AI Unfiltered

The Layoffs You Don’t See Are Reshaping Tech

Poonam Parihar — Mon, 07 Sep 2026 10:14:46 GMT

source: nextmedia / istock

The tech-layoff count is already alarming. More than 180,000 jobs reportedly cut globally in 2026, with U.S. technology employers announcing 155,126 cuts through August alone. But those figures are almost certainly incomplete. They record the layoffs companies chose, or were required - to announce. They do not capture the far larger, harder-to-track change happening underneath.

This is the era of the silent layoff.

The role that disappears when someone resigns.
The “temporary” hiring freeze that quietly becomes permanent.
The contractor whose agreement ends without replacement.
The small product team absorbed into a central AI platform.
The employee who finds themselves managed out after an overnight shift in performance expectations.
The graduate role that is never posted because the work is now done by a tool, a smaller team, or someone several time zones away.

Uber’s 3,300 job cuts, PayPal’s global restructuring, Cisco’s planned 4,000 reductions, Oracle’s workforce contraction, Meta’s reported experiments with far deeper team reductions, and thousands of cuts at telecom groups such as T-Mobile and Verizon are the visible part of a wider reset.

The important question is no longer whether technology companies are cutting jobs. They are. The question is whether we are measuring the real scale of the change? or merely counting the redundancies that made it into a press release.

The People Building AI Say Work Is About to Change!

The rhetoric from the people leading the AI race is getting harder to ignore.

Elon Musk has argued that AI and robotics could make work “optional” within 10 to 20 years. on 2 July 2026, he wrote that “AI+Robots will be able to do everything, resulting in universal high income. Work will be optional In his vision, a world of abundant machine labour would make employment more like a choice - comparable, and he says, to growing vegetables yourself when you could simply buy them. It is a sweeping prediction, built on the assumption that capable AI and robots become cheap, widespread and economically transformative.

Sam Altman’s position is more cautious, but not reassuring in the short term. He has said that “many current jobs will go away,” while arguing that new jobs will emerge and that people will find “lots of better ones.” He has also said AI will “definitely impact the job market,” even if he expects society to adapt as it has through previous technological shifts.

Then, in May, Altman pushed back against the strongest version of the alarm: he said AI was unlikely to produce a global “jobs apocalypse” and that it had not yet eliminated white-collar jobs at the scale he had previously feared.

All three statements can be true at once.

AI may not trigger a sudden, economy-wide unemployment event. It may still eliminate or redesign large numbers of specific roles. And companies may use the expectation of future AI productivity - not only the productivity they can measure today - to justify cutting headcount now.

That is the uncomfortable gap between Silicon Valley’s promise and workers’ experience. Leaders describe abundance, new categories of work and higher productivity. Employees experience fewer backfills, thinner teams, contractor cuts, rising output expectations and roles quietly disappearing from the organisation chart.

The question is not simply whether AI will replace every job. It is whether companies will share the gains from AI-driven productivity or use them primarily to operate with fewer people.

The Visible Cuts

The obvious examples are large enough that nobody can call them subtle.

Uber announced approximately 3,300 job cuts in early September, around 10% of its workforce. The stated rationale was familiar: reduce management layers, eliminate small “micro-teams,” and focus spending on core operations. In plain English, Uber wants a leaner organisation that moves faster with fewer internal handovers.
PayPal has been pursuing a broad global restructuring reported to involve around 20% of its workforce. The cuts have surfaced in several places: 251 planned job losses at its Silicon Valley headquarters, reports of significant reductions in India, and staff reductions in Israel.
Cisco planned to cut roughly 4,000 jobs while shifting resources toward AI. Oracle was reported to be considering further cuts in August, after its workforce fell by around 21,000 - thats 13% over the fiscal year to May.
Meta reportedly considered cutting some teams by as much as 60% as part of an effort to become “AI-native,” before pausing further layoffs. T-Mobile’s six-month layoff tally approached 4,700, while Verizon was reported to have cut 500 roles and closed stores.

