demands "deep thought."

Many e-commerce businesses to date have used AIs like ChatGPT to generate product descriptions or perform simple data analysis. However, these have been limited to relatively simple tasks.

The Transformation Brought by Deep Think AI

"Deep-thinking AI" like Gemini Deep Think has the potential to revolutionize core e-commerce operations.

• Optimization of Complex Pricing Strategies: Instead of the traditional method of "guessing prices based on competitors," AI will be able to simultaneously consider every factor through parallel thinking—multiple competitors, diverse channels, inventory status, turnover rate, seasonality, trends, price sensitivity per customer segment, promotional synergies, and even long-term brand value impact—to derive the optimal pricing strategy.
• Integrated Inventory Management Strategies: Even in complex situations like "managing 100 SKUs across 3 warehouses and 5 sales channels with seasonal fluctuations and lead times ranging from 14 to 60 days," AI will be able to forecast demand patterns for each product using multiple methods, considering transfer costs between warehouses, channel specificities (return rates, customer demographics, etc.), and even emergency scenarios, to propose optimal inventory allocation.
• Customer Lifetime Value (LTV)-Driven Marketing: AI will concurrently examine and propose strategies that consider not just short-term sales but also customer acquisition cost (CAC), repeat purchase rates, brand image, predicted competitor reactions, and long-term LTV changes over 6 months, 1 year, and 3 years.
• Identifying Market Opportunities in Product Development: AI will be able to simultaneously conduct complex analyses such as extracting latent customer needs from review data, multi-faceted analysis of competitor product strengths and weaknesses, validating trend sustainability through multiple scenarios, optimizing the balance between manufacturing costs and expected revenue, and quantifying cannibalization risks, thereby presenting the most promising product development directions.
• Crisis Management and Risk Response: In response to crises like sudden stockouts of key products, large competitor sales, delivery issues, or the spread of negative reviews, AI can simulate multiple countermeasures simultaneously to help derive optimal solutions that balance short-term damage control and long-term trust recovery.

I am confident that AI addressing business challenges in a way that approaches human "deep thought" will be a true game-changer.

Seizing the Future: What We Must Do Now

Now that AI has won a gold medal at the International Mathematical Olympiad, we humans also need to prepare to maximize the business potential of this technology.

Data Infrastructure Development

To unleash the power of "deep-thinking AI," high-quality data is essential. It's crucial to collect diverse data in a consistent format, fill in missing values, handle anomalies, and establish a real-time update mechanism. This includes detailed sales data from the past few years, customer behavior history (Browse, cart additions, purchases), inventory movements (inbound, outbound, returns), marketing campaign effectiveness, and even external factors like weather, events, and competitor activities.

Data is like the "nutrition" AI needs to deepen its thought.

Problem Structuring Skills

To enable AI to "think deeply," we need the skill to properly organize and structure problems for it. Practice breaking down daily decisions into smaller elements and diagramming how these elements interact.

Also, setting multiple evaluation axes and clearly defining elements with trade-off relationships are crucial hints for AI to derive optimal solutions.

Phased Implementation Plan

When introducing new technology, trying to change everything at once often leads to failure. A phased approach is recommended, where you first test AI's capabilities on a small scale and then gradually expand its application. For example:

• Phase 1 (3 months): Single Problem Validation
  • Apply Deep Think AI to relatively small yet complex problems, such as optimizing the pricing of a specific product.
  • Measure the effects of AI and gather feedback by comparing it with traditional decision-making methods.
• Phase 2 (6 months): Expansion to Compound Problems
  • Apply AI to more complex problems, such as integrating and optimizing multiple elements like pricing, inventory, and promotions.
  • At this stage, it will also be necessary to strengthen inter-departmental collaboration and review KPIs (Key Performance Indicators) in line with AI utilization.
• Phase 3 (12 months): Company-Wide Utilization
  • Ultimately, use AI to solve organizational challenges at the strategic management level, for new business considerations, and for long-term vision formulation.

The Evolution of Human Roles

Even as AI learns to "think deeply," our role as humans will not disappear. Rather, as AI takes over complex routine work and computational thinking, humans can dedicate more time to creative, high-value tasks.

Setting visions and values, making ethical judgments, building emotional connections with customers, and making final decisions and refinements on AI-generated outputs — these remain crucial roles unique to humans. AI will not replace us; it will be our "collaborator," expanding our intellect and enhancing our capabilities.

Now that AI has won a gold medal at the International Mathematical Olympiad, how should we leverage this technology in our businesses and create the future? Are you ready to make this "thinking power" a powerful ally for your business?

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