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Summary

• President Trump reminds Iran of deal timeline, threatens "all hell will reign down" if time runs out.

• Israel launched heavy strikes on Tehran, targeting Iranian air-defense and ballistic-missile sites, while a projectile also hit the perimeter of Iran's Bushehr nuclear plant

• The U.S. military continued search operations for an American airman who ejected after an F-15E fighter jet was shot down over Iran

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President Trump Reminds Iran of Timeline, Threatens "All Hell Will Reign Down"

As the long weekend continues, President Trump has issued a statement on his social media feed, reminding Iranian negotiators of his timeline for a deal:

Remember when I gave Iran ten days to MAKE A DEAL or OPEN UP THE HORMUZ STRAIT.

And then the threat:

Time is running out - 48 hours before all Hell will reign down on them.

Glory be to GOD! President DONALD J. TRUMP

The odds of 'boots on the ground' have soared to 83% by the end of the month:

It seems the stock market's hope (diverging from oil's surge) was misplaced...for now.

Search Operations Continue for Missing Airmen Continues

With U.S. and Israeli air-delivered munitions still striking targets across Iran, and Tehran retaliating by hitting high-value sites around the Gulf area, while continuing to disrupt the Strait of Hormuz, the conflict is now entering its sixth week with no credible signs of near-term de-escalation. Add in President Trump's speech last week, which warned that intense targeting could continue for a few more weeks, and it's a very fair assessment that the conflict will carry into next week, with momentum and escalation to the upside.

On Saturday, the U.S. military continued search operations for an American airman who ejected after an F-15E fighter jet was shot down over Iran, marking the first downed U.S. aircraft in the conflict. One crew member was rescued, but the second remained missing, with Iranian forces also racing to find the missing pilot.

The downed F-15 jet came shortly after a U.S. Black Hawk was hit by ground fire, and an A-10 Thunderbolt II reportedly crashed Friday near the Hormuz chokepoint. Friday was not a great day for U.S. aircraft as the conflict intensified.

C-17 Globemaster IIIs are on the move. 

???????? With one of the F-15 pilots still missing in Iran, the U.S conducted the largest visible airlift of the war so far.

C-17s carrying tons of cargo to the Middle East, along with KC-135 refueling tankers, essential for extended duration airstrikes.pic.twitter.com/rHPKx04dYb https://t.co/v9atjkSCGN

— Mario Nawfal (@MarioNawfal) April 4, 2026

Strikes Continue on Both Sides

In a rapidly escalating phase of the US-Israel war on Iran (now around day 36+ since late February strikes that targeted Iranian leadership and infrastructure), Tehran has intensified its retaliation while the US and Israel press air campaigns. Iranian missiles struck central Israel on Saturday, triggering widespread sirens and causing visible damage, including to residential areas and an industrial zone near Beersheba. Reports mentioned cluster bomb effects and shrapnel injuries, though Israeli defenses intercepted many projectiles.

At the same time, Israel launched heavy strikes on Tehran, targeting Iranian air-defense and ballistic-missile sites, while a projectile also hit the perimeter of Iran's Bushehr nuclear plant, according to the semiofficial Iranian Tasnim news agency. The International Atomic Energy Agency said Iran had notified them about the incident.

?????????? The IDF just posted footage of a ballistic missile launcher being destroyed in Tabriz, Iran's 4th largest city, deep in the northwest.

That's the IDF reaching across the entire country to take out the thing shooting at Tel Aviv.

They found it, filmed it, and posted it.… https://t.co/YOKobSXGY1 pic.twitter.com/hMhX5uhTLc

— Mario Nawfal (@MarioNawfal) April 4, 2026

Let's not forget President Trump's speech on Wednesday, in which he suggested the conflict could continue for weeks and insisted the missing airman would not alter efforts to negotiate an end to the conflict.

Iran launched a fresh missile barrage at central Israel, causing fires, damage in areas like Negev, Rosh Haayin, Bnei Brak, and reports of cluster munitions; minor injuries reported, with one man hurt in Bnei Brak.

