Daily Tech Digest by Kannan Subbiah: Daily Tech Digest

Quote for the day:

"Stop Judging people and start understanding people everyone's got a story" -- @PilotSpeaker

Stop calling it 'The AI bubble': It's actually multiple bubbles, each with a different expiration date

The is actually three distinct layers, each with different economics, defensibility and risk profiles. Understanding these layers is critical, because they won't all pop at once. ... The most vulnerable segment isn't building AI — it's repackaging it. These are the companies that take , add a slick interface and some , then charge $49/month for what amounts to a glorified . Some have achieved rapid initial success, like , which reached approximately $42 million in annual recurring revenue (ARR) in its first year by wrapping GPT models in a user-friendly interface for marketers. But the cracks are already showing. ... Economic researcher points to as an example of the bubble dynamic, noting that the company has made around $1 trillion in AI deals, including a $500 billion data center buildout project, despite being set to generate only $13 billion in revenue. The divergence between investment and plausible earnings "certainly looks bubbly," Bernstein notes. ... But infrastructure has a critical characteristic: It retains value regardless of which specific applications succeed. The fiber optic cables laid during the dot-com bubble weren’t wasted — they enabled YouTube, Netflix and cloud computing. Twenty-five years ago, the original dot-com bubble burst after debt financing built out fiber-optic cables for a future that had not yet arrived, but that future eventually did arrive, and the infrastructure was there waiting.

Modernizing Network Defense: From Firewalls to Microsegmentation

For many years, has been based on the concept of a perimeter defense, likened to a fortified boundary. The functioned as a protective barrier, with a serving as the main point of access control. Individuals and devices within this secured perimeter were considered trustworthy, while those outside were viewed as potential threats. The "perimeter-centric" approach was highly effective when data, applications, and employees were all located within the physical boundaries of corporate headquarters. In the current environment, however, this model is considered not only obsolete but also poses significant risks. ... Microsegmentation significantly mitigates the impact of by transitioning from traditional perimeter-based security to detailed, policy-driven isolation at the level of individual workloads, applications, or containers. By establishing secure enclaves for each asset, it ensures that if a device is compromised, attackers are unable to traverse laterally to other systems. ... Microsegmentation solutions offer detailed insights into application dependencies and inter-server traffic flows, uncovering long-standing technical debt such as unplanned connections, outdated protocols, and potentially risky activities that may not be visible to perimeter-based defenses. ... One significant factor deterring organizations from implementing microsegmentation is the concern regarding increased complexity.

Human-in-the-loop has hit the wall. It’s time for AI to oversee AI

This is not a hypothetical future problem. is already failing in production. When automated systems malfunction — flash crashes in financial markets, runaway digital advertising spend, automated account lockouts or viral content — failure cascades before humans even realize something went wrong. In many cases, humans were “in the loop,” but the loop was too slow, too fragmented or too late. The uncomfortable reality is that human review does not stop machine-speed failures. At best, it explains them after the damage is done. Agentic systems raise the stakes dramatically. Visualizing a multistep agent workflow with tens or hundreds of nodes often results in dense, miles-long action traces that humans cannot realistically interpret. As a result, manually identifying risks, behavior drift or unintended consequences becomes functionally impossible. ... Delegating monitoring tasks to AI does not eliminate human accountability. It redistributes it. This is where trust often breaks down. Critics worry that AI governing AI is like trusting the police to govern themselves. That analogy only holds if oversight is self-referential and opaque. The model that works is layered, with a clear separation of powers. ... Humans shift from reviewing outputs to designing systems. They focus on setting operating standards and policies, defining objectives and constraints, designing escalation paths and failure modes, and owning outcomes when systems fail.

