Quote for the day:
"If you care enough for a result, you will most certainly attain it." -- William James
The data center gold rush is warping reality
The real impact isn’t people—it’s power, land, transmission capacity, and
water. When you drop 10 massive facilities into a small grid, demand spikes
don’t just happen inside the fence line. They ripple outward. Utilities must
upgrade substations, reinforce transmission lines, procure new-generation
equipment, and finance these investments. ... Here’s the part we don’t say out
loud often enough: High-tech companies are spending massive amounts of money
on data centers because the market rewards them for doing so. Capital
expenditures have become a kind of corporate signaling mechanism. On earnings
calls, “We’re investing aggressively” has become synonymous with “We’re
winning,” even when the investment is built on forecasts that are, at best,
optimistic and, at worst, indistinguishable from wishful thinking. ... The bet
is straightforward: When demand spikes, prices and utilization rise, and those
who built first make bank. Build the capacity, fill the capacity, charge a
premium for the scarce resource, and ride the next decade of digital
expansion. It’s the same playbook we’ve seen before in other infrastructure
booms, except this time the infrastructure is made of silicon and electrons,
and the pitch is wrapped in the language of transformation. ... Then there’s
the cost reality. AI systems, especially those that deliver meaningful,
production-grade outcomes, often cost five to ten times as much as traditional
systems once you account for compute, data movement, storage, tools, and the
people required to run them responsibly.Chip-processing method could assist cryptography schemes to keep data secure
Just like each person has unique fingerprints, every CMOS chip has a
distinctive “fingerprint” caused by tiny, random manufacturing variations.
Engineers can leverage this unforgeable ID for authentication, to safeguard a
device from attackers trying to steal private data. But these cryptographic
schemes typically require secret information about a chip’s fingerprint to be
stored on a third-party server. This creates security vulnerabilities and
requires additional memory and computation. ... “The biggest advantage of this
security method is that we don’t need to store any information. All the
secrets will always remain safe inside the silicon. This can give a higher
level of security. As long as you have this digital key, you can always unlock
the door,” says Eunseok Lee, an electrical engineering and computer science
(EECS) graduate student and lead author of a paper on this security method.
... A chip’s PUF can be used to provide security just like the human
fingerprint identification system on a laptop or door panel. For
authentication, a server sends a request to the device, which responds with a
secret key based on its unique physical structure. If the key matches an
expected value, the server authenticates the device. But the PUF
authentication data must be registered and stored in a server for access
later, creating a potential security vulnerability. What MCP Can and Cannot Do for Project Managers Today
The most mature MCPs for PM are official connectors from the platforms
themselves. Atlassian’s Rovo MCP Server connects Jira and Confluence,
generally available since late 2025. Wrike has its own MCP server for
real-time work management. Dart exposes task creation, updates, and querying
through MCP. ClickUp does not have an official MCP server, but multiple
community implementations wrap its API for task management, comments, docs,
and time tracking. ... Most PM work is human and stays human. No LLM replaces
the conversation where you talk a frustrated team member through a scope
change, or the negotiation where you push back on an unrealistic deadline from
the sponsor. No LLM runs a planning workshop or navigates the politics of
resource allocation. But woven through all of that is documentation. Every
conversation, every decision, every planning session produces written output.
The charter that captures what was agreed. ... Beyond documentation,
scheduling is where I expected MCP to add the most computational value. This
is where the investigation got interesting. Every PM builds schedules. The
standard method is CPM: define tasks, set dependencies, estimate durations,
calculate the critical path. MS Project does this. Primavera does this. A
spreadsheet with formulas does this. CPM is well understood and universally
used. CPM does exactly what it says: it calculates the critical path given
dependencies and durations. How to Write a Good Spec for AI Agents
Instead of overengineering upfront, begin with a clear goal statement and a
few core requirements. Treat this as a “product brief” and let the agent
generate a more elaborate spec from it. This leverages the AI’s strength in
elaboration while you maintain control of the direction. This works well
unless you already feel you have very specific technical requirements that
must be met from the start. ... Many developers using a strong model do
exactly this. The spec file persists between sessions, anchoring the AI
whenever work resumes on the project. This mitigates the forgetfulness that
can happen when the conversation history gets too long or when you have to
restart an agent. It’s akin to how one would use a product requirements
document (PRD) in a team: a reference that everyone (human or AI) can consult
to stay on track. ... Treat specs as “executable artifacts” tied to version
control and CI/CD. The GitHub Spec Kit uses a four-phase gated workflow that
makes your specification the center of your engineering process. Instead
of writing a spec and setting it aside, the spec drives the implementation,
checklists, and task breakdowns. Your primary role is to steer; the coding
agent does the bulk of the writing. ... Experienced AI engineers have learned
that trying to stuff the entire project into a single prompt or agent message
is a recipe for confusion. Not only do you risk hitting token limits; you also
risk the model losing focus due to the “curse of instructions”—too many
directives causing it to follow none of them well.