An apparent Iranian drone damaged the Dubai headquarters of the U.S. tech giant Oracle on Saturday after Iranian forces threatened dozens of US firms. Iran has been targeting Gulf area data centers, and reports of a water desalination plant on Friday made headlines.

Latest headlines

(courtesy of Bloomberg):

US Military Losses

• Iran shot down a US F-15E Strike Eagle fighter jet on Friday, with one crew member still missing and search-and-rescue operations ongoing [APW] [BN] [APW]

• A second US combat plane reportedly crashed in the Persian Gulf the same day [BN] [APW]

• Iran has called on the public to find the 'enemy pilot' and is promising a reward [APW]

• Iran says it used a new air defence system to target the US fighter jet [NS1]

Iranian Attacks

• Iran's Revolutionary Guards targeted an Israel-linked ship, the MSC Ishyka, with a drone attack in the Strait of Hormuz, setting it on fire [NS8] [NS1]

• Iranian cluster missiles hit central Israel with at least four impact sites and reports of vehicles on fire [NS8]

• Missile fragments impacted near Tel Aviv after an Iranian missile barrage, with no casualties reported [JPT]

US-Israeli Strikes

• US-Israeli strikes allegedly hit multiple areas in Iran on Saturday, targeting government-affiliated and industrial facilities including the Bushehr nuclear site [NS8]

• More than 30 universities across Iran have been directly targeted by US-Israeli strikes since the war began in late February [NS8]

• The US destroyed the B1 Bridge in Karaj on April 2 in two separate bombings, targeting what Iran describes as a civilian engineering project [NS8]

Diplomatic Efforts

• Pakistan, Turkey, and Egypt are working to bring the US and Iran back to the negotiating table with a compromise framework focusing on ending hostilities and reopening the Strait of Hormuz [NS8]

Global Impact

• The war has entered its sixth week with energy prices rising and little sign that Iran will back down or reopen the vital Strait of Hormuz [BN]

• Senegal has banned all but essential foreign trips for government ministers due to cost-saving measures triggered by the energy crisis linked to the Iran war [APW]

• Chinese firms with ties to the military are marketing detailed intelligence on US force movements as the war continues [WPT]

In commodity markets, the ongoing energy shock, with crude and LNG facilities across the Gulf area disrupted and the Hormuz chokepoint still clogged, prompted Goldman analyst Yulia Zhestkova Grigsby to ask on Friday evening: "Are We Running Out Of Oil?"

"As the last tankers that crossed the Strait of Hormuz before the war are reaching their destination, concerns about potential oil shortages are rising," Grigsby told clients.

She said, "We analyze country-product-specific oil markets, identify pockets of potential extreme tightness, and discuss the potential evolution of near-term shortages if the Strait of Hormuz remains effectively closed for longer."

"Our three-way analysis highlights already critically low supplies of petrochemical feedstocks -- naphtha and LPG -- in Asia, with cross-product scarcity in multiple Asian countries in April," the analyst added.

To end the week, Brent futures and WTI futures both closed Friday in triple-digit territory as traders are becoming increasingly alarmed not just of the crude oil and LNG shortage spreading worldwide but also of petrochemical supply disruptions that are inbound that could affect plastics production, the core material that is bedrock for the modern economy.

Let's remind readers of how the energy shock dominoes fall.  

JPMorgan analysts mapped out how the energy shockwave from the Iran war spreads across the world, hitting Asia first, then Africa and Europe, before settling on the US - primarily California.

We'll provide updates throughout the day as the situation in the Middle East is ongoing.

