This is what OpenAI predicts for AI in 2026

It's inevitable to feel a certain level of confusion when every week a new language model appears or a piece of news seems to change everything. [music] But in 2026, the difference between companies that simply use AI and those that lead the market lies in strategy. Today, we're going to go beyond the tools. Join on the latest insights from OpenAI, Stanford, and our own ecosystem of experts, we're going to break down the seven trends that explain where AI [music] is heading this year. And if you feel overwhelmed by the number of new models, [music] here's a hint. The game is no longer about who is better. It's about where it fits seamlessly into your daily workflow. And that's why the first thing to accept is this. The model wars are over. One, from models to ecosystems. A few years ago, we were obsessed with whether GPT-4 was better than Claude. Today, that technical gap has narrowed. Models have become a commodity, something similar to what happened [music] in the automotive industry. At first, what mattered was who had the most powerful [music] engine. Today, what matters is features and comfort. In 2026, the war isn't won through raw power, [music] but through the absence of friction. If your company runs on Google [music] Workspace, Gemini is your natural ally because it already has the context of your emails and Drive without needing to connect anything. If you're looking for reliability [music] in coding, you'll likely turn to Claude. The decision is no longer technical, it's about workflow. If you look closely, most AI tools have been adding integrations with other tools to solve [music] this. Perplexity connects to your Drive, ChatGPT can, too. Adobe has incorporated Google [music] into models, and the next Apple Siri will use Gemini. Whoever wins won't do so because of the model, but because of the ecosystem and [music] the user experience. And here's the interesting part. If the decision is about workflow, then the real superpower is no [music] longer choosing a model. It's designing the system so it works with you. Because if you're still improvising prompts, you're playing in manual mode. Two, stop creating prompts and start designing workflows. There's a lot of talk about 2026 being the year of agents, but Stanford's data is clear. Only 10% of organizations have scaled real autonomous agents. [music] However, 20% of business success happens in workflows with assistance. This is where you move from level one, which is using AI like a search engine, to level two and three. The expert user no longer sticks to just one AI tool. Instead, they create multimodal flows. For example, you might use Claude to run sentiment analysis at scale on a large set of comments because of [music] its technical precision, and then pass that result to ChatGPT to validate whether the sentiment [music] classification is correct. The goal is to turn your favorite prompts into repeatable processes where AI becomes a collaborator, not just [music] a tool. Now, when you turn AI into a repeatable workflow, something remarkable happens. Technology stops being a barrier and becomes an equalizer. [music] Suddenly, teams that weren't technical start doing things that they had never even considered before. Three, AI as a technical equalizer. AI is functioning as a capability equalizer. According to OpenAI, 75% of users are now completing tasks they previously didn't know how to do, such as generating an Excel macro or building a landing page. A real example is the digital avatar agency Clueless. They have a team of 10 people, and surprisingly, only two have a technical background. The rest are creatives who use AI to bridge the technical gap. This doesn't mean developers disappear, but it does mean that the language between marketing and technology is finally the same. But there's a catch. Just because AI can technically level the playing field, doesn't mean it can guess what you actually need. And this is the new problem in 2026. It's no longer about writing the perfect prompt, it's about AI having your context, like your goals, your style, and your criteria. Four, from prompt engineering to the context gap. Knowing how to write the perfect prompt matters less and less because models already understand natural language much better now. So, the new challenge is the context gap. AI may know everything about the world, but it knows nothing about your goals for this quarter or your style guide unless you provide it. More and more, it's necessary to document not just the data, but also the reasoning behind your decisions. In 2026, your success depends on how you configure your AI. You can personalize it at the core level by adjusting the general instructions. For example, adding rules like don't waste tokens or always ask me two questions before executing. [music] Or at the project level with dedicated spaces for specific clients or teams where the AI only draws on [music] that information. But AI isn't only growing in the professional world, it's also expanding in the personal one. An estimated 900 million users now use ChatGPT every week, and the vast majority do so without paying anything, which raises an uncomfortable question. Who is paying for all of this? And the answer seems to be the same as on other platforms, access to powerful models in [music] exchange for advertising. Five, advertising reaches LLMs. Advertising has arrived in chatbots, [music] and although nobody likes seeing ads, OpenAI presents it as a way to democratize [music] access. Just like services such as Spotify, this allows users who cannot afford a pro account to access cutting-edge models. Ad-supported plans are beginning to emerge, a middle-tier option cheaper than Plus that still gives users access to powerful models. What matters for us as marketers is that this advertising does not bias the AI's response, which remains neutral. Instead, it appears linked [music] to users' interest while maintaining the model's reliability. This also changes [music] something deeper. If people begin to consume answers inside chatbots, then your brand no longer [music] competes only for clicks, it's competing to be cited as a reliable source. Six, SEO in 2026, from clicks to citation. Ranking no longer depends on your domain, but [music] on your trust footprint across third-party sources. AI decides whether you are a reliable source [music] by consulting places like Wikipedia, Reddit, Quora, or media outlets. We no longer write just [music] for keywords, we build information that AI systems can understand and cite. Here, public relations tools have become essential because they allow us to distribute press releases [music] designed so that the language models can find and cite us. If AI cannot easily explain [music] who you are, what we call AI readiness, you simply won't exist in the answer it gives to the user. So far, we've been talking about AI on the screen, text, decisions, marketing, but the next leap happens when that intelligence leaves the chat and enters [music] the real world. Because in 2026, AI doesn't just respond, it acts. Seven, machines as platforms, physical AI. Finally, AI has jumped into the physical world. Although truly capable humanoid robots will likely take another 15 years to be deployed at scale, [music] we're already seeing robots powered by Gemini working in factories or autonomous taxis reducing accidents by 96%. [music] The lesson for us is that technological assets are no longer [music] disposable. You don't buy a new machine, you update its software. Hardware becomes a [music] platform that constantly evolves, just like Tesla vehicles or the new augmented reality glasses that generate [music] visual contexts in real time. In short, winning in 2026 isn't about mastering every tool, but about being willing to redesign your processes every day without losing your sanity. [music] If you found this useful, give it a like and subscribe for more. See you in our next video.