[Generative AI] 5 Trends to Keep an Eye On | June 2026 Summary | (Generative AI / AI News / AI Agents / Tried with AI / AI Utilization)
Hello everyone. This is Shinya Latte.
I have summarized the movements in generative AI since the beginning of 2026 in a way that is as compact as possible, though perhaps not quite a one-minute read. Rather than an expert's analysis, this is a summary of what I have felt as someone who uses these tools every day: this is the current trend.
I made a summary note of trends for my own use, so I decided to turn it into an article. I hope it will be helpful for those who are also interested but haven't been able to keep up.
What has changed in 2026?
Roughly speaking, generative AI has moved past the stage of "just trying it out," and there is now an atmosphere where whether it can actually produce results in work and life is being questioned.
A few years ago, the focus was on the surprise that "amazing technology has emerged," but now I feel the interest has shifted to "So, what has become easier because of it?"
At the same time, we have moved from a period where "using ChatGPT is enough" to a trend of using different tools for different purposes. With more tools available, I also feel that choosing the right one has become a bit of a challenge.
Trends to keep an eye on (my own 5 points)
1. AI is becoming more like a "colleague"
There has been an increase in "AI agents" that don't just wait for instructions but can plan and act on their own to some extent.
For example, we are becoming able to entrust multiple steps at once, such as "research this and create a draft."
As of 2025, very few companies had implemented such systems, but I have seen predictions that by the end of 2026, about 40% of corporate applications will be equipped with task-specific agents.
2. From "tried it out" to "did it pay off?"
There are surveys suggesting that about 70% of companies have introduced it but are yet to see results, while only about 30% have been able to connect it to solid results.
There is also talk that those who are achieving results have reduced their operational costs by about 10% to 30% on average.
The difference seems to be whether it remains an individual's "trick" or whether it has been turned into a system as a procedure for the entire team.
3. The power balance has started to shift
The market share that was dominated by ChatGPT is gradually declining, while Gemini is growing, Claude is growing, and names like Grok are also being heard more often.
The top of the rankings changes frequently, so rather than 'always using the #1 model,' I think it's more realistic to 'combine the ones thatsuit your specific needs.'
The idea is to choose based on strengths, such as using this one for writing consultations, that one for reading long texts, and another for research.
4. 'Thinking before answering' is becoming the norm
Instead of responding immediately, a mode that thinks through the reasoning before answering has become standard. It is reliable for complex problems and tasks that require logical thinking.
At the same time, 'multimodal' capabilities that can handle images, audio, and video alongside text have become commonplace, making it natural to use features like showing an image to ask a question.
5. Moving toward the real world and local LLMs
AI is expanding beyond smartphones and PCs into the real world, such as with robots (Physical AI). 'World models' that simulate physical movements are also gradually gaining attention.
On the other hand, small, specialized models like 'local LLMs' that run on your own device are also gaining presence. The background for this seems to be the peace of mind regarding privacy and costs.
Recent model trends (in brief)
Even in the last month or two, major companies like OpenAI, Anthropic, and Google have been announcing new models and features one after another, at a pace that is honestly hard to keep up with.
I personally think there is no need to follow everything strictly; just trying out one thing that catches your interest is enough.
Thoughts after using them
I also only used ChatGPT at first, but recently I've started separating them by use case—'this one for reading long texts,' 'that one for research'—and I feel that is much easier.
I have started delegating small tasks and finishing them off myself at the end more often. It doesn't always go well, but I find the trial and error involved in that process interesting.
However, since there have been times when I got into trouble by trusting the output too much just because it was convenient, checking with my own brain at the end is something I want to continue to value.
As the development of rules is also progressing, a stance of 'using it thoroughly while being careful' rather than 'not using it at all' might be just right.
Rather than trying to keep up perfectly, finding just one way to use it in your daily life feels like a good sense of distance to maintain.
Thank you for reading. See you in the next article.
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