Building leaders in the age of AI

The leaders who end up thriving in the AI era will be those who blend human depth with digital fluency. They will use AI to think with them, not for them. And they will treat this AI moment not as a threat to their leadership but as an opportunity to focus on those elements of their portfolios that only humans can excel at. ... Leaders will need to give teams a set of guardrails (clear values and decision rights) and establish new definitions of quality while fostering a sense of trust and collaboration as new challenges emerge and business conditions evolve. ... Aspiration, judgment, and creativity are “only human” leadership traits—and the characteristics that can provide an irreplaceable competitive edge, especially when amplified using AI. It’s therefore incumbent upon organizations to actively identify and develop the individuals who demonstrate critical intrinsics like resilience, eagerness to learn from mistakes, and the ability to work in teams that will increasingly include both humans and AI agents. ... Organizations must actively cultivate core leadership qualities such as wisdom, empathy, and trust—and they must give the development of these attributes the same attention they do to the development of new IT systems or operating models. That will mean providing time for leaders to do the inner work required to lead others effectively—that is, reflecting, sharing insights with other C-suite leaders, and otherwise considering what success will mean for themselves and the organization.

The Rising Phoenix of Software Engineering

Software is undergoing a tectonic transformation. Modern applications are no longer hand-crafted from scratch. They are assembled from third-party components, APIs, open-source packages, machine-learning models, and now AI-generated snippets. Artificial intelligence, low-code tools, Open-Source Software (OSS), reusable libraries have made the act of writing new code less central to building software than ever before. ... In this new era, the primary challenge is not about builder software faster, cheaper, or more feature-rich. It is how to engineer software safely and predictably in a hostile ecosystem. ... Software engineering, as a discipline, must rise again — not as a metaphor for resilience, but as a mandate for survival. ... The future does not eliminate developers or coders. Assembling, customizing, and scripting third-party components will remain critical. But the accountability layer must shift upward, to professionals trained to reason about system safety, dependencies, and security by design. In other words, software engineers must reemerge as true engineers responsible for understanding not only how their code works, but how and where it runs… and most critically how to secure it. ... To engineer software responsibly, practitioners must model threats, evaluate anti-tamper capabilities, and verify that each dependency meets a baseline of assurance. These tasks were historically reserved for or quality assurance (QA) teams.

The concerning cyber-physical security disconnect

The background of many is in military and law enforcement, which change much slower, but are known for extensive training. The nature of the threats they need to defend against is evolving at a slower pace, and destructive, kinetic threats remain a primary concern. ... The focus of cybersecurity is much more on the insides of an organization. Detection is supposed to catch attackers lurking on compromised devices. Response activities have to consider the entire infrastructure rather than individual hosts. Security measures are spread out across the network, taking a defense-in-depth approach. Physical security is much more outward looking, trying to prevent threats from entering. Detection systems exist within premises, but focus on the outer layers. Response activities are focused on evicting individual threats or denying their access. The majority of security efforts focuses on the perimeter. ... Companies often handle both topics in different teams. Conferences and publications may feature both topics, but often focus on one and rarely address their interdependence. Security assessments like sometimes include a physical component that tends to focus on without involving deep physical security expertise. ... Risks, especially in the form of human threat actors, will always look for the easiest way to materialize. Therefore, they will attack physical assets via their digital components and vice versa, if these flanks are not protected.

Architecting Agility: Decoupled Banking Systems Will Be the Key to Success in 2026

The banking industry is undergoing an evolutionary and market-driven shift. Digital banking systems, once rigid and monolithic, are being reimagined through decoupled architecture, AI-driven intelligence, programmatic technology consumption, and and partnerships. ... Delay is no longer an option — the future of banking is already being built today. To capitalize on these innovations, tech leaders must prioritize digital core banking agility, ensuring integration with new innovations and adapting to evolving market demands. ... Identify suspicious patterns in real time. As illustrated in the figure, a decoupled risk analytics gateway and prompt engine streamlines regulatory reporting and ensures adherence to evolving rules (). Whitney Morgan, vice president at , a fintech provider, states that takes this a notch further by automating regulatory reporting and accelerating product development. ... empowers banks to detect anomalies across large translation datasets with the speed and accuracy that manual processes can’t match. Risk modeling and stress testing will enhance credit risk scoring, market risk simulations, and scenario analysis that drive preemptive and revenue options. ... The banking and financial services innovation race, with challenges in adoption and capturing market advantages, beckons leaders to be nimble and, at the same time, stay focused on the fundamentals. CIOs, CTOs, and other tech leaders can take proactive steps to strike the right balance.

