Artificial intelligence redefines the business ecosystem: What challenges await the sector in 2026?

The software world has reached a saturation point. The next phase of transformation will no longer be digital — it will be physical. Tech companies that have learned to manipulate bits must now face the reality of atoms. This challenge opens countless opportunities but also carries consequences that the industry is only beginning to address.

Physical Foundations: From Code to Infrastructure

The energy and manufacturing industries will become natural AI laboratories

The United States is rebuilding its economy from the ground up. Energy, mining, logistics, and manufacturing are once again at the center of strategic priorities. This time, differently from the past — not by modernizing existing systems, but by building a new generation of industrial sectors designed for artificial intelligence from the very beginning.

This transformation manifests on many levels. Companies are utilizing automated design, advanced simulations, and AI-driven operations. In sectors like nuclear energy, advanced mining, or biological manufacturing — wherever process optimization is required — algorithms surpass the capabilities of traditional operators.

Autonomous drones and sensors can now monitor entire infrastructure complexes: ports, rail networks, power transmission lines, pipelines. Systems that years ago were too vast to manage efficiently are now becoming transparent through continuous monitoring and real-time analysis.

Renaissance of American manufacturing: Factory as a product

The history of American industry has been written during periods of prosperity. However, decades of offshoring and underinvestment have stifled innovation. Today, as machines begin to operate with new energy, we are witnessing a revival of manufacturing on an unprecedented scale.

A change in mindset will be essential. Instead of viewing AI as a tool to optimize existing processes, companies should think like Henry Ford — designing with scale and repeatability in mind from day one. This means:

• Simplifying regulatory procedures and permitting processes through automation
• Integrating AI with humans: strategic roles for people, repetitive and hazardous tasks for machines
• Accelerating design cycles by engineering for manufacturing from the conceptual phase
• More effective coordination of mega-projects

Combining traditional mass production principles with modern AI capabilities opens the door to a revolution: mass production of nuclear reactors, nationwide residential construction, ultra-fast data center expansions.

Observability of the physical world: A new dimension of perception

Over the past decade, software monitoring systems have transformed how we manage digital infrastructure. Software has revealed the world of bytes and servers through logs, metrics, and traces. Now, a similar shift is coming to physical reality.

With billions of connected cameras and sensors deployed in American cities, a new possibility emerges: real-time understanding of infrastructure status. This “physical observability” becomes both technically feasible and strategically necessary.

However, this transformation carries risks. Tools that detect wildfires or prevent construction accidents could equally be used for dystopian mass surveillance scenarios. The winners will be those who build systems that combine transparency with privacy protection — interoperable, natively AI-enabled, without infringing on civil liberties.

Industrial electronics: The bridge between bits and atoms

The revolution will occur not only in factories but inside the machines that power them. Advances in electrification, new materials, and AI converge at a single point: software gains real control over the physical world.

Electric vehicles, drones, data centers, modern factories — all rely on a unified stack: industrial electronics. This encompasses the entire chain — from mined minerals, through components, energy stored in batteries, its distribution, to motion driven by precise motors — all coordinated by software.

This invisible foundation underpins every breakthrough in automation. It determines whether software merely orders transportation or truly controls the vehicle’s direction. The problem? The skills to build this stack are disappearing. From refining critical materials to manufacturing advanced chips — supply chains fragment, and capabilities erode.

If the US wants to lead the next industrial era, it must not only write code — it must produce the physical carriers that implement it. Countries mastering industrial electronics will define the future of both civilian and military technology.

Autonomous laboratories: Science without humans

Multimodal models and robotics are reaching a point where they can close the loop of scientific discovery entirely. Hypotheses → experiment design → execution → analysis → new research directions — all without human intervention.

Teams building such laboratories will be interdisciplinary: AI, robotics, physical sciences, manufacturing, operations — all integrated into unattended research centers. This is an indirect but undeniably powerful transformation of the scientific method.

Data from the business battlefield: The currency of artificial intelligence

In 2025, limitations were computational power and data center construction. By 2026, paradigms will shift — access to data and the ability to structure it will become the main barriers.

Traditional sectors — manufacturing, transportation, logistics — generate vast amounts of unstructured data. Every truck ride, odometer reading, maintenance repair, production operation, assembly, test — all serve as training material. Yet terms like data acquisition or labeling remain foreign to traditional industries.

Companies like Scale or AI research labs pay significant sums for “factory sweat data” — real processes, not just end results. Industrial firms with existing physical infrastructure and staff are in an ideal position. They can collect data almost at no cost and leverage it for their own models or licensing.

New startups will also emerge offering complete stacks: data collection and labeling software, sensor hardware, reinforcement training environments, training pipelines, and even autonomous machines.

Application Layer: From Tasks to Ecosystems

AI not only accelerates — it transforms the business model

Until now, most AI startups focused on task automation. The new phase involves deeper transformation: algorithms not only reduce costs but fundamentally boost customer revenues.

Example? In a profit-sharing model, law firms earn only upon success. Companies using AI to forecast case success chances help lawyers select better cases, serve more clients, and improve win rates. AI doesn’t lower costs — it generates higher profitability.

This logic will extend to other sectors: AI systems will be more deeply integrated with customer incentive mechanisms, creating complex advantages that cannot be copied by traditional software.

ChatGPT as an application ecosystem

Decades of innovation cycles have required three ingredients: new technology, changing consumer behavior, and a new distribution channel. AI has fulfilled two of these, but lacked a native distribution channel for applications.