Authored by Alex Imas and Soumitra Shukla via Ghosts of Electricity,

One of the most widely cited findings in AI policy comes from a 2023 paper by Eloundou, Manning, Mishkin, and Rock titled “GPTs are GPTs.” The title is a nice double meaning: the paper studies how general-purpose technologies (GPTs) powered by large language models (also GPTs) may reshape the labor market. The headline finding is that around 80% of U.S. workers could have at least 10% of their tasks affected by LLMs, and roughly 19% may see half or more of their tasks impacted. Broadly, these exposure measures try to capture how “exposed” the occupation is to AI as a function of whether AI can augment the tasks involved in the job: direct exposure is defined as “whether access to an LLM or LLM-powered system would reduce the time required for a human to perform a specific DWA or complete a task by at least 50%.” The authors are crystal clear on this in the paper: exposure corresponds to the capacity of AI to be involved in the job, not the extent to which the job can be automated away.

But the word “exposure” turned out to bring on all sorts of anxieties about exactly that—displacement. And perhaps for this reason, these AI exposure measures have routinely gone viral on social media over the last couple of months.

A recent example is by Andrej Karpathy, one of the co-founders of OpenAI and a leader in how to think about AI more generally (e.g., he coined both the terms “jagged intelligence” and “vibe coding”). His dashboard, which he described as a “vibe-coded” weekend project, was a ranking of how exposed major occupations are to AI-driven automation. It quickly went viral on X, as it fed all of the already-existing narratives about rapid job loss due to AI.

After seeing the dashboard sensationalized and spread like wildfire, Karpathy clarified that his “exposure” scorecard was based on a quick, LLM-generated measure of how digital a job is, and was never meant to be a serious forecast of which occupations will shrink or disappear. While his own project website made the same caveat, it was largely ignored on X. To butcher the well-known phrase: “A vibe coded weekend project will travel twice around the world before the caveat has time to put its pants on.”

What this recent episode illustrates, however, is that such exposure measures have caught the public eye but are routinely misread (with some proposing a moratorium on the term “exposure” altogether). When people hear that a job is “80% exposed” to AI, they picture 80% of that job disappearing. The actual economics of AI exposure and job loss are pretty far from that characterization.

What is a “job”?

A job is a set of tasks; a person typically gets paid based on how well they complete all of the tasks associated with the job. So let’s say you’re a project manager. Your job involves a bunch of tasks like generating ideas, outlining those ideas succinctly and getting feedback from team members, putting together presentations, and a bunch of rote work (e.g., approving time sheets, fielding logistics). As the AI models become better, you’ve realized that you can automate many of these things: AI can do a lot of the rote work for you, and can even help you put together presentations. According to the exposure measure, your job is now “exposed” to AI. What happens to your job and what happens to your wage? Well, if automating some of the tasks frees up time to generate better ideas, your overall productivity goes up—you become even more valuable to the firm. Humans are still employed and if anything the wages go up.

On the other hand, if AI automates all of the tasks—let’s say your job only involves two tasks and they both get automated—then yes, human labor will get displaced. Importantly, the fewer the number of tasks (what we call the dimensionality of a job), the greater the incentive of the company to automate it in the first place. This is the part much of the analysis on automation misses: adopting AI into an existing organization is costly, so the firm will be more likely to invest if it can automate the job, not just the task. “Exposure” and risk of automation is not just a function of model capabilities, it also depends on firm incentives. And this is not a hypothetical: we now have plenty of evidence that such incentives matter greatly for what gets automated and when (e.g., firms are much more likely to automate when the cost of human labor increases).

Lastly, even if AI makes people more productive and yields higher wages, there can still be massive layoffs in that sector if consumers do not “absorb” the increased productivity: if productivity-driven price drops do not increase demand for the product, then fewer workers will be needed in that sector.

More generally, a task being exposed to AI—even if that exposure corresponds to full automation of that task—can potentially lead to higher wages and more hiring for that occupation. Or it can lead to layoffs and even full displacement. Whether exposure leads to better or worse labor market outcomes for workers depends on two key variables: the elasticity of consumer demand in that sector (how much more of the product people buy as prices decrease), and the dimensionality of the job (how many tasks are involved in that job). As we hope to convince you by the end of the piece, we should be a lot more worried about jobs like trucking and warehousing than we currently are.

