Toshifumi Kuga

December 6, 2025

ChatGPT, Gemini 3, Google, Nano Banana Pro, GPT-5

The OpenAI Code Red: What’s Next for the Generative AI Market?

Toshifumi Kuga

December 6, 2025

ChatGPT, Gemini 3, Google, Nano Banana Pro, GPT-5

In late November 2022, OpenAI released ChatGPT. It has been three years since then, and just as it was about to celebrate its third birthday, an event occurred that dampened the celebratory mood. CEO Sam Altman declared a "CODE RED" (Emergency) (1). The driving force behind this was the breakthrough of the new generative AI, "Gemini 3" (2), released by Google on November 18. Today, I would like to delve into this theme and forecast the generative AI market for 2026. Let’s get started.

1. Gemini 3 vs. GPT-5

On August 6, 2025, OpenAI released GPT-5. Since it was the first major update since GPT-4, people had very high expectations. However, in reality, it was difficult to perceive a significant difference compared to other models. Although it managed to update scores across various benchmarks, the impression was that its impact felt somewhat muted compared to the arrival of GPT-4.

Of course, it is evolving steadily, so if rival companies' models had remained stagnant, I believe it could have celebrated its third birthday peacefully. However, the moves made by its rival, Google, surpassed our expectations. On November 18, 2025, Gemini 3 was released, and everyone was astonished by its high performance. Its scores in almost all benchmarks surpassed those of GPT-5, and for the first time since the birth of ChatGPT, GPT-5 lost its "technological competitive advantage." The battle surrounding generative AI has entered a new phase.

2. Why Gemini 3 is Particularly Superior

There are several technical talking points, but what I am paying special attention to is its high capability in image processing and generation. As shown in the leaderboard (3) below, its strength is overwhelming and unrivaled. The famous image generation app Nano Banana Pro is officially named Gemini 3-Pro-Image, and its high scores truly stand out.

When considering individual customers, the ability to easily generate and edit images exactly as envisioned is crucial and can serve as a "killer app." I feel that once individuals experience the technical level of Gemini 3, they will find it difficult to easily switch back to competitor apps. The image below was generated using Nano Banana Pro. As you can see, it has become easy to render both English and Japanese text together on an image. Previously, Japanese text was often incomplete or incomprehensible, so it was quite moving to see clean Japanese generated for the first time.

3. The Generative AI Market in 2026

With Sam Altman issuing a CODE RED, I believe OpenAI will allocate significant development resources to improving the model itself and will frantically work to close this gap in the image generation field. On the other hand, Google, armed with Gemini 3, possesses several multimodal generative AI models beyond just Nano Banana Pro, and I expect them to leverage that expertise to aim for further breakthroughs.

In particular, generative AI capable of simulation using 3D structures—known as World Models—will likely influence Large Language Models (LLMs) as well, solidifying Google's competitive advantage. One has to admit that Google, which owns YouTube, is incredibly strong in this field. It looks like 2026 will be a year where we cannot take our eyes off how OpenAI launches its counterattack.

How was it? While there are several other players creating generative AI, I believe the industry style will involve companies defining their own positions within the context of the "OpenAI vs. Google" battle. Therefore, the outcome of OpenAI vs. Google is extremely important for all AI-related companies. I would like to write another blog post on this same theme if the opportunity arises.

That’s all for today. Stay tuned!

You can enjoy our video news ToshiStats-AI from this link, too!

1) Sam Altman’s ‘Code Red’ Memo Urges ChatGPT Improvements Amid Growing Google Threat, Reports Say, Forbes, 2 Dec 2025
2) A new era of intelligence with Gemini 3, Google, 18 Nov 2025
3) Leaderboard Overview,

Notice: ToshiStats Co., Ltd. and I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithms or ideas contained herein, or acting or refraining from acting as a result of such use. ToshiStats Co., Ltd. and I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on ToshiStats Co., Ltd. and me to correct any errors or defects in the codes and the software.

Toshifumi Kuga

September 8, 2025

GPT-5, generative ai, marketing, sales forecasting

Unlocking Sales Forecasts: Can GPT-5 Reveal the Most Important Data?

Toshifumi Kuga

September 8, 2025

GPT-5, generative ai, marketing, sales forecasting

Have you ever found yourself in marketing, wanting to predict sales and gathering a ton of data? For example, let's say you have sticker sales data (1) like the set below. The num_sold column represents the number of units sold. This is actually a large dataset with over 200,000 entries. So, among these data columns (which we call "features"), which one is the most important for predicting sales? They all seem important, and it's impossible to check all 200,000 records one by one. So, let's try asking the generative AI, GPT-5.

