Summary Bullets:

• IBM leads the quantum compute (QC) race with its 156-qubit machine leading, yet the technology is also causing significant cybersecurity concerns.

• While IBM is driving IBM Quantum Safe, investments in other areas are also important for addressing the ‘Known, Unknowns’ with managing emerging security threats.

IBM leads the QC race with its 156-qubit machine leading major rivals such as Google, Fujitsu, and Rigetti.

This latest machine can dimensionally space of 2 to the power of 156 states at the same time, which equates to a 47-digit number. QC is less of a novelty and gradually becoming commonplace. Unlike conventional computing, QC utilize the quantum mechanical principle of superposition, which stipulates that the quantum qubits, ‘qubits’, can be simultaneously in the states 0 and 1 and everything in-between unlike classical computers, which have only two possible binary states of 0 and 1. And through a process of entanglement, QC can see relationships between qubits, impossible on classical computers. This fresh approach brings massive parallelism to computing and promises to accelerate advances research into domains such as science and medicine as well as accelerating AI research.

The Threat to Cyber Defenses
The major discussion however has been the threat to cybersecurity. Namely, the fear that RSA 2048, a 2048-bit encryption key (a top standard for cryptography), for example, could be broken by Cryptographically Relevant Quantum Computers (CRQC) through massively parallel factorization using Shor’s algorithm on a day that is often referred to as “Q-Day”. This would take the best classic computer perhaps a billion years to do and speculatively months or days for QC. Who knows? There is fear that QC can escalate cyberattacks through fraudulent authentication accessing data, systems and applications. It can forge digital signatures, fake records, and compromise blockchain assets. And while nothing is on the market today, cyber adversaries can potentially steal sensitive data now as well as store and decrypt sensitive data when QC is mature.

IBM’s Approach for IBM Quantum Safe
The conversation is recognition that QC is evolving much faster than any previous time. IBM estimates its superconducting QC are between 1,200x to 70,000x cheaper to run, and between 400x to 2,000x faster than ion trap quantum computers. And while IBM is ahead in terms of having the largest computers, it is working with other businesses, government, and regulatory bodies to raise awareness. It is also looking to standardize quantum resistant algorithms. IBM, for example, played a leading and foundational role in three of four proposed NIST standards for post quantum cryptography (PQC). There is also quantum key distribution (QKD) to ensure the secure exchange of information between two or more parties continues in the quantum world. NIST has a 2030 recommendation for new quantum resistant cryptography to be in place. The EU, for example, is coordinating its Quantum roadmap. The switch over to post quantum is likely 2035.

While the impact of securing infrastructure and key distribution for all scenarios – Quantum Safe – will be far reaching, the IBM play is leadership in building the fastest quantum computers, including the processors, hardware, software, and middleware. This is also the experience in supporting industries, especially those regarded as critical national infrastructure (e.g., telecommunications, energy, utilities, banking, and payments), which tend to be highly regulated, rely on legacy systems, and require extra levels of security protection for compliance considerations.

IBM is working with enterprise on mapping cryptographic footprint and assets across systems and applications (e.g., source code, libraries) and network protocols (SSL and TLS). This is to better understand vulnerabilities, dependencies, current posture, before understanding where and how to apply IBM Quantum Safe principles. This is often done to align with compliance laws specific to industry verticals, including critical infrastructure. The company has 160,000 global consultants, has vibrant partner ecosystem working with the likes of Palo Alto Networks, for example, on threat detection and management. The vendor also has a play for quantum readiness.

While leading in overall quantum R&D is important, investments in adjacent many areas such as hybrid cloud, agentic AI, including multi-agent orchestration, will also have big implications for security as much as everything else. In the era of disaggregation, multi-domain experience and optionality will be important for tackling multiple issues, including the challenges with quantum. IBM is supporting its customers goals of being rigid on security, yet flexible on IT strategy and business agility.

Summary Bullets:

• Cloud will have a role in the AI journey, bit no longer the destination. The world will be hybrid, and multi-vendor.

• Agentic AI manifests from this new platform but will be double-edged sword. Autonomy is proportionate to risk. Any solution that goes to production needs governance.

The AI triathlon is underway. A year ago the race was about the size of the GenAI large language model (LLM). Today, it is the number AI agents connecting to internal systems to automate workflows, moving to the overall level of preparedness for the agentic enterprise. The latter seems about giving much higher levels of autonomy to AI agents to set own goals, self-learn and make decisions, possibly manage other agents from other vendors, that impact customers (e.g., approving home loans, dispute resolution, etc.). This, in turn, influences NPS, C-SAT, customer advocacy, compliance, and countless other metrics. It also raises many other legitimate legal, ethical, and regulatory concerns.

Blending Tech with Flexible Architectures

While AI in many of its current forms are nascent, getting things right often starts with placing the right bets. And the IBM vision, as articulated, aligns tightly to the trends on the ground. This is broadly automation, AI, hybrid and multi-cloud environments and data. Not every customer will go the same flight path, but multiple options are key in the era of disaggregation.

In February 2025 IBM acquired HashiCorp. This was a company that foresaw public cloud and on-prem integration challenges decades ago and invested early in dev tools, automation, and saw infrastructure as code. Contextualize to today’s language models, enterprises still will continue to have different needs. While public cloud will likely be the ideal environment for model training, inferencing or fine tuning may better at the edge. Hybrid is the way, and automation is the solution glue. The GlobalData CXO research shows that AI is accelerating edge infrastructure, not cloud. And there are many considerations such as performance, security, compliance, and cost causing the pendulum to swing back.

Watsonx Orchestrate

The acquisition of Red Hat six years ago helped to solidify the ‘open source’ approach into the IBM DNA. This is more relevant for AI now. Openness also translates to middleware and one of the standouts of the event with is the ‘headless architectures’ with Watsonx. The decoupling of UI/UX at the frontend with the backend databases and business logic focuses less on the number of agents, but rather how well autonomous tasks and actions are synchronized in a multi-vendor environment. Traditional vendors have a rich history of integration challenges. An open platform approach working across many of the established application environments with other frameworks is the most viable option. In this context, IBM shared examples of working with a global SaaS provider using Watsonx to support its own global orchestration roll-out; direct selling to the MNC with a large install base of competing solutions, to other scenarios of partners who have BYO agents. IBM likely wants to be seen as having the most open, less so the best technology in a tightly coupled stack.

The Opportunity

Agentic AI’s great potential has a double-edged sword. Autonomy is proportionate to risk. And risk can only be managed with governance. These can include guardrails (e.g., ethics) and process controls (e.g., explainability, monitoring and observability, etc.). Employees will need varying levels of accountability and oversight too. While IBM is a technology company with its own products and infrastructure, it also has its own consulting resources with 160,000 global staff. Most competitors will lean towards the partner-led approach. Whichever path is taken, both options are on the table for IBM. This is important for balancing risk with technology evolution. Still, very few AI peroof of concepts ever make it to production. And great concepts will require the extra consulting muscle, especially through multi-disciplinary teams, to show business value. Claims of internal capability needs to walk that tight rope with vendor agnosticism to keep both camps motivated and the markets confident.