The standard approach to automation

Let us start with the “standard” approach to thinking about automation. First, we decompose jobs into tasks using a taxonomy like O*NET, then evaluate how many of those tasks can be automated or augmented by AI. The total impact on the job is a weighted average of how much each task was improved, which means you can build an “exposure index”—typically defined as what share of a job’s tasks can AI do?—and that index maps linearly into how much the job is affected (see, e.g., Michael Webb’s already-classic paper). This approach has been enormously useful for mapping the landscape of AI’s potential reach. But it contains an assumption that is almost certainly wrong for most real-world jobs: it assumes tasks are separable. That is, automating task A has no effect on the productivity of task B, and the overall impact is just the sum of parts.

Consider the jobs that you know. There are many out there where the output consists of doing many different things right, not just some of them. You can’t have a cook who follows most of the steps of a recipe, a drummer who is mostly on the beat, a programmer whose code only partially works (or, for that matter, a professor who only does the research half of the job…though some have tested this requirement). These are jobs where each task needs to be completed successfully for the output to be acceptable.

Put differently, the tasks are not separable; they are complements, i.e., doing one task right or wrong affects how well you can do others in the job in order to complete it. That tasks within a job are complements rather than substitutes seems quite plausible for most real-world production. And this has a wide range of important implications for how AI will actually affect jobs.

The O-ring model of jobs

The idea that complementary tasks create nonlinear productivity goes back to Michael Kremer’s classic 1993 paper, “The O-Ring Theory of Economic Development”. The name comes from the tragic Challenger disaster: a single faulty O-ring caused the catastrophic failure of the entire system. Kremer’s insight was that if production requires many steps, and each step needs to be done well for the final product to have value, then productivity becomes a multiplicative rather than a linear function of skill. A worker who makes slightly fewer errors per task will be dramatically more productive overall, because those small quality gains compound across every step.

This task-based model of jobs has gained fresh relevance with a recent paper by Joshua Gans and Avi Goldfarb, “O-Ring Automation,” which applies Kremer’s framework directly to AI-driven automation. While their model might appear simple at first glance, its implications are far-reaching and profound. At least one of us (Alex) has been obsessed with this paper for months (see here, here, and here).

Gans and Goldfarb build a model of a firm where each worker’s job is composed of n tasks. The job’s output is multiplicative in the quality of each task—this is the O-ring production function:

A worker has a time endowment h and allocates it across the n tasks. If task s is performed manually, the worker spends h_s hours on it and generates quality:

where a is labor productivity, assumed constant across tasks (a simplifying assumption). The worker's time constraint is:

The firm can also choose to automate any task by renting a piece of capital that delivers a fixed quality ? at cost r per task. This is the key part to pay attention to: whether firms invest in automating a task depends on the trade-offs embedded in this problem. Once a task is automated, the worker no longer needs to spend any time on it.

So far the setup is quite simple. The interesting part is what the multiplicative structure of the production function implies once automation enters the picture.

How can automation raise wages?

Now suppose a firm chooses to automate k out of n tasks. What happens to the worker, and how does that affect the wage?

Before automation, the worker allocates time evenly across all n tasks, which is optimal given the symmetric structure. Each manual task therefore receives h/n hours and has quality a · h/n. Total output is:

After k tasks are automated at quality ?, the worker now has all h hours to allocate across only n - k remaining manual tasks. Each manual task now gets h/(n-k) hours, producing quality a · h/(n-k). Total output becomes:

So output rises after partial automation if and only if:

This is an important condition which states that if the automated task quality ? is at least as good as the worker’s original pre-automation manual quality on those tasks, then the output increases for sure. Output does not automatically rise just because some tasks are automated; it rises when the quality of automation is high enough.

But here is the key insight: because automation also frees the worker to concentrate more time on the remaining tasks, output can increase even if the automated tasks are performed at slightly lower quality than the worker originally achieved before automation. Automation lets the worker concentrate on fewer tasks, raising the quality of each one. This is the “focus effect.” Because of the functional form of the production function, higher quality on the remaining manual tasks doesn’t just add to output—it multiplies through the production function. The worker becomes more productive precisely because they’re doing fewer things.