1. Asking GPT-5 with a Prompt

To identify the important features for a prediction, you first have to create a predictive model. This is a task that data scientists perform all the time. However, they usually create these models by coding in Python, which can be a high barrier for the average business person. So, isn't there an easier way? Yes, and this is where prompts come in handy. If you can give instructions to GPT-5 with a prompt, no coding is necessary. Here is the prompt I created for this task.

Key points of the prompt:

Use HistGradientBoostingRegressor from sklearn.
Evaluate the error using mean_absolute_percentage_error.
Split the data into train-data and test-data at an 80:20 ratio.
Display the top 10 feature importances with their original variable names.
Print the results as numerical output.

By getting the top 10 feature importances, we can understand which data column is the most significant. I won't explain the predictive model itself this time, so for those who want to dive deeper, please refer to a machine learning textbook.

2. The Code Actually Being Executed

Based on the prompt above, GPT-5 generated the following Python code on its own. It might look complicated to non-specialists, but rest assured, we don't have to touch Python at all. However, we can review this code to see how the calculation is being done, so it's by no means a black box. I believe this transparency is very important when using GPT-5 in a business context.

GPT-5's code for building the prediction model

3. "Product" Was the Most Important!

Ultimately, we got the following result.

Feature Importance Ranking

A higher "importance" value in the table above means the feature is more significant. This analysis revealed that "product" was overwhelmingly important. It seems that thinking about "what is selling" is essential. This is followed by "store" and "country". This suggests that considering "in what kind of store" and "in which country" is also crucial.

So, what did you think? This time, we instructed GPT-5 with a prompt to calculate which features are most important for predicting sales. It's true that you might run into errors along the way that GPT-5 has to correct itself, so I felt that having some basic knowledge of machine learning is beneficial. However, we were able to get the result without the user having to write any Python, which means marketing professionals can start trying this out today. I hope you can use the method we introduced today in your own marketing work. That's all for now. Stay tuned!

You can enjoy our video news ToshiStats-AI from this link, too!

1)Forecasting Sticker Sales, kaggle, January 1,2025

Toshifumi Kuga

August 31, 2025

Metaprompting, GPT-5, OpenAI

How to Turn GPT-5 into a Pro Marketing Analyst with AI Agents！

Toshifumi Kuga

August 31, 2025

Metaprompting, GPT-5, OpenAI

A while back, I introduced a guide to prompting GPT-5, but it can be quite a challenge to write a perfect prompt from scratch. Not to worry! You can actually have GPT-5 write prompts for GPT-5. Pretty cool, right? Let's take a look at how.

1. Using GPT-5 to Do a Marketer's Job

I have some global sales data for stickers(1). Based on this data, I want to develop a sales strategy.

In a typical company, a data scientist would analyze the data, and a marketing manager would then create an action plan based on the results. We're going to see if we can get GPT-5 to handle this entire process. Of course, this requires a good prompt, but what kind of prompt is best? This is where it gets tricky. The principle I always adhere to is this: "Data analysis is a means, not an end." There are many data analysis methods, so the same data can be analyzed in various ways. However, what we really want is a sales strategy that boosts revenue. With this in mind, let's reconsider what makes a good prompt.

It's a bit of a puzzle, but I've managed to draft a preliminary version.

2. Using Metaprompting to Improve the Prompt with GPT-5

Now, let's have GPT-5 improve the prompt I quickly drafted. The image below shows the process. The first red box is my draft prompt.

The second red box explicitly states the principle: "Perform data analysis with the goal of creating a Marketing strategy." When you provide the data and run this prompt, GPT-5 creates the improvement suggestions you see below, which are very detailed. I actually ran this process twice to get a better result.

3. The Result: GPT-5 Generates MARKETING Strategy!

Running the final prompt took about a minute and produced the following output. The detailed analysis and resulting insights are directly connected to marketing actions, staying true to our initial principle. It's fantastic.

The output is concise and perfect for busy executives. Creating this content on my own would likely take an entire day, but with GPT-5, the whole process—including the time it took to draft the initial prompt by myself —takes only about 30 minutes. This really shows how powerful GPT-5 is.

What do you think? This time, we explored a method for getting GPT-5 to improve its own prompts. This technique is called Metaprompting, and it's described in the OpenAI GPT-5 Prompting Guide (2).

I encourage you to try Metaprompting starting today and take your AI agent to the next level. That's all for now! Stay tuned!

