What happens when the growth of computing demand becomes so rapid that even the best systems become unable to match it? This became a pressing question in the year 2025, as the areas of quantum computing, agent-based AI, application-specific semiconductors, among others, converged to form a new paradigm of competitiveness.
The advancements in the quantum computing world this year also went beyond the laboratory. At the Q2B Silicon Valley Conference, IBM’s Jamie Garcia said, “More people are getting access to quantum computers than ever before, and I have a suspicion that they’ll do things with them that we could never even think of.” Hardware maturity scaled to unprecedented heights as well, with Scott Aaronson declaring the end of 2025 a time when “all of the key hardware building blocks seem to be more or less in place, at roughly the required fidelity.”
However, the scale of the technological hurdles has yet to be significantly bridged. Error correction, a problem lingering around the edges of quantum computing development from its inception, saw major advancements with the introduction of Google’s Willow processor, which relies on 105 physical qubits working in error-free conditions to perform complex algorithms exponentially faster than a regular supercomputer. Startup firms Alice & Bob and Riverlane brought innovative designs and decoders to the table, while QuantWare’s plan to employ a superconducting material to produce 10,000 qubits in 2.5 years threatens to upend the quantum computing development landscape.
Although the pace of advancements in quantum hardware accelerates, the software is trailing in providing applications for real-world use cases. However, competitions such as the $5 million XPRIZE for Quantum Applications are motivating the use of biomolecular simulation, clean energy materials simulation, and cryptography applications. The cryptography application still is a crucial testing ground, and a demonstration of Shor’s algorithm on logical qubits by Infleqtion was a significant step, though still a long way off from a cryptosystem breakage solution.
At the same time, the growth of quantum computing has paved the way for the development of agentic AI, which is a revolutionary method of doing work. The concept here is that these “virtual coworkers” enable planners, along with foundation model capabilities, to execute processes involving a series of tasks. However, their adoption is still in the early days, as only 23% of the respondents, as identified by the McKinsey survey, are using or scaling agentic AI in at least one function.
The results, however, are staggering. For example, when integrated into the ERPS and CRM systems, these agents have enabled the automation of the resolution of tickets, the redirection of supplies, and the generation of purchase flows, resulting in a 20% to 30% reduction in cycle times. Similarly, handling insurance claims from start to finish using AI agents has led to a 40% reduction in processing times and, more importantly, has increased net promoter scores by 15%. Even the finance departments have started using AI agents to identify anomalies and forecast cash requirements, resulting in a 60% decrease in risk events.
When it comes to the implementation of Agentic AIs, the need to rebuild architectures to achieve compatibility has emerged. The best practices adopted in these architectures involve access control, kill switches, sandboxing, and constant monitoring. However, according to Ayanna Howard,“I fundamentally believe there should always be a human in the loop somewhere. Always.” This helps prevent the problem of overtrust in AIs when they are applied in areas that require direct interaction.
Driving both the quantum and AI evolution are application-specific semiconductors, which are capable of meeting the exponentially increasing compute requirements of AI model training and inference. Technological advancements in AI-optimized chip design are now optimizing architectures for bandwidth, processing, and thermal performance. AI-optimized chips, which are increasingly being incorporated into agentic workflows, are capable of managing traffic spikes without requiring more human labor, thus reducing low-value work time by up to 40% and processing times by up to 50%.
The energy efficiency of the data center is now an important consideration. Efficient cooling technologies, modular micro-grids, and customized hardware setups for workloads such as AI and quantum computing are now being implemented. Executives face the dilemma of the need for centralization on a grand scale for model development versus deployment that places specialized AI applications right inside devices such as factory robots and self-driving cars.
For tech leaders, it is where these areas meet that competitive advantage will emerge. Quantum computing has the potential to bring revolutionary improvements in optimization and simulation that could turbocharge AI, according to a report by FuturICT 2.0. Agentic AI can coordinate increasingly complex tasks across hybrid compute infrastructures that combine classical and quantum computing.
The application-specific semiconductor represents the hardware that makes these technologies possible on a large scale, and efficient infrastructure represents sustainability under growing demand. But the race is not just between the builds of the fastest quantum computer or the most intelligent AI system it’s between the ability to harness these technologies in a robust, secure, and adaptive manner that yields clear business outcomes. In 2025, the winners are those who can connect the advancements of these technologies with the disciplines of operational readiness and the ability to scale.