When the automation quality is sufficiently high relative to what the worker was producing manually on those tasks, the worker’s marginal product rises—and so (typically) does their wage. Partial automation, in the O-ring world, is often a complement to human labor rather than a substitute for it, which increases the worker’s wage.

But this is not necessarily good news for labor

Higher worker productivity is good for wages, but does it lead to more jobs or fewer? This depends on consumer demand. Each worker makes one calculator a day and the firm has 10 workers. All calculators are sold at the prevailing price. Now imagine each worker becomes much more productive so that each worker can make 10 calculators. The price of each calculator falls (costs fall), but consumers still demand roughly the same number of calculators. This is the case of inelastic demand—one that does not respond much to prices. Now the firm will fire 9 of the workers. But what if consumers buy way more calculators at lower prices, i.e., demand is very elastic. Then the firm will actually end up hiring more workers to meet the new demand, despite the fact that they’re more productive.

More generally, if demand is elastic (elasticity > 1), then a price decrease leads to a more-than-proportional increase in quantity demanded. Output expands a lot. The firm needs more workers to produce this higher output, even though each worker is now more productive. Net effect: more hiring.

If demand is inelastic (elasticity < 1), a price decrease leads to a less-than-proportional increase in quantity demanded. Output does not expand much and the firm can produce the same (or slightly more) output with fewer workers since each one is more productive. Net effect: displacement.

This is closely related to a popular idea commonly referred to as Jevons’ paradox: when a resource becomes more efficient to use, total consumption of that resource often increases rather than decreases. When the steam engine made coal more efficient, coal consumption skyrocketed because so many new applications became economically viable. The same logic applies to labor: if AI makes a worker dramatically more productive, and demand for that product is elastic, one may end up with more workers in that occupation, not fewer.

Why job dimensionality matters: The case of firm incentives

The relationship between tasks and the elasticity of consumer demand is an important dimension for predicting AI-driven displacement, but one variable that is often overlooked is the number of tasks in the job itself, i.e., its dimensionality. A job’s dimensionality matters for two reasons.

First, conditional on a task being automated, a low-dimensional job is more likely to be fully displaced. If a job has 20 tasks and one gets automated, a human worker is still required to do the other 19 tasks. But if a job has one task and one task gets automated, that job is gone. Second—and this dimension is perhaps overlooked the most—organizations have a stronger incentive to automate tasks the fewer non-automated tasks are left in the job. Imagine that automating a task requires a $10 million dollar investment (buying the software, onboarding, connecting it to the rest of the system, etc.). In one case, this task is the only non-automated task left in a job; in the other case, if this task is automated, there are 19 other non-automated tasks left. The firm has a much higher incentive to automate the task in the first case than the second because it can then replace the worker and reap the cost savings involved.1

Because of this, firms have a stronger incentive to invest in technology to automate low dimensional jobs. In a low-dimensional job, automating all or most of the core tasks can eliminate the position and the wage bill altogether. That makes the return to automation much larger. In other words, not all “unexposed” tasks matter equally: in some jobs the remaining tasks still keep the existing worker at the firm; in others they do not.

This gives a clear prediction: even if a job is not currently “exposed” to AI, in the sense that AI is not being used for the tasks involved, if it is low dimensional and the technology is getting close to automating the tasks, it should be considered at risk. Firms will work harder and invest more to automate the task(s) involved than in the case where jobs have many non-automated tasks.

Trucking and warehousing, the overlooked canaries in the coal mine

This is why we think people should be more worried about jobs like trucking and warehousing.