You can enjoy our video news ToshiStats-AI from this link, too!

1)Forecasting Sticker Sales, kaggle, January 1,2025

2) GPT-5 prompting_guide, OpenAI, August 7, 2025

Toshifumi Kuga

August 24, 2025

AI agent, GPT5, generative ai, promt, prompt engineering

Let's Explore the Best Practices for Crafting GPT-5 Prompts!

Toshifumi Kuga

August 24, 2025

AI agent, GPT5, generative ai, promt, prompt engineering

We are already hearing from many in the field that with the arrival of GPT-5, "the writing style is different from GPT-4o and earlier" and "its performance as an agent is on another level." Here, we will build upon the key points from OpenAI's "GPT-5 Prompt Guide (1)" and organize, from a practical perspective, "how to write prompts to stably reproduce desired behaviors." The following three keywords are key:

GPT-5 acts very proactively as an AI agent.
Self-reflection and guiding principles.
Instruction following with "surgical precision."

Let's delve into each of these.

1. GPT-5 acts very proactively as an AI agent.

GPT-5's enhanced capabilities in tool-calling, understanding long contexts, and planning allow it to proceed autonomously even with ambiguous tasks. Whether you "harness" or "suppress" this capability depends on how you design the agent's "eagerness").

1-1. Controlling Eagerness with Prompts

To suppress eagerness, intentionally limit the depth of exploration and explicitly set caps on parallel searches or additional tool calls. This is effective in situations where processing time and cost are priorities, or when requirements are clear and exploration needs to be minimized.

To enhance eagerness, explicitly state rules for persistence, such as "Do not end the turn until the problem is fully resolved" and "Even with uncertainty, proceed with the best possible plan." This is suitable for long-duration tasks where you want the agent to see them through to completion with minimal check-ins with the user.

Practical Snippet (To suppress eagerness):

<context_gathering>
Goal: Reach a conclusion quickly with minimal information gathering.
Method: A single-batch search, starting broad and then narrowing down. Avoid duplicate searches.
Budget: A maximum of 2 tool calls.
Escape: If a conclusion is reasonably certain, accept minor incompleteness to provide an early answer.
</context_gathering>

Practical Snippet (To encourage eagerness):

<persistence>
Do not end the turn until the problem is completely resolved.
Reason through uncertainty and continue with the best possible plan.
Minimize clarifying questions. Adopt reasonable assumptions and state them later.
</persistence>

1-2. Visualize with a "Tool Preamble"

When the agent outputs a long rollout during execution, having it first provide a brief summary—explaining the objective, outlining the plan, noting progress, and confirming completion—makes it easier for the user to follow along and creates a better user experience.

Recommended Snippet:

<tool_preambles>
First, restate the user's goal in a single sentence. Follow with a bulleted list of the planned steps.
During execution, add concise progress logs sequentially.
Finally, provide a summary that clearly distinguishes between the "Plan" and the "Actual Results."
</tool_preambles>

2. Self-reflection and Guiding Principles

GPT-5 excels at "internally refining" the quality of its output through self-reflection. However, if the criteria for judging quality are not established beforehand, this reflection can become unproductive. This is where guiding principles and a private rubric are effective.

2-1. Provide a "Self-Grading Scorecard" with a Private Rubric

For zero-to-one generation tasks (e.g., creating a new web app, drafting specifications), have the model internally create a scorecard with 5-7 evaluation criteria. Then, have it repeatedly rewrite and re-evaluate its output based on these criteria.

Rubric Generation Snippet:

<self_reflection>
Define the conditions that a world-class deliverable should meet across 5-7 categories (e.g., UI quality, readability, robustness, extensibility, accessibility, accountability). Score your own proposal against these criteria, identify shortcomings, and redesign. The rubric itself should not be shown to the user.
</self_reflection>

2-2. Reduce Inconsistency with Guiding Principles

For ongoing development or modifying existing code, first provide the project's conventions by clearly stating its design principles, directory structure, and UI standards. This ensures that the model's suggested improvements and changes integrate naturally with the existing culture.

Guiding Principles Snippet (Example):

<guiding_principles>
Clarity and Reusability: Keep components small and reusable. Group them and avoid duplication.
Consistency: Unify tokens, typography, and spacing.
Simplicity: Avoid unnecessary complexity in styling and logic.
</guiding_principles>

2-3. Separately Control Verbosity and Reasoning Effort

GPT-5 can control its verbosity (the length of the final answer) and its reasoning_effort (the depth of thought) independently. This allows for context-specific overrides, such as "be concise in prose, but provide detailed explanations in code." The guide introduces a practical example of prompt tuning by Cursor, which is worth checking out. A useful tip for fast mode (minimal reasoning) is to require a brief summary of its thinking or plan at the beginning to assist its process.

