Tax season doesn't have to mean wondering if you have the right forms, second-guessing your deductions, or scrambling to pull everything together before the deadline.

With BELAY’s tax prep support, you can approach tax season with confidence. Stay organized with one centralized place to gather and check off your documents, keep track of valuable deductions like HSA contributions and education expenses while leaning on experienced professionals who make tax preparation accurate, efficient, and completely hands-off.

Download BELAY's free Personal Tax Checklist and start preparing with confidence, today.

Download the Free Checklist

Two weeks ago, both ChatGPT and Gemini crossed one billion active users. This week, researchers asked children what they actually think about that. The answers are not what the press releases would have written.

MIT Technology Review's August 2026 report, "How Kids Feel About AI, in Their Own Words," gathered perspectives from children and teens across multiple countries and asked them, without leading or framing, to describe their relationship with AI tools. The findings are not a clean story of digital natives thriving. Nor are they a panic narrative about attention spans collapsing. They are something more complicated and more interesting: a portrait of a generation that is genuinely uncertain whether the thinking happening in their presence is theirs.
Several recurring themes emerged from the children's accounts. Many described a certain anxiety about not knowing when to stop using AI assistance and start trusting their own thinking. One teenager, quoted directly, said something close to: she no longer knew if she was bad at writing or if she had just never found out. Without overdramatizing, I would say this describes not a failure of education or technology policy, but a specific kind of epistemic vertigo that has no real historical precedent. Few technologies (if any) have upended the human sense of its own self as violently as AI (and we are still at early stages). This one risks never developing the feedback loop that makes “knowing” possible.
Other students described AI as genuinely useful in ways adults often dismiss. Students with learning differences, language barriers, or simply fewer resources at home reported that AI gave them a first draft delivering confidence, a way in, before they could find their footing on their own. That blank page staring at you as a deadline looms will no longer be a thing. Access to a patient, infinitely available explainer is not a luxury every child has had, and the report is careful to consider that benefit.

The most striking section concerns what kids think learning is for. A meaningful portion of the children interviewed expressed some version of the belief that if a machine can do something, there is no point in a person learning to do it, and that’s problematic. It’s a very logical conclusion, given our cultural moment. The problem is that it collapses the distinction between a capability and the cognitive growth that acquiring a capability produces. You do not learn long division because you will need to divide numbers without a calculator. You learn it because the process of learning it builds something in your brain that transfers to problems long division never touches. And as The Machine takes over more and more of the processes that humans used to have to reason about, our ability to step in (and even assess if The Machine got it right), will weaken. If kids are already thinking “why bother?” then future generations risk never developing an essential muscle.

In some ways, children are doing the philosophy that adults have largely avoided. They are asking “what is thinking for?” and “what learning is for?” and whether those things change when The Machine can do everything for you. As an educational consultant, it’s something I think a lot about, too: what will the universities of the future teach when AI can already reason, strategize, and calculate everything faster and more capably than we can? Humanity must start wrestling with these questions soon.

The generationally distinct perspective here is not naiveté, but proximity. These children are the first humans to develop their cognitive identities alongside systems that can achieve cognition faster and better than they can. Globally, but particularly in the US, students’ test scores have steadily been declining. That’s a different developmental environment than any prior generation has navigated, and it will produce different intuitions about human brains, effort, and authorship.
three things you should know about AI.

Anthropic has begun embedding invisible watermarks in Claude's outputs, and TechCrunch reports that a vocal segment of users is angry, because the feature will expose them using the tool at work and in academic settings without disclosure. The backlash is clarifying: what people are actually defending is not privacy but the social performance of unaided competence, which suggests that the question of authorship in the AI era is far less settled than the productivity discourse pretends. As AI becomes more and more embedded in our life and work, it becomes harder and harder to draw a hard line between human and AI authorship. I use AI to research and edit most of my writing these days; and it’s said that upwards of 60% of software engineers use AI to help them code (or outright code for them). Anthropic watermarking Claude’s output points to a kind of paternalistic hypocrisy that has many in the tech community slowly hardening against them.

Anthropic's research team has published a detailed analysis of patterns and problems in emerging multiagent systems, identifying how AI agents working in concert can fail in ways that individual agents do not, including trust boundary erosion, compounding errors across handoffs, and emergent behaviors that no single agent was designed to produce. If you are building or managing any workflow that delegates decisions to chained AI systems, this is the taxonomy you need before something goes wrong rather than after. It’s also important to educate yourself with agentic workflows now, because as future AI models become more intelligent and complex (even on the single-agent basis), without basic knowledge, you become less and less empowered to assess whether what they’re doing is helpful to you or not.

Google DeepMind has released a sign-language-to-text model designed to be used directly by members of the Deaf and hard-of-hearing community, prioritizing user control and accuracy over novelty. The model is a concrete demonstration that the most laudable AI applications are not the ones that replace human expression, but the ones that extend greater agency to people who have historically been excluded from the infrastructures that the rest of us take for granted.

The extended transcript of Fei-Fei Li's 2025 Stanford HAI annual address, available through Stanford's Human-Centered AI Institute site, is one of the more honest public accounts of what it feels like to be a researcher who helped build a field now reshaping the conditions of research itself. Li speaks specifically about the tension between moving fast enough to stay relevant and slowing down enough to ask: “Is relevance even the right goal?” These are questions we’re likely to keep wrestling with for decades to come.

never forget: the human mind is the original generative machine.