The arrival of quantum computing is future, but the threat is current.
Commercial and federal organizations need to protect against quantum computing
decryption now. Various new mathematical approaches have been developed for
PQC, but while they may be theoretically secure, they are not provably secure.
Ultimately, the only provably secure key distribution must be based on physics
rather than math. ... While this basic approach is secure, it is neither
efficient nor cheap. “Quantum key distribution is an expensive solution for
people that have really sensitive information,” continues Bruggeman. “So,
think military primarily, and some government agencies where nuclear weapons
and national security are involved.” Current implementations tend to use
available dark fiber that still has leasing costs. ... “The big advance from
NIST is they are able to provide single photons at a time, as opposed to
sending multiple photons,” continues Bruggeman. Single photons aren’t new, but
in the past, they’ve usually been photons in a stream of photons. “So, they
encode the key information on those strings, and that leads to replication.
And in cryptography, you don’t want to have replication of data.” There is
currently a comfort level in this redundancy, since if one photon in the
stream fails, the next one might succeed. But NIST has separately developed
Superconducting Nanowire Single-Photon Detectors (SNSPDs) which would allow
single photons to be reliably sent and received over longer distances – up to
600 miles.
NIST’s Quantum Breakthrough: Single Photons Produced on a Chip
The arrival of quantum computing is future, but the threat is current.
Commercial and federal organizations need to protect against quantum computing
decryption now. Various new mathematical approaches have been developed for
PQC, but while they may be theoretically secure, they are not provably secure.
Ultimately, the only provably secure key distribution must be based on physics
rather than math. ... While this basic approach is secure, it is neither
efficient nor cheap. “Quantum key distribution is an expensive solution for
people that have really sensitive information,” continues Bruggeman. “So,
think military primarily, and some government agencies where nuclear weapons
and national security are involved.” Current implementations tend to use
available dark fiber that still has leasing costs. ... “The big advance from
NIST is they are able to provide single photons at a time, as opposed to
sending multiple photons,” continues Bruggeman. Single photons aren’t new, but
in the past, they’ve usually been photons in a stream of photons. “So, they
encode the key information on those strings, and that leads to replication.
And in cryptography, you don’t want to have replication of data.” There is
currently a comfort level in this redundancy, since if one photon in the
stream fails, the next one might succeed. But NIST has separately developed
Superconducting Nanowire Single-Photon Detectors (SNSPDs) which would allow
single photons to be reliably sent and received over longer distances – up to
600 miles.Quantum security is turning into a supply chain problem
The core issue is timing. Sensitive supplier and contract data has a long shelf
life, and adversaries have already started collecting encrypted traffic for
future decryption. This is the “harvest now, decrypt later” model, where
encrypted records are stolen and stored until quantum computing becomes capable
of breaking current public-key encryption. That creates a practical security
problem for cybersecurity teams supporting procurement, third-party risk, and
supply chain operations. ... There’s growing pressure to adopt post-quantum
cryptography (PQC), including partner expectations, insurance scrutiny, and
regulatory direction. It argues that PQC adoption is increasingly being driven
through procurement requirements, especially from large enterprises and
public-sector organizations. Vendors without a PQC roadmap may face longer
audits or disqualification during sourcing decisions. ... Beyond cryptographic
threats, the researchers argue that quantum computing may eventually improve
supply chain risk management by addressing complex optimization problems that
overwhelm classical systems. It describes supply chain risk as a “wicked
problem,” where variables shift continuously and disruptions propagate in
unpredictable ways. ... Quantum readiness spans both cybersecurity and supply
chain management. For cybersecurity professionals, the near-term work focuses on
long-term encryption durability across vendor ecosystems, along with
cryptographic migration planning and third-party dependencies.
CEOs aren't seeing any AI productivity gains, yet some tech industry leaders are still convinced AI will destroy white collar work within two years
Most companies are yet to record any AI productivity gains despite widespread
adoption of the technology. That's according to a massive survey by the US
National Bureau of Economic Research (NBER), which asked 6,000 executives from a
range of firms across the US, UK, Germany, and Australia how they use AI. The
study found 70% of companies actively use AI, but the picture is different among
execs themselves. Among top executives – including CFOs and CEOs – a quarter
don't use the technology at all, while two-thirds say they use it for 1.5 hours
a week at most. ... "The most commonly cited uses are ‘text generation using
large language models’ followed by ‘visual content creation’ and ‘data
processing using machine learning’," the survey added. When it comes to
employment savings, 90% of execs said they'd seen no impact from AI over the
last three years, with 89% saying they saw no productivity boost, either. The
report noted that previous studies have found large productivity gains in
specific settings – in particular customer support and writing tasks. ...