And those are just the companies that made the news.

The central point is not that these businesses are failing. Many remain profitable, are investing heavily, or are being rewarded by markets for AI plans. This is what makes the present wave different from a conventional recession layoff cycle.

Companies are cutting people while increasing investment in data centres, chips, models, automation, and AI products.

The Silent Layoff!

Formal redundancy announcements are only the measurable part of the story.

A company can remove hundreds or thousands of roles without calling it a layoff.

It can freeze backfills. It can let contractors go. It can reduce agency spend. It can fold product teams into central platforms, push support work to lower-cost locations, or decide that one AI-enabled employee can now cover work previously shared by two or three people.

It can also make the environment uncomfortable enough that people leave voluntarily.

This is the silent layoff: workforce reduction by attrition, non-replacement, reorganization, performance management, outsourcing, and automation. It is quieter than a mass email from the CEO, but it can produce the same outcome - fewer people doing more work, and fewer opportunities for those looking to enter or move within the industry.

Public trackers almost certainly undercount this.

Reported tech employees impacted by layoffs, October 2025–September 2026. Source: TrueUp. These figures capture publicly reported layoff events—not the full scale of workforce contraction. They exclude many quiet measures, including roles left unfilled, voluntary attrition, contractor reductions, outsourcing and smaller rolling cuts. October–December are 2025; September is month-to-date.

Layoffs.fyi-style datasets rely on reported company announcements. Challenger counts employer-announced job cuts in the United States. Neither dataset can reliably capture the role that was never advertised, the fixed-term contract that ended, or the employee persuaded that their future is elsewhere.

That matters because technology companies increasingly have an incentive to shrink without drama. Public layoffs attract negative attention, damage employer brands, and can make recruitment harder later. Quiet attrition looks cleaner in an earnings call.

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Is H2 Getting Worse?

The answer is more nuanced than the headlines suggest.

The year’s layoffs were heavily front-loaded. Layoffs.fyi data cited in reporting put around 81,700 tech job cuts in the first quarter alone - the highest quarterly total since early 2023. By 6 August, 125,759 technology workers had been laid off across 264 companies, already above the tracker’s entire 2025 total of 122,606.

Cuts have clearly continued through the second half. The global total rose above 175,000 by late August and above 180,000 by early September. Uber’s 3,300 roles, the continuing PayPal restructuring, Oracle’s further reductions, and ongoing telecom cuts all show that the wave has not stopped.tech.yahoo+1

But the available monthly data does not yet prove a smooth H2 acceleration.

Challenger recorded 6,103 technology cuts in August, its lowest monthly tech total of 2026. That does not mean the danger has passed. It means the pattern is lumpy: fewer announcements one month, then a major restructuring the next.

The better description is not “the tech industry is collapsing.” It is “the tech industry is redesigning its cost base.”

AI Is Part of the Story - But Not All of It!

AI is the most visible explanation, and sometimes it is real.

Executives are openly arguing that automation can reduce routine work, flatten management layers, improve developer productivity, automate support, and make internal operations cheaper.

But “AI” also offers a useful corporate narrative for choices companies already wanted to make:

cut duplicated teams after acquisitions,
reduce expensive middle layers,
close unprofitable product lines,
concentrate budgets on cloud and infrastructure, and
improve margins after years of expansion.

The real dynamic is an AI-capex trade-off. Firms are spending billions on compute, data infrastructure, chips, model development, and AI product integration. If they want to protect margins at the same time, headcount is one of the few major cost lines they can move quickly.

That is why a company can announce layoffs and AI hiring in the same quarter without contradiction.

What to Watch Next?

The next phase may be less visible than the last.

Watch for falling job-posting volumes, missing backfills, consolidation of product and operations teams, cuts to contractors and consultancies, tighter performance processes, and language such as “simplification,” “fewer priorities,” “span of control,” “efficiency,” and “AI-native.”

