When the world's first commercial central power and distribution system was established in New York in 1882, people viewed electricity merely as a 'lighting technology.' Factory owners replaced steam engines with electric motors but kept the factory structure unchanged. It wasn't until about 30 years later that the true power of electricity was realized, as factories began to install small motors on each machine, transforming entire production lines. The companies that succeeded were not those that simply used electricity well, but those that redesigned everything based on electricity.
Artificial intelligence (AI) is now at a similar threshold. In the four years since the emergence of generative AI, we have focused on 'how to use AI effectively,' akin to the early days of electricity when it was only used for lighting. The year 2026 will mark the end of this phase. AI will transition from being a tool that is turned on when needed to an environment where it operates seamlessly in the background.
By 2027, the questions we ask will change. Until now, discussions in boardrooms have centered on where to apply AI. In 2027, the focus will shift to what humans need to hold onto in a world where AI is already integrated into work. The former question concerns costs, while the latter addresses accountability. We will enter a full-fledged era of AI transformation.
I am a futurist who has researched and taught AI for 42 years. I identified ten key trends to watch in 2027, and when I arranged the first letters of these trends, they unexpectedly formed the phrase 'HUMAN DRIVE.' This alignment is too coincidental to be mere chance, as all ten trends point in the same direction: humans will hold the steering wheel.
The first trend, H, stands for Human-Copilot, the collaboration between humans and AI. In an airplane cockpit, the pilot (human) and co-pilot (AI) share the same instruments, but the pilot bears the responsibility. While AI can gather data and draft documents, it cannot guarantee their accuracy—only humans can do that. In 2027, the most valuable phrase in the workplace will be this assurance.
The second trend, U, represents Ultra-Computing. Quantum computers will not replace traditional computers by 2027. Instead, the approach will involve breaking down complex problems into smaller pieces and assigning each piece to the most suitable computer. There is also an urgent need to address the fact that current encryption methods may eventually be compromised by quantum computers, prompting a major overhaul of encryption in finance and communications.
The third trend, M, is Multimodal AI, which can see and hear. It will be able to detect subtle sounds, vibrations, and temperature changes in factory equipment and alert operators to check bearings. Until now, the benefits of AI have primarily been in office work involving documents, but it will now extend to factories, hospitals, and construction sites.
The fourth trend, A, is Agentic AI, which can work independently. AI that previously answered questions will begin to complete tasks on its own. The key issue will be how much responsibility to delegate. If an answer is incorrect, a human can correct it, but if an execution is wrong, it can lead to financial losses and contractual obligations. The principle will be to delegate tasks that can be reversed. A team leader in a company starts Monday mornings by approving tasks that AI handled over the weekend, with only about twenty out of four hundred tasks requiring his direct attention. The role of managers will shift from directing to designing rules and making exceptions.
The fifth trend, N, is Neuromorphic computing, which mimics the human brain. The human brain operates on less electricity than a single light bulb, while training a large AI model consumes as much electricity as an entire city. Neuromorphic technology aims to bridge this significant gap, presenting an opportunity for countries like ours, which excel in memory semiconductors.
The sixth trend, D, is Open Weight, which refers to the democratization of AI models. Until now, companies have rented AI services, paying monthly fees and sending their data to external servers. Now, they can download open AI models and run them internally. This shift from renting to owning AI is particularly crucial for hospitals, banks, and public institutions that cannot share data externally. A representative from a pharmaceutical company reportedly asked during a board meeting, 'What are we actually purchasing? After paying three years' worth of fees, we have no assets left in the company.'
The seventh trend, R, is Reliable & Verifiable AI. In 2027, the bottleneck for companies will not be performance but proof. If they cannot provide documentation on what the AI has learned, why it made certain decisions, and who is responsible in case of an incident, they will be unable to use it at all. In a bank's review meeting, the discussion stalled not on performance but on these questions. If a customer is denied a loan, how will the bank explain the reason?
The eighth trend, I, is Industry-Specific AI. The era of the largest AI winning is over. The AI that most accurately solves the problems of our factory, hospital, or industry will prevail. An AI trained on the insights of a quality inspector with 27 years of experience in a shipyard will be more accurate than any large model, as it is the only AI that understands the unique materials, welding methods, and failure history of that shipyard.
The ninth trend, V, is Vibe Coding. Instead of learning programming syntax, users will be able to describe their needs in words, and AI will create the program. It will no longer be newsworthy when a seafood wholesaler in Busan creates a price tracking program for his store in just three days. Within companies, marketing teams will create their own dashboards, and HR teams will develop their own recruitment management tools, eliminating the need to submit requests to the IT department and wait months for a response.
The tenth trend, E, is Eco-Intelligence, which emphasizes sustainable AI. Unsustainable intelligence will ultimately not endure. Efforts to reduce the electricity consumed by AI must go hand in hand with using AI to solve environmental issues. Currently, the biggest obstacle to building new data centers is not land or money, but electricity.
So, what are our challenges? The obstacles are not technological but threefold. First is data. Our industrial sites have decades of accumulated data, but it is unorganized and unusable. Raw materials piled in a warehouse do not yet constitute capital. Second is electricity. While AI models can be developed in months, building transmission lines takes years. If AI planning and energy planning are not aligned, this gap will not close. Third is regulation. Companies struggle not with strict regulations but with ambiguous ones. If it is clear what they need to comply with, they will do so and move forward.
South Korea does not need to compete to create the largest AI. Instead, it can find its winning position by deeply integrating AI into global industries such as manufacturing, semiconductors, shipbuilding, batteries, biotechnology, and content. This means transforming the semiconductor industry to consume less electricity and developing a trust industry for inspecting and certifying AI. Success in all these areas will depend not on the size of the investment but on accumulation and regulation.
For individuals, I recommend three questions: What can I guarantee? What will I not delegate to AI? What will I continue to learn? Tools may change every six months, but the ability to define problems, verify results, and recognize risks remains valuable regardless of the tools used.
The year 2027 will not be the year AI begins to replace humans. Instead, it will be a year to test how far humans can expand their capabilities and the potential of their industries through AI. The future will be determined not by the speed of AI but by our choices about which direction to steer that speed. The steering wheel is held by humans.
* This article has been translated by AI.
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