Roughly 3 million Americans drive trucks for a living. Many are in their 50s, have been driving for decades, and live in communities where trucking is an economic backbone. Trucking is one of the best jobs one can get without a college degree. The actual work of a long-haul truck driver is dominated by a few core functions: moving the truck safely from point A to point B. The logistics, loading/unloading, etc. are all done by others. If autonomous driving becomes reliable on long-haul routes, the job of a truck driver is not just being augmented; it is fundamentally threatened and may even be displaced entirely. And that possibility is no longer theoretical. Companies such as Aurora Innovation and Kodiak Robotics are already running large-scale autonomous trucking pilots and commercial deployments on constrained routes. Warehousing tells a similar story. Warehousing employs millions of U.S. workers, and many warehouse jobs—picking, packing, sorting, pallet movement—are relatively narrow and increasingly automatable. Abroad, firms are already operating highly automated “dark warehouses” that run around the clock with minimal human labor. These warehouses look nothing like what we see today: they are designed from the ground up to be run by machines.

Now compare that to a knowledge worker, say, a management consultant. The job combines research, data analysis, client communication, presentation design, strategic reasoning, team coordination, and relationship management. That’s at least seven or eight distinct complementary tasks. Claude or Codex might automate the first pass on the data analysis and slide deck creation, but the consultant is still needed for everything. In O-ring terms, automating some tasks can make the remaining ones more valuable by allowing the worker to allocate more time to them—the consultant can spend more time talking to the client and making them comfortable with the implementation, getting buy-in from the various units, etc. As a consequence, wages may rise, and employment may rise too if better output and lower prices expand client demand.

You can see the same logic in many high-stakes professions such as medicine and academia. There are now over 870 FDA-approved radiology AI tools, and 66% of doctors use at least one AI tool, mainly for note dictation and diagnostic support. But these tools are augmenting radiologists and physicians, not replacing them. AI typically handles the routine pattern recognition aspect of the job, freeing doctors to focus on complex cases, patient communication, and clinical judgment. Likewise, academics have been debating whether advances in AI make research assistants more or less valuable. As AI automates routine analytical tasks, both professors and RAs can concentrate more on ideas and judgement, thereby expanding output and demand for skilled research labor. This is yet again the O-ring focus effect in practice.

Same in our lab. Each additional member can do so much more, the challenge is getting everyone up to speed, having open discussions on best ways to use these tools vs not and building a culture where people feel valued more not less. https://t.co/0nEwUadRPF

— Abhishek Nagaraj ??? (@abhishekn) March 18, 2026

What do exposure indices capture?

Let us bring this back to the exposure framework. In the standard approach, a management consultant is highly “exposed” to AI whereas a truck driver is not. But does this mean that the consultant is at higher displacement risk than the truck driver? Not necessarily. The consultant’s high exposure may actually be good news because it means AI will augment many of their complementary tasks, triggering the focus effect and potentially raising wages. On the other hand, the truck driver’s moderate exposure on a single critical task is much more dangerous because trucking companies have a much higher incentive to automate the task of driving, and once that’s done, the job is gone as well. These incentives are already playing out in practice:

NEWS: Jeff Bezos is in talks to raise $100 billion for a new fund that would buy up manufacturing companies and seek to use AI technology to accelerate their path to automation.

It's linked to Jeff's Project Prometheus AI startup, which aims to build AI products for engineering… pic.twitter.com/6zlXRQHhOY

— Sawyer Merritt (@SawyerMerritt) March 19, 2026

The relevant object therefore is not average task exposure, but the structure of bottlenecks and how automation reshapes worker time around them. Two jobs with identical exposure scores can have completely opposite displacement risks depending on whether their tasks are complements, whether demand for their output is elastic or inelastic, and the incentives of the firm to invest in automation. The workers at greatest risk are not necessarily those with the highest average exposure, but those whose jobs are built around a small number of core tasks that AI can automate.

1 In the case where jobs are not fully automated, the cost savings from automating the marginal task will depend on the complementarities between the other tasks in the job. The exact relationship is worked out in the O-ring model of automation paper.

Alex Imas is a professor at UChicago Booth. Doing research on Economics and Applied AI. Substack here. 

Via Remix News,

The Klodzko scandal could bring down the Civic Coalition (KO).

This is the view of PiS candidate for prime minister, Przemyslaw Czarnek, despite