Those phrases often arrive before the number.

The 2026 data shows that tech layoffs are not a passing social-media narrative.

They are a material restructuring trend. But the more important number may never appear on a tracker: how many roles are quietly removed before they have a name, a job advert, or a severance package.

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Trust Is Becoming Infrastructure

Poonam Parihar — Wed, 26 Aug 2026 00:33:44 GMT

img source foxglove

In July - August 2026, reporting by the Financial Times examined links between former senior NHS figures, Palantir, and the procurement of the NHS Federated Data Platform. The reporting did not establish misconduct. It did, however, renew questions about how public bodies demonstrate that major technology decisions are independent, fairly run, and clearly in the public interest.

Public institutions should not need a scandal before they explain how conflicts were declared, decisions were made, and value was tested.

That distinction matters.

Pub

lic debate about national data infrastructure should not depend on proving misconduct before asking whether the system is sufficiently transparent. Nor should concerns about supplier relationships be dismissed simply because a procurement was formally competitive. In programmes involving sensitive data, large public expenditure, and long-term technical dependence, legitimacy depends on more than procedural compliance.

The issue is therefore broader than one company or one contract. It concerns how public institutions select technology partners, how expertise moves between buyers and suppliers, how citizens can understand those relationships, and how organisations demonstrate that a major data or AI programme is delivering value.

Public-sector AI is entering a more demanding phase.

The question is no longer only whether a system works technically. Buyers, citizens, clinicians, regulators, and delivery partners increasingly expect to understand how a system was selected, who can access its data, how decisions are audited, and what tangible benefit it produces.

This is particularly visible in health, where national data platforms and AI-enabled services sit at the intersection of operational resilience, research capability, privacy, clinical confidence, and public trust. The infrastructure may be digital, but the legitimacy required to sustain it is institutional.

For AI providers, this changes the definition of a strong product. Performance, security, and integration remain essential. They are no longer sufficient on their own.

The Procurement Challenge

Large technology procurements inevitably involve close contact between public institutions, suppliers, advisers, technical specialists, and former public-sector leaders. That proximity is not inherently improper. Complex programmes require expertise, and people move between sectors.

The challenge is that perceived influence can become as damaging as actual influence when decision-making is opaque. If stakeholders cannot see how requirements were set, how conflicts were managed, or why one supplier was selected, they may reasonably question whether competition was fair and whether the public received value for money.

A robust procurement process therefore needs more than a compliant tender. It needs a defensible record of how decisions were made.

That includes:

Clear declarations of interests and transparent recusal processes
Separation between market engagement, solution design, and formal evaluation
Evaluation criteria that can be explained after the award
Strong evidence that a programme delivers measurable outcomes
Technical choices that preserve interoperability and customer control

These are not administrative burdens. They are mechanisms for preserving confidence in the system.

Value Must Be Visible

The scale of public spending on data and AI platforms has sharpened a straightforward question: what outcomes are being purchased?

A credible answer should move beyond generic claims of transformation. It should specify the operational baseline, the intended improvement, the measurement method, and the timeframe. In health, that might include reduced administrative burden, improved bed utilisation, faster research recruitment, more reliable forecasting, reduced elective-care delays, or better-quality data available to frontline teams.

This is where many AI programmes struggle. They can demonstrate technical activity ie models deployed, dashboards built, data sources connected—without demonstrating whether the work changed an important operational outcome.

Each commitment should name the baseline, the target, the measurement owner, the review point, and the consequence if the promised outcome does not materialize. A better model is to define a small number of measurable commitments at the outset:

When outcomes are explicit, technology procurement becomes easier to defend—and easier to improve.

Interoperability Is Strategic

Public institutions should be wary of confusing a coherent platform with a closed ecosystem. A platform can provide useful common services while still allowing specialist tools, regional innovation, and local workflows to connect through well-governed interfaces.

