Quote for the day:
"Prepare, work smarter, Learn from your Mistakes. These are the secret to success!" -- Elizabeth McCormick
What’s wrong (and right) with AI coding agents
“At the scale AI is generating pull requests today, humans simply can’t keep up.
You don’t check the accuracy of Excel with an abacus… and in 2026 we shouldn’t
expect maintainers to manually inspect machine-speed code without machine-speed
assistance,” said Fox. “AI reviews can go deeper than humans in many cases. They
don’t get tired, they can reason across large codebases… and they can spot
patterns at a scale no individual reviewer can hold in their head. If AI is
generating more code, the only viable answer is to use AI to help review and
validate it. You have to fight fire with fire.” ... He reminds us that quantity
does not always equal quality – especially in the AI-driven world we now live
in. He notes that, at least for now, the reality is that AI development tools
and ‘vibe coding’ can generate a lot of code very quickly, but code that’s often
slower and more memory‑hungry than what a skilled developer would write. ...
Although this entire discussion is focused on the now-increasingly-automated
command line, it feels like the real focus should be higher and architecture has
been mentioned already. “We’re entering a world where, with AI, software changes
are propagating faster than governance models can track them. That means AI
tools are, plain and simple, accelerating systemic complexity. When an AI agent
can generate and deploy changes across interconnected enterprise systems,
there’s real danger in the invisible dependencies and downstream effects most
orgs can’t fully see,” said Ido GaverIdentity verification systems are struggling with synthetic fraud
The researchers tied the growth of synthetic identity fraud to the increasing
use of AI tools, which can generate convincing fake documents that pass casual
inspection. “The biggest risk I see in the next 12 to 18 months is the growing
and advancing use of AI. AI is creating fake people, fake voices, and fake
documents. Bad actors are using these capabilities to open accounts, take over
existing accounts, and impersonate real people in places like bank branches,”
Lewis said. ... Financial institutions remain a major target for identity fraud
due to access to credit, account funding, and cash movement. A successful
fraudster can monetize a single fake or synthetic identity for tens of thousands
of dollars before detection, making the sector a frequent target. Online-only
retail banks recorded the highest rate of failed identity verification among the
financial institution categories in Intellicheck’s dataset. The report also
found elevated failure rates across businesses serving underbanked consumers,
including check cashing, payday lending, subprime lending, and lease-to-own
services. ... AI tools are being used to produce synthetic IDs that are
difficult for humans to spot. Lewis said attackers are already using AI and
large language models to generate documents that can bypass basic checks. “AI
and LLM can create fake ID’s that can easily pass the templating test, old
methods don’t work and ID verification service providers can’t rest on their
laurels,” Lewis said. Neoclouds: Meeting demand for AI acceleration
This surge in demand for AI acceleration has seen a surprising benefactor.
According to Tiger Research, cryptocurrency mining firms, seeking to reduce
their exposure to bitcoin’s volatile pricing, are redirecting their graphics
processing unit (GPU) farms toward AI acceleration applications. ... Before
the emergence of neoclouds a few years ago, if an organisation wanted to work
with AI, it had no choice but to go to a hyperscaler like Amazon Web Services
(AWS) or Google. While the hyperscalers offer AI infrastructure as part of
their vast public cloud services portfolio, Roy Illsley, chief analyst at
Omdia, says the hyperscalers tend to be expensive and, as he recalls, a few
years ago, there was very little choice other than Google’s AI offerings. ...
AI infrastructure strategies are becoming inherently hybrid and multicloud by
design – not as a by-product of supplier sprawl, but as a deliberate response
to workload reality. The cloud market is fragmenting along functional lines,
and neoclouds occupy a clear and growing role within that landscape.
“Neoclouds started as GPU as a service. If you needed GPUs, these companies
bought or leased GPUs from Nvidia, and then they would slice them and sell
them off to people in smaller groups and bundles,” says Omdia’s Illsley.
