CONTRIBUTED BY
Karolina, SolveStack.ai Team
DATE
Jan 7, 2026
Did you manage to find yourself in the turmoil of the 2025 reflections and 2026 predictions? Paraphrasing Samuel Beanie: 2025 taught us that AI’s momentum is powered by compute and scale, that our intellect for evaluating it lags behind its capabilities, and that the real frontier for next year lies in aligning these huge technical shifts with human systems and decision-making.
Just like him, many experts have expressed what they think is going to happen now and what the technological progress will mean in the upcoming months. Both business-wise and society-wise.
It is indeed interesting to read about expected changes and the new dynamics in the AI sector to come, but digesting all the voices we heard throughout the past week can be a bit overwhelming. We have prepared for you the summary of some of the most captivating 2026 AI insights we have encountered, hoping to help you organize the knowledge and form your own opinion around the new year predictions, so you can answer:
Is it going to be a disruptive or stabilizing era?
The year won’t be defined by dramatic leaps in raw AI capability, but by who controls and governs AI systems and how those decisions are made and executed.
Last year we understood that effective AI governance and decision-making is the key to secure and prosperous use of the new technology. AI regulations began to take shape. Now it is time to put theory in practice on multiple levels – from individual use to national and global political applications. People will now expect both their employers and political leaders to articulate how AI will be governed and whose interests AI benefits.
Aleksandra Przegalińska and Tamilla Triantoro focus on the argument that smarter AI will be defined by who is in charge. Since, rather than just automating tasks, AI will start to take over pattern recognition, optimization, and scaled execution, humans will be the ones who focus on context, values, judgment, and oversight. People will increasingly take on roles involving rules, boundaries and responsibility, which are harder than execution and require thoughtful institutional design.
This development, combined with the opening of new positions related solely to AI specialization demand updates in workforce structures, competence development and curricula. More and more companies will create AI governance, audit, and oversight teams to monitor bias, performance drift, alignment with values, and decide when AI should be paused or overruled.
The focus will shift from merely building hardware to recruiting and retaining AI experts as a national priority. With experience in educational sector, Przegalińska and Triantoro expect companies to partner with universities or open satellite labs to secure key talent outside traditional tech hubs.
The real competitive edge in 2026 won’t be better models, but better systems around them.
Even though Przegalińska and Triantoro shared some general insights into the general AI agents being replaced by more specified and handy systems, Phil Schmid, in his 8 predictions for AI in 2026 shares some more concrete thoughts on this matter.
As AI models commoditize, value shifts to how AI is deployed: agent harnesses, local/on-device agents, human verification, orchestration layers, and strong human judgment in engineering and content. The winners won’t be those with the smartest AI, but those who design the best human–AI interfaces, controls, and trust mechanisms.
Particularly, user interfaces will start being created automatically on the fly and what will matter is that they’ll be increasingly tailored to people’s tasks and preferences, driven by AI that generates code and UI elements in real time. In general, more personalized models, but running on smaller language models, will be deployed – for more context-aware experiences.
Besides more governance-related roles, the role of software engineers will transform due to these developments, too. And the evolution began already. As coding won’t be just writing code, engineers will focus mostly on reviewing, evaluating, conceptualizing, and aligning AI-generated code with business goals.
Schmid also mentions AI becoming a standard element in many of everyday-life areas, such as face recognition as the security measure for more and more apps.
Perhaps the most optimistic and inspiring conclusion Schmid suggested is that content will become increasingly human. In the times when AI generates most of it, human-created materials became somewhat of a ‘premium quality’.
Content is AI Until Proven Human will be a new Content is Human Until Proven AI.
AI capabilities will keep improving steadily in 2026 but we should not expect an explosive (economic) transformation overnight.
The authors of “Understanding AI” blog tell us subtly to hold our horses. Despite rapid adoption and investment, real-world economic growth will remain within historical norms, suggesting AI boosts productivity without dramatically accelerating GDP.
Even though AI has already transformed business, speeding the growth of new services and products, their resilience is in fact not the highest. Many companies arise and many of them fall, failing to monetize their ideas among the competition, and falling prey to temptingly easy MVP building.
At the same time, the AI giants will indeed grow in even more power, reinforcing the imbalance in the AI leadership. According to the experts, major tech companies are projected to exceed $500 B in AI-related capital expenditures in 2026 as they build data centers and infrastructure to support AI workloads. OpenAI and Anthropic are both expected to reach ambitious revenue goals, doubling or more from 2025 levels, indicating maturing business models around AI.
Reinforcement is highlighted over the disruption in this approach. AI’s technical capabilities will improve, but its economic impact, legal frameworks, and competitive landscape will start to settle into stable patterns rather than runaway hype or disruption. Especially, as the courts and regulators will start imposing more meaningful limits and consequences for misconduct.
This view presented by Understanding AI sees 2026 as a year of maturation and integration, where AI moves from early adoption into more mainstream economic, legal, and operational harmony than before.
Will all of this unfold within a single year? That’s uncertain. Will every trend come to pass? Even more so.
Whether AI accelerates rapidly or begins to stabilize, what matters is that we observe, learn, and adapt, both personally and in our professional environments. Let’s support meaningful entrepreneurship and conduct business with values in mind, productively but not cheaply and carelessly.