For suppliers, this creates a more constructive competitive position. The goal is not necessarily to replace a national platform. It may be to provide a differentiated capability that works with existing data environments while preserving customer choice.

This is especially relevant for emerging AI systems, edge intelligence, privacy-preserving analytics, and domain-specific decision support. Their value often comes from operating close to the workflow, where latency, context, resilience, and human oversight matter.

The strongest proposition is therefore not “replace everything.” It is: “deliver a measurable capability that interoperates cleanly, leaves the customer in control, and can be evaluated transparently.”

Governance by Design

[ Federated Data Platform (FDP): The specific solution design where individual organizations (such as separate NHS trusts or Integrated Care Boards) maintain sovereign, localized control of their own data instances (”tenants”) rather than dumping everything into a single massive central data pool.

Palantir Foundry: A proprietary enterprise data platform and ontology-powered operating system built by Palantir Technologies to integrate siloed data, manage pipelines, and run AI-driven workflows.]

Trust cannot be added at the end of a deployment. It has to be designed into the product and commercial model.

For founders, this means treating governance as a core product capability:

Traceability: Users should be able to understand the origin of data, the logic of automated outputs, and the history of material decisions.
Human accountability: AI should support accountable professionals rather than obscure responsibility.
Data minimization: Systems should use only the data required for a defined outcome and apply appropriate controls throughout its lifecycle.
Independent evaluation: Claims about performance and value should be open to testing by customers and, where appropriate, third parties.
Interoperability: Customers should retain practical freedom to integrate, extend, and change components over time.
Commercial clarity: The relationship between pilot scope, success criteria, pricing, and scale-up should be understandable before deployment begins.

These principles are not only good governance. They are a route to adoption in markets where reputational risk and institutional confidence are central to the buying decision.

A New Standard for AI

The next generation of public-sector AI will not be judged only by model accuracy or technical sophistication. It will be judged by whether it solves a real problem, produces evidence of value, preserves accountability, and leaves public institutions in control.

In critical public services, trust is not a communications strategy. It is infrastructure.

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AI Is Eating Mobile, But Not Infrastructure Yet

Poonam Parihar — Thu, 18 Jun 2026 08:53:56 GMT

AI has very clearly eaten mobile. In just a year, global time spent in generative AI apps has roughly doubled, from 17.2 billion hours in the first half of 2025 to a projected 36 billion hours in the first half of 2026. Consumer spending has followed the same curve.

AI apps are on track to generate more than $4.2 billion in in‑app purchase revenue in H1 2026, up about 36% on the second half of last year and more than double year‑on‑year.

Across mobile overall, AI has become a core revenue engine: global in‑app purchase revenue reached around $167 billion in 2025, and for the first time non‑game apps overtook games as the main source of spend.

Within that surge, assistants still sit at the centre.

ChatGPT has become the fastest application in history to reach 1 billion monthly active users, hitting that level roughly three years after launch. It remains the largest assistant, but it no longer looks like a monopoly.

Recent data puts ChatGPT’s “True Audience” share at about 46.4% in May 2026, with Google’s Gemini at 27.7% and Anthropic’s Claude around 10.3%. Time spent is heavily concentrated in a small cluster of assistants, but the lead is now shared between several players rather than one. In practice, the assistant layer has become a three‑ or four‑player market, sitting on top of a much broader wave of AI‑branded mobile apps.

The more interesting story however, is what happens when you stop looking at sheer scale and start looking at revenue per user.

At a headline level, ChatGPT still dominates mobile revenue. Its app has generated quarterly revenue in the low billions and has out‑earned the rest of the top 10 AI apps by a factor of five or more. But on a per‑user basis, Claude now comes out ahead.

In the US, Sensor Tower data suggests that,

Claude’s iOS app earns about $2.76 per user, compared with $1.74 for ChatGPT –

roughly one and a half times higher ARPU off a smaller user base. Claude also turns a larger share of its users into paying customers, with around 13% of iOS users subscribed versus about 8% for ChatGPT in recent snapshots.

