Why 2026 Will Be Shaped by Many Small AI Breakthroughs, Not One Big Leap

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In 2025, AI agents emerged as a key technological trend, with many businesses transitioning from merely discussing AI to actively utilizing it in critical decision-making processes.

According to a McKinsey report, nearly all respondents indicated that their organizations utilized AI, with 62% stating they were experimenting with AI agents. Predicting AI agents to take center stage has been accurate. Nonetheless, the technology offers more than just agents.  

In 2026, as the hunger to dominate the AI landscape intensifies, new bets are being made, with over $500 billion expected to be invested by major companies, according to a consensus among Wall Street analysts.

​Here are five predictions experts are doubling down on for the year:

1. Learning the Language and Understanding the Context

To date, most AI models we’ve been toying with have predesigned algorithms working behind the scenes. These are trained to statistically predict the most probable next word in a sequence. Think of them as advanced detectives. Based on patterns learned from massive amounts of data, they use systems such as the Transformer architecture to understand context and grammar, and then refine responses with human feedback (RLHF).

However, AI has not yet achieved artificial general intelligence (AGI) and cannot fully serve as a co-pilot for human activities and tasks, like coding and development. While AI is an excellent tool for technical professionals, it requires its fair share of human supervision.

However, a new trend is emerging.

As GitHub gains over one new developer per second, reaching 36 million in 2025, the community creates over 230 new repositories per minute and merges 43.2 million pull requests on average each month. This growth indicates that AI has become integral to the development and improvement of software.

​Mario Rodriguez, GitHub’s chief product officer, predicts that due to the volume boom, 2026 will see “repository intelligence,” an AI’s ability to understand the code and the reasons behind it.

​By analyzing patterns in code repositories, AI will be able to ascertain what changed and how the pieces will fit together. “It’s clear we’re at an inflection point,” he says in a Microsoft blog, adding that repository intelligence “will become a competitive advantage by providing the structure and context for smarter, more reliable AI.”

2. Making Healthcare Accessible

Healthcare has always been touted as one of the biggest beneficiaries of AI advancements. Post-revolutionizing diagnosis (radiology, pathology), treatment (precision medicine, drug discovery), and operations (robotic surgery, virtual assistants, automated documentation), AI in healthcare is witnessing a turning point, according to Dr. Dominic King, Vice President of Health at Microsoft AI.

In late 2024, Imperial College London and the University of Edinburgh trained their software on a dataset of 800 brain scans from stroke patients and then trialed it on 2,000 additional patients. The result? It was able to detect the timescale of the stroke, a crucial detail for professionals.

This is just one of the few areas in which AI is reshaping healthcare and medical treatments globally. But there is more ground to cover.

​“We’ll see evidence of AI moving beyond expertise in diagnostics and extending into areas like symptom triage and treatment planning,” King says. “Importantly, progress will start to move from research settings into the real world, with new generative AI products and services available to millions of consumers and patients.”

A McKinsey article highlights that AI innovation is poised to spark two key shifts: leaders will now look to lay the groundwork for a modular, connected AI architecture, which will connect point solutions, data infrastructure, and intelligent agents into a larger picture, while healthcare organizations will transform vast stores of patient records into key enablers by creating clinical-data foundries. “Data governance will be at the core of both,” the article read.

The World Health Organization forecasts a deficit of 11 million health workers by 2030, leaving 4.5 billion people without access to vital health services. The integration of technology into new healthcare sectors and the development of practical solutions are crucial as the global community faces ongoing healthcare challenges.

3. Physical Embodiment of AI

“Robotics and physical AI are definitely going to pick up,” said IBM’s Peter Staar on a shift in AI research priorities for 2026.

Robots powered by physical AI are a living reality in cities such as Singapore, Dubai, Tokyo, London, and New York. AI-enabled drones, autonomous vehicles, and other robots are being leveraged to inspect power grids, assist in surgery, navigate city streets, and help humans in warehouses.

​Unlike traditional robots, which follow a set of predefined instructions, physical AI systems are designed to analyze the environment, learn on the go, and adapt their behavior in real time as conditions evolve.

​Technological advancements poised to drive physical AI–robotics integration include vision-language-action models and onboard computing and processing capabilities. Computer vision, sensors, actuators, and spatial computing have made robots more accessible and capable.

​Waymo, Aurora Innovation, Amazon, and BMW are among the companies now using these systems at scale. However, key physical barriers remain a challenge. “Visual images in simulated environments are pretty good, but the real world has nuances that look different,” says Ayanna Howard, dean of the College of Engineering at The Ohio State University, in a Deloitte article 

“A robot might learn to grab something in simulation, but when it enters physical space, it’s not a one-to-one match.” Issues like that are expected to improve by 2026.

4. Open Source Diversification and the Rise of SLMs

The past year also marked the rise of small language models (SLMs), a trend that PyTorch Foundation’s executive director, Matt White, had predicted. “The industry validated the thesis,” he shares with IBM Think. ​Tech giants were quick to roll out Phi (Microsoft), Gemini Nano (Google), and GPT-4o mini (OpenAI). 

Companies are leveraging SMLs for a more targeted, efficient, and cost-effective approach to implementing AI. Several factors, including cost considerations and data privacy concerns, have driven its adoption among organizations.

“When you look at the large firms, they’re all saying: ‘How do we take charge of our AI destiny?’ Small language models trained on precise data are actually quite effective,” Nandan Nilekani, Chairman and Co-founder of Infosys, shared with the Financial Times.

NVIDIA researchers recommend leveraging SLMs for routine, narrow tasks and reserving LLMs for more complex reasoning. SLMs can be finetuned to specific tasks, resulting in faster iteration cycles and adoption for use cases.

Meanwhile, White now predicts three forces that will drive open source to new advancements in 2026: global model diversification, interoperability as a competitive axis, and strengthened governance. ​“Developers need flexible tooling for multimodal reasoning, memory components, and safety-aligned evaluation, and that’s where open source thrives,” he adds.

Staar notes that open-source AI will keep the landscape competitive: “The ones in the lead want to keep it closed, and the ones catching up go open.”

Anthony Annunziata, Director of Open Source AI at IBM, notes that if the future is automated AI capabilities doing major work, then the standards of interaction need to be open, “otherwise, you end up with fragmented silos, or a winner-take-all platform.”

5. AI for Cybersecurity

Deploying AI at scale has become imperative for organizations. However, the cracks began to show as the same technology introduced advanced security risks such as shadow AI deployments and AI-accelerated attacks.

​IBM’s data breach 2025 report revealed that the global average cost of a data breach stood at $4.4 million. ​As AI security risks emerge across data, AI models, applications, and infrastructure, organizations continue to grapple with emerging threats—elements that were previously unaccounted for or unanticipated.

​Several existing security practices can be adapted to address AI-specific risks. For instance, Rich Baich, Senior Vice President and Chief Information Security Officer at AT&T, is enforcing strong software development life cycle approaches. “What we’re experiencing today is not much different than what we’ve experienced in the past…The only difference with AI is speed and impact,” says Baich in the Deloitte article.

While AI introduces new vulnerabilities, it also provides powerful defensive capabilities. One strategy is red teaming, a method that involves rigorous stress testing and challenging of AI systems to identify vulnerabilities and weaknesses before real adversaries can exploit them.

​Governance has also become a key strategy. An emerging trend is the assignment of audit committees to review and assess AI-related activities continually.

In 2026, AI will not advance on the back of one big leap, but rather on several small yet crucial moments.