Everything changed with the release of OpenAI Apps SDK, Apple’s support for mini-apps, and the introduction of group chat in ChatGPT. Developers gained access to a user base of 900 million and can grow through new ecosystems like Wabi.

This final element of the consumer product lifecycle could initiate a new era in consumer technology by 2026 — if developers understand how to leverage it effectively.

Voice assistants: From entry point to full workflows

In the last 18 months, the vision of AI agents handling real interactions has shifted from theory to practice. Thousands of companies — from startups to giants — have deployed voice systems for reservations, data collection, surveys.

These agents not only reduce operational costs but free employees from routine tasks, allowing them to focus on work requiring creativity and judgment.

However, most current solutions only offer “voice as an input” — one or a few interaction types. The future lies in assistants expanding to entire workflows, potentially multimodal, managing the full customer relationship cycle. Agents will be more deeply integrated with business systems, gaining autonomy to handle complex interactions.

Every company should now prioritize: deploying AI products with a focus on voice channels and using them to optimize key operations.

Proactive applications: The end of the prompt era

By 2026, the era of users manually typing commands will end. The next generation of AI applications won’t wait for commands — they will observe actions and proactively suggest next steps.

IDEs will suggest code refactoring before developers ask. CRMs will automatically draft emails after calls. Design tools will generate design variants during work. Chat will no longer be the main interface but an auxiliary support.

AI will become the invisible scaffolding of every workflow, activated by user intent rather than keywords.

Fintech and insurance: Rebuilding instead of patching

Many financial institutions have already integrated AI — document import, voice agents — but this is barely patching old systems. True transformation requires rebuilding entire infrastructure around AI.

By 2026, the risk of falling behind will outweigh the fear of investment. Large financial institutions will start abandoning traditional vendors in favor of native AI solutions.

These new platforms will become data hubs, normalizing and enriching information from traditional systems and external sources. The results?

• Workflows will be drastically simplified. Instead of switching between systems, employees handle hundreds of tasks in one interface, with agents managing the dullest details.
• Existing categories will merge into larger units. KYC data, account opening, transaction monitoring will integrate into a single risk station.
• Winners in new categories will be ten times larger than traditional players.

The future of financial services is not AI layered on old foundations but a new operating system built entirely around intelligence.

Wide penetration: AI beyond Silicon Valley

Until now, the benefits of AI innovation have mainly gone to the top 1% of companies located in or connected to the Bay Area. Understandable — entrepreneurs naturally sell to those they know.

By 2026, the paradigm will shift. Startups will realize that the greatest opportunities lie outside Silicon Valley, in traditional industries — manufacturing, retail, professional services. They will adopt proactive strategies to uncover hidden potential in large, conventional sectors.

System integrators, implementation firms, manufacturers — all can become fields for an AI revolution. The question is: who will be the one to sow the change there?

Multi-agent systems: A new work structure

By 2026, companies will transition from isolated AI tools to multi-agent systems functioning as coordinated digital teams. As agents manage complex, interconnected workflows, organizations will need to redesign structures and information flows between systems.

New roles will emerge: AI workflow designers, agent managers, digital worker coordinators. Beyond traditional record-keeping systems, companies will require coordination layers — new systems to manage agent interactions, assess context, and ensure the reliability of autonomous processes.

Humans will focus on edge cases and the most complex problems. This is not just the next step in automation — it’s a complete reconstruction of the company from the ground up.

Social AI: From “help me” to “know me”

2026 will bring a breakthrough when consumer AI shifts trajectory: instead of merely supporting productivity, it will enhance human relationships and self-awareness.

Algorithms will learn not only from what you tell the chatbot but from every aspect of your existence — photo galleries, private messages, daily habits, stress indicators. Products will start adapting to you, not the other way around.

Systems like “know me” will have better retention than “help me” — they profit from daily interactions, not one-time tasks. The question remains: will users be willing to exchange data for real value?

New foundational models: Companies that were previously impossible

By 2026, new companies will emerge that couldn’t exist without breakthroughs in inference, multimodality, and computer vision. Industries like law or customer service previously used AI to augment existing products. Now, companies are being born whose entire value depends on what was previously impossible.

Advanced inference enables assessment of complex financial claims. Multimodal models extract hidden data from videos — for example, from cameras on production floors. Computer vision automates entire industries whose value was previously limited to desktop software and fragmented workflows.

AI startups selling to AI startups

We are witnessing an unprecedented wave of company formation driven by the AI cycle. Unlike previous eras, existing companies are actively deploying AI.

So how can new startups succeed against the competition? One of the most effective yet underrated tactics is serving new companies from the ground up — greenfield companies that are just developing without the burden of existing systems and contracts.

Stripe, Deel, Mercury, Ramp — all followed this path. By attracting and growing with newly formed companies, you become a large enterprise yourself. Many of today’s giants serve clients that didn’t exist when they were founded.

In 2026, this pattern will repeat across many enterprise software categories. Success only requires: a better product and a complete focus on new, unconnected clients, not existing suppliers.

Conclusions: Software is transforming reality

The world of software has absorbed the digital realm. Now it becomes a physical force of transformation — from factories to laboratories, from cities to power grids. However, this transformation is not without risks. Dystopian scenarios of surveillance, job loss, loss of control over systems — all are possible.

The winners will be those who build AI that is not only powerful but also responsible.