This is a familiar pattern from other software markets.

The broad, general‑purpose product wins the biggest audience; the more focused product captures the highest‑value users.

Right now, the division of labour looks roughly like this:

ChatGPT is the default assistant for almost everyone, ( almost! ) Gemini is the deeply integrated option for people inside the Google ecosystem, and Claude is emerging as a favourite for high‑intent professional and enterprise use, where depth, safety and workflow fit matter more than novelty.

The fact that Claude can sustain higher ARPU and better conversion on fewer users is a reminder that enterprise‑style usage tends to be structurally more monetizable than mass‑market experimentation.

It also highlights how narrow the first wave of AI monetization has been.

Most of the money so far has gone into the very top of the stack:

chatbots,
writing tools,
companions,
coding assistants, and
image and video generators.

Apps that explicitly brand around “AI”, “LLM” or “machine learning” are on track to make about $12 billion in in‑app purchase revenue in the first half of 2026, with downloads up 177% versus 2023. Almost all of that activity is still about what happens on the screen – typing, reading, chatting, scrolling.

Even the first wave of ads in AI assistants follows the same pattern. OpenAI has started testing ads inside ChatGPT; by May, roughly 17% of daily users were seeing ads, and impressions had grown more than seven‑fold in a few months, mainly from software, retail and media brands.

[ While this piece is a focused, number‑driven zoom‑in on assistants/mobile and the missing infrastructure monetization, I also did an overview / bigger picture if the story is consistent out side of AI mobile, and the assistive interface being a major revenue surface here - ]

AI Needs a Better Founder Narrative.

Very little of this touches infrastructure though.

None of the headline statistics are about AI planning maintenance windows or improving reliability or performance. The AI that has “eaten mobile” is mostly AI that talks, draws and recommends, and not AI that understands physical systems, reasons about risk, or optimizes interventions on the ground.

The business models mirror that focus:

consumer subscriptions,
prosumer upgrades,
usage‑based APIs on text and images, and
now ad inventory.

There is no equally visible set of metrics for AI that quietly reduces downtime, extends asset life or improves safety in the background of critical operations. take it most of the founders reading will fall in to visible category, but this is what worries me, personally.,

For founders and operators, there is both a caution and an opportunity here.

Agents, Meters and Bans!

Poonam Parihar — Tue, 16 Jun 2026 11:21:22 GMT

You might not be using Claude. You might not even know who Anthropic is. But there’s a good chance it’s already inside your stack. As for me, and the substack, I can’t seem to able to escape it.

Claude Code is writing a sizable percentage of production code at companies you rely on. Mythos‑class models have been scanning the operating systems, browsers and cloud infrastructure your products run on. The Linux kernel, Firefox, AWS? all being audited by a capability tier most developers can’t touch directly.

Last week, I argued that Anthropic’s Fable 5 launch was a bait‑and‑switch on privacy: Mythos‑level capability, but only if you accept 30‑day data retention and human review. The trade‑off was brutal but understandable: jailbreak defense for a model that can find zero‑days in the global software supply chain.

Six days later, that whole debate was rendered moot by a single letter from Washington. The U.S. government issued an export‑control directive ordering Anthropic to suspend access to Fable 5 and Mythos 5 for any foreign national, anywhere in the world, including Anthropic’s own non‑U.S. employees. Anthropic responded by killing access to both models globally while it negotiates with regulators.

In other words:

the state just became the highest‑privilege safety layer in the stack.

Agents, meters, and a Friday‑night kill switch

From a builder’s perspective, look at the sequence.

Anthropic spent the spring trying to choke off “cheap agents” by banning third‑party tools and the Agent SDK on standard Claude subscriptions. Developers revolted, and by June 15 the company quietly walked the ban back:

agents stayed, but programmatic usage moved to a separate, hard‑capped credit pool per plan, with no rollover.