Key Management Testing: The Most Overlooked Pillar of Crypto Security

The majority of security testing in focuses on code correctness or . , however, is mainly considered a procedural issue rather than a technical problem. This is a dangerous false belief. Entropy sources, , and are key to generating. Ineffective randomness, broken device software or a corrupted environment may lead to keys that seem valid but are appallingly weak to attack. The testing mechanisms used to create new wallet addresses for users must be watertight when an exchange generates millions of new addresses. Testing should also be done on key storage. ... The recovery process is one of the most vulnerable areas of key management, yet it is discussed least. Backup and restoration are prone to human error, improperly configured storage, or unsafe transmission. The unfortunate fact about crypto is that recovery mechanisms can be either a saviour or a disaster. Recovery phrases, encrypted backups, and distributed shares need to be repeatedly tested in a real-world, adversarial environment. ... End-to-end lifecycle testing, automatic verification of key states, automated attack simulations and automated recovery protocols that self-heal will be the order of the day. The industry has already become such that key management is no longer a concealed or even supporting part of the security strategies.

Inside the Chip: How Hardware Root of Trust Shifts the Odds Back to Cyber Defenders

Defenders often lack direct control or visibility into the hardware layer where workloads actually execute. This abstraction can obscure low-level threats, allowing attackers to manipulate telemetry, disable software protections, or persist beyond reboots. Crucially, modern attacks are not brute force attempts to break encryption or overwhelm defences. They exploit the assumptions built into how systems start, update, and prove what’s genuine. ... At the centre of this shift is Hardware Root of Trust (HRoT): a security architecture that embeds trust directly into the hardware layer of a device. US National Institute of Standards and Technology (NIST) defines it as “an inherently trusted combination of hardware and firmware that maintains the integrity of information.” In practice, HRoT serves as the anchor for system trust from the moment power is applied. ... For CISOs, HRoT represents an opportunity to strengthen resilience, meet regulatory demands, and finally realise true zero trust. From a resilience standpoint, it changes the balance between prevention and response. By validating integrity from power-on and continuously during operation, it reduces reliance on post-incident investigation and recovery. Compromised devices and systems are stopped early, limiting blast radius and disruption. Regulators are already reinforcing this direction. Frameworks such as the US Department of Defense’s explicitly highlight HRoT as a stronger foundation for assurance.

What AI skills job seekers need to develop in 2026

One of the earliest AI skills involved prompt engineering — being able to get to the necessary AI-generated results by using the right questions. But that baseline skill is being pushed aside by “.” Think of context engineering as prompt engineering on steroids; it involves developing prompts that can deliver consistent and predictive answers. Ideally, “everytime you ask the same question, you always get the same answer,” said , vice president at , a division of . That skill is critical because AI models are changing quickly, and the answers they spout out can differ from day to day. Context engineering is aimed at ensuring consistent outputs despite a rapidly evolving AI ecosystem. ... “Beyond algorithms and coding, the next wave of AI talent must bridge technology, governance and organizational change. The most valuable AI skill in 2026 isn’t coding, it’s building trust,” Seth said. Along those lines, he recommended that job seekers immerse themselves in the technology beyond simply taking a class. “Instead of a course, go to any conference,” Seth said. ... In hiring, genuine AI capability shows up through curiosity and real experience, Blackford said. “Strong candidates can talk honestly about something they tried, what did not work, and what they learned,” he said ... “Things are evolving at such a fast pace that there will be no perfect set of skills,” said Seth. “I would say more than skills, attitudes are more important — that adaptability to change, how quick you are to learn things.”

Daily Tech Digest by Kannan Subbiah

Daily Tech Digest - January 19, 2026