3. GPT-5's Instruction Following has "Surgical Precision"

GPT-5 is extremely sensitive to the accuracy and consistency of instructions. Contradictory requests or ambiguous prompts waste reasoning resources and degrade output quality. Therefore, it is crucial to "structure" your instruction hierarchy to prevent contradictions before they occur.

3-1. Design to Avoid Contradictions

Take the example of a healthcare administrator scheduling a patient appointment based on symptoms. "Exceptions," such as altering preceding steps only in emergencies, must be clearly stated so they do not conflict with standard procedures.

Bad Example: The instructions "Do not schedule without consent" and "First, automatically secure the fastest same-day slot" coexist.
Correct Example: When "Always check the profile" and "In an emergency, immediately direct to 911" coexist, the exception rule is declared first.

OpenAI offers the following warning:

We understand that the process of building prompts is an iterative one, and that many prompts are living documents, constantly being updated by different stakeholders. But that’s why it is even more important to thoroughly review for instructions that are phrased improperly. We have already seen multiple early users discover ambiguities and contradictions within their core prompt libraries when they did such a review. Removing them dramatically streamlined and improved GPT-5's performance. We encourage you to test your prompts with our Prompt Optimizer tool to identify these kinds of issues.

How was that? In this article, we explored key points for prompt design from OpenAI's GPT-5 Prompt Guide (1). GPT-5 is a "partner in practice," combining powerful autonomy with precise instruction following. Try incorporating the points discussed today into your prompts and take your AI agents to the next level. That's all for today. Stay tuned!

1) GPT-5 prompting_guide, OpenAI, August 7, 2025

You can enjoy our video news ToshiStats-AI from this link, too!

Toshifumi Kuga

August 17, 2025

GPT5, OpenAI, generative ai

Unexpected Weakness Revealed! What Happened When I Tried Image Analysis with the New "GPT-5" Generative AI from OpenAI

Toshifumi Kuga

August 17, 2025

GPT5, OpenAI, generative ai

OpenAI's New Generative AI "GPT-5" Has Arrived. I Tried Image Analysis and Discovered a Surprising Weakness! (1)

The long-awaited new generative AI, "GPT-5," has been released by OpenAI. I believe its multimodal capabilities have also improved, so I decided to upload a few images and run some simple tests. Let's get started.

1.The car is stopped, but why is it stopped?

The image shows a Mazda passenger car on display inside a train station (Hiroshima Station). This is just an exhibit car, but I thought GPT-5 could answer if it understood the background. It seems to have correctly recognized that this is an indoor space and not a public road. The answer was correct.

2.How many minutes until departure?

This is a common scenario when traveling. I asked how many minutes until the train I was planning to board, "Nozomi 104," would depart. The key was whether GPT-5 could understand that the large displayed time was the current time. This time, it also worked out well.

3.Which way should I go for car number 4?

This is another common travel situation. At a Shinkansen platform at Tokyo Station, I wanted to go to car number 4, and I asked which way to go, left or right, based on the sign above. The result was correct.

4. I want to go to Shin-Osaka Station. How many trains can I take?

The last one is a difficult question. This is a Shinkansen information board at Tokyo Station, and it shows 16 trains in total. When I asked, "I want to go to Shin-Osaka Station," it replied with 8 trains. This is the number of trains with Shin-Osaka as the destination, which is a bit of a simplistic answer. For example, a Shinkansen bound for Hakata also stops at Shin-Osaka. It seems that GPT-5, in its default mode, didn't think that far ahead.

To redeem itself, I switched to "Thinking" mode and tried one more time. As expected, it considered the intermediate stops and answered 14 trains, excluding the trains bound for Nagoya. That's the correct answer.

So, what do you think? Overall, the performance is excellent. GPT-5 is said to use a "real-time router" that defaults to "Auto" and automatically switches to "Thinking" for difficult tasks. However, since it's just been released, this switching might not always work perfectly. As the examples above show, although "Thinking" mode was appropriate in some cases, it didn't activate automatically. Therefore, if you feel something is "a little off," I recommend switching to "Thinking" mode. I hope it will become more stable over time. I look forward to covering GPT-5 again in the future. Stay tuned!

1) GPT-5 System Card., OpenAI, August 7, 2025