Despite the lack of impact to date, business leaders still predict AI will start
to boost productivity and reduce the number of employees needed in the coming
years. Respondents predict a 1.4% productivity boost and 0.8% increase in output
thanks to the technology over the next three years, for example. Yet the NBER
survey also reveals a "sizable gap in expectations", with senior execs saying AI
would cut employment by 0.7% over the next three years — which the report said
would mean 1.75 million fewer jobs. Observability Without Cost Telemetry Is Broken Engineering
Cost isn't an operational afterthought. It's a signal as essential as CPU saturation or memory pressure, yet we've architected it out of the feedback loop engineers actually use. ... Engineers started evaluating architectural choices through a cost lens without needing MBA training. “Should we cache this aggressively?” became answerable with data: cache infrastructure costs $X/month, API calls saved cost $Y/month, net impact is measurable, not theoretical. ... The anti-pattern I see most often is siloed visibility. Finance gets billing dashboards. SREs get operational dashboards. Developers get APM traces. Nobody sees the intersection where cost and performance influence each other. You debug a performance issue — say, slow database queries. The fix is to add an index. Query time drops from 800 ms to 40 ms. Victory. Except the database is now using 30% more storage for that index, and your storage tier bills by the gigabyte-month. If you're on a flat-rate hosting plan, maybe that cost is absorbed. If you're on Aurora or Cosmos DB with per-IOPS pricing, you've just traded latency for dollars. Without cost telemetry, you won't notice until the bill arrives. ... Alerting without cost dimensions misses failure modes. Your error rate is fine. Latency is stable. But egress costs just doubled because a misconfigured service is downloading the same 200 GB dataset on every request instead of caching it.A New Way To Read the “Unreadable” Qubit Could Transform Quantum Technology
“Our work is pioneering because we demonstrate that we can access the
information stored in Majorana qubits using a new technique called quantum
capacitance,” continues the scientist, who explains that this technique “acts as
a global probe sensitive to the overall state of the system.” ... To better
understand this achievement, Aguado explains that topological qubits are “like
safe boxes for quantum information,” only that, instead of storing data in a
specific location, “they distribute it non-locally across a pair of special
states, known as Majorana zero modes.” That unusual structure is what makes them
attractive for quantum computing. “They are inherently robust against local
noise that produces decoherence, since to corrupt the information, a failure
would have to affect the system globally.” In other words, small disturbances
are unlikely to disrupt the stored information. Yet this strength has also
created a major experimental challenge. As Aguado notes, “this same virtue had
become their experimental Achilles’ heel: how do you “read” or “detect” a
property that doesn’t reside at any specific point?.” ... The project
brings together an advanced experimental platform developed primarily at Delft
University of Technology and theoretical work carried out by ICMM-CSIC.
According to the authors, this theoretical input was “crucial for understanding
this highly sophisticated experiment,” highlighting the importance of close
collaboration between theory and experiment in pushing quantum technology
forward.
When Excellent Technology Architecture Fails to Deliver Business Results
Industry research consistently shows that most large-scale transformations fail
to achieve their expected business outcomes, even when the underlying technology
decisions are considered sound. This suggests that the issue is not technical
quality. It is structural. ... The real divergence begins later, in day-to-day
decision-making. Under delivery pressure, teams make choices driven by
deadlines, budget constraints, and individual accountability. Temporary
workarounds are accepted. Deviations are justified as exceptions. Risks are
taken implicitly rather than explicitly assessed. Architecture is often aware of
these decisions, but it is not structurally embedded in the moment where choices
are made. As a result, architecture remains correct, but unused. ... When
architecture cannot explain the economic and operational consequences of a
decision, it loses relevance. Statements such as “this violates architectural
principles” carry little weight if they are not translated into impact on cost
of change, delivery speed, or operational risk. ... What is critical is that
these compromises are rarely tracked, assessed cumulatively, or reintroduced
into management discussions. Architecture may be aware of them, but without a
mechanism to record and govern them, their impact remains invisible until
flexibility is lost and change becomes expensive. Architecture debt, in this
sense, is not a technical failure. It is a governance outcome. When decision
trade-offs remain unmanaged, architecture is blamed for consequences it was
never empowered to influence.