The ban died;

the meter survived.

Then came Fable 5, positioned as Mythos‑class for everyone, but with mandatory 30‑day retention and human review wrapped around the most sensitive workloads. Privacy was traded away in exchange for more telemetry and more leverage to intervene when the model did something dangerous.

Finally, on a Friday evening, an export‑control order flipped Fable and Mythos from “metered SaaS products” to “restricted dual‑use technology” overnight.

No clever routing, no pricing workaround - just a blanket ban for anyone with the wrong passport, which in practice became a global shutdown.

This is what frontier AI looks like in 2026:

three overlapping control layers on the same capability.

Pricing and metering – who can afford to automate at scale.
Data retention and policy – who is allowed to use the sharpest tools without giving up logs.
Export controls and nationality – who is allowed to use them at all.

We’ve moved from controlling chips, to controlling training runs, to now controlling models

themselves at the inference layer;

Fable 5 is the first visible proof that export controls have climbed all the way up the stack.

If you’re a founder outside the U.S., you just watched the third layer become real in a single news cycle.

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This isn’t just Anthropic’s problem

From the outside it may look like Anthropic’s only drama. But Anthropic isn’t the only lab in this game. OpenAI, Google, DeepSeek and a growing open‑weight ecosystem are all chasing Mythos‑class cyber capability, but Anthropic is the first to have a frontier model formally yanked into an export‑control regime. That makes Fable 5 less a one‑off and more a prototype for how other frontier models -closed and open- may be treated when they cross certain capability lines.

The timing matters.

Regulators are experimenting in public with where to draw the line between “powerful tool” and “regulated capability.” Once a pattern is set like certain eval scores, certain cyber benchmarks, certain deployment patterns. all the other models that hit those thresholds can be dragged into the same regime almost automatically.

If you’re building on a competitive stack today, you’re not immune—you’re just earlier on the curve.

Why my Anthropic playbook Part 3 is delayed.

I was planning to publish the 3 part of the series on intelligent founder on sunday. for those who; din’t read the previous one focussed on -

Part 1: how Mythos and Project Glasswing turned a series of near‑disasters into a moat- security as a market, not just a cost center.
Part 2: the business map - Glasswing’s coalition, the revenue curve, where Anthropic sits against OpenAI, Google and others, and what a Mythos‑grade security model does to the rest of the market.

The Part 3 was meant to be the “everything else” chapter: the PBC structure, the investor map, the internal fractures, the China problem, the IPO window, and the next eight months. It’s the chapter about governance, incentives and how a $900B‑ish PBC behaves when quarterly earnings collide with a safety mission.

The Fable 5 shutdown doesn’t replace that story, but it changes its center of gravity.

Anthropic isn’t just shipping models. it’s rewiring how software gets built thats from Claude Code in your IDE to Mythos‑class scanners in critical infrastructure.

and It’s doing that as a Public Benefit Corporation heading toward a 2026 IPO at close to a trillion‑dollar valuation, with a charter that legally constrains how it can prioritize profit over mission and a cap table that now includes Amazon, Google, Microsoft and NVIDIA.

Whatever governance structure they lock in this year is the one public markets and regulators will inherit.

And now, for its most advanced systems, Anthropic sits under an explicit national‑security regime that can switch your most important tools off with a fax. You can’t write a serious “Anthropic governance and incentives” chapter without integrating that reality.

So yes, Part 3 is delayed. and not because the outline has changed - it’s still origin, revenue miracle, the real product, the code play, investors, efficiency, internal fractures, China, IPO, permission race. But the frame just shifted from “how Anthropic will govern itself post‑IPO” to “how Anthropic will be governed, full stop - by boards, by regulators, and by whoever gets to sign the export‑control letters. I think i’ll take a minute before i’ look in to this again.

Now why all this matters even if you never call the Claude API