However, over time, neocloud providers have added software stacks and
developed other services to meet the demand of IT buyers who need GPU power
and the software stack required for AI training or AI inferencing.Sam Altman just said what everyone is thinking about AI layoffs
This isn’t the first time industry stakeholders questioned the veracity of AI-related layoffs. A study by Oxford Economics in January this year claimed most layoffs are due to “more traditional drivers” such as overhiring or poor financial performance. ... "While a rising number of firms are pinning job losses on AI, other more traditional drivers of job layoffs are far more commonly cited,” the report said. “What's more, we suspect some firms are trying to dress up layoffs as a good news story rather than bad news, such as past over-hiring." ... “There’s some real displacement by AI of different kinds of jobs,” he said. “We’ll find new kinds of jobs as we do with every tech revolution. I would expect that the real impact of AI doing jobs in the next few years will begin to be palpable.” Altman’s prediction here aligns with research from Gartner and Forrester on the potential impact of AI on the global jobs market. In January, Forrester predicted 10 million jobs could be lost worldwide as enterprise adoption ramps up. ... Despite a string of studies pointing to the contrary, some tech industry figures still believe that AI will eventually render some workers obsolete. In a recent interview with the Financial Times, for example, Microsoft AI CEO Mustafa Suleyman insisted AI will begin replacing “white collar” workers within 18 months. “I think we’re going to have a human-level performance on most if not all professional tasks,” Suleyman toldJailbreaking the matrix: How researchers are bypassing AI guardrails to make them safer
As AI assistants move from novelty to infrastructure, helping write code,
summarizing medical notes and answering customer questions, the biggest question
isn't just what these systems can do, but what happens when they are pushed to
do what they shouldn't. "By showing exactly how these defenses break, we give AI
developers the information they need to build defenses that actually hold up,"
Jha said. "The public release of powerful AI is only sustainable if the safety
measures can withstand real scrutiny, and right now, our work shows that there's
still a gap. We want to help close it." ... Focusing on the internal workings of
the LLM allows more accurate measurements of failures while encouraging the
development of more robust defenses against the failure of safety measures.
According to the researchers, HMNS can help reveal whether specific internal
pathways, if exploited, could cause a breakdown. That information can guide
stronger training, monitoring and defense strategies. ... Understanding the
security shortcomings of LLMs is critical as they become more widespread.
Companies like Meta, Alibaba and others have released powerful AI models that
are available to anyone. While each platform incorporates safety layers meant to
keep it from being misused, the UF team has found that those safety layers can
be systematically bypassed.
Plan vs. planning: Why continuous planning must traverse time
The problem is not the plan’s quality. The problem is that a plan freezes a
moment in time while the organization continues to move through time. Planning,
by contrast, must be a continuous discipline, remaining active as assumptions
decay, signals emerge and constraints shift. ... Planning exists to test those
assumptions continuously, a distinction long recognized in leadership and
management literature that separates planning as an ongoing discipline from
planning as a static artifact. Plans are optimized for agreement and commitment.
Planning is optimized for learning, decision-making and managing consequences in
the face of uncertainty. In practice, this means consequences must be visible at
the moment of decision, not discovered months later through execution. ... Many
enterprises optimize for compliance, predictability and approval at the expense
of feedback and adaptation. Learning is pushed downstream, arriving only after
outcomes are locked in and costs incurred. Systems theorist Russell Ackoff
described this dynamic clearly: “Most organizations are not short of
information. They are short of the ability to learn from it.” Continuous
planning restores learning by design, not as postmortem analysis, but as
pre-decision feedback. Feedback that arrives before commitment changes behavior.
Feedback that arrives after execution becomes an explanation. In volatile
environments, that timing difference is decisive, which is why scenario planning
and structured foresight have re-emerged as critical executive tools.
The rise of AI factories: Powering an era of pervasive intelligence
In India alone, Google is building a gigawatt-scale AI hub in Visakhapatnam.
Microsoft is expanding its cloud and AI footprint in Pune and Chennai and
creating a new “India South Central” region in Hyderabad. In partnership with
NVIDIA, Reliance Jio is developing a major AI data center in Jamnagar for
nationwide GPU-as-a-service offerings. TCS is planning a 1-gigawatt AI data
center, likely in Gujarat or Maharashtra, to support startups, hyperscalers, and
government institutions. And as part of its Stargate project, OpenAI is actively
scouting locations in India for what could become one of the largest AI data
centers in all of Asia. ... The growth of AI represents a fundamental
transformation in how the world builds and operates computing infrastructure.
While traditional data centers are designed for general-purpose workloads, AI
superclusters are purpose-built facilities that function as industrial-scale
intelligence production systems. And their output is defined by new metrics —
most notably tokens per watt and tokens per dollar — that quantify the
efficiency and productivity of intelligence at scale. ... To deliver the
performance at scale that AI requires, silicon designers are increasingly
turning to multi-die designs, including 3D integrated circuits (3DIC) and
chiplet-based architectures. While these chip designs offer gains that
traditional monolithic SoCs cannot achieve cost-effectively, they also introduce
significant complexity to the design process.
Cognizant CAIO Babak Hodjat explains how Agentic AI will transform enterprises
One of the things that agentic systems do is they allow for a diversity of data sources because you can actually have an agent responsible for a data source talking to other agents responsible for other data sources. Your interface into this system could be a consolidation of information and decisions that come from these disparate sources. It is the first time that we can actually have a mapping between intent and disparate sources of data and applications. I think that will work well. That kind of design can work well in a country like India with such diversity of data. ... Population-based approaches like genetic algorithms are very good at non-linear optimisation, especially if you are looking at multiple outcomes at the same time. Pretty much every problem that we look at is multi-objective. Every problem that we look at has improved revenue but reduced costs. You look at curing disease but reduce impact on the economy. It is always more than one outcome that we are looking at. In problems like optimisation of power grids or managing urban traffic systems, these are very well-suited algorithms. ... There are two opposing forces when it comes to AI. Scaling laws mean that building bigger is more powerful, and building bigger typically means using more energy. Many companies are looking at green sources for that additional consumption. On the other hand, companies are optimising models to be smaller and less energy-hungry. For multi-agent systems, smaller models can be more cost-effective and greener.Inference Becomes the Next AI Chip Battleground
Inference has fundamentally different economics and performance requirements
than training, said Karl Freund, founder and principal analyst at Cambrian AI
Research. Training AI models is a cost center, while inference is a “profit
center” that directly generates revenue. Freund and Kimball noted that while
GPUs deliver excellent performance, they often carry architectural features
optimized for training that don’t always translate to lower latency or higher
efficiency in pure inference use cases. Purpose-built inference chips – ASICs
and other accelerators – can deliver faster responses, improved energy
efficiency, and lower total cost of ownership. ... "As inference workloads
exceed the total amount of training workloads in terms of token output, there
will be a greater need for diversity because alternative XPU architectures can
achieve better efficiency on some specific inferencing tasks,” said Brendan
Burke, research director of semiconductors, supply chain, and emerging tech
at Futurum Group. ... Inference opportunities span data centers and the edge,
and requirements vary widely by workload and deployment. “The inference you do
in your autonomous vehicle is far different than the inferencing you do when
you’re an online customer service bot,” Kimball said. ... Analysts expect Nvidia
to maintain dominance in both training and inference, but diverse requirements
create space for specialized solutions to capture share.
Why the CFO's Playbook Belongs on Every CIO's Desk
Recent research from Gartner on how CFOs are allocating budgets gives CIOs
insight into what priorities look like across departments, and where technology
and AI can help move the needle. The research firm's CFO Report: Q1 2026 finds
that while budgets are shifting and AI ambitions are high, enterprise-wide AI
success remains an aspiration rather than a reality. ... AI is also changing the
conversation on ROI for both finance and technology leaders. "There's a lot more
to evaluating the success of some of this investment in technology than simply
just ROI, and AI is definitely helping change that," Abbasi said. "AI isn't your
traditional asset." Unlike standard hardware expenditures, AI investments don't
have predictable depreciation curves, and the ways in which returns on AI
investment may show up across the business can vary. They may manifest in time
to market, customer satisfaction or competitive positioning, not just in cost
savings, Abbasi said. CIOs should be sure to articulate how AI will generate
strategic returns rather than focus on pitching it as a capital project. "It
changes the way you measure the effectiveness of AI, as well as how you measure
your business more holistically," he said. "It's not like a traditional asset
because you don't necessarily know what the outcomes are going to be for some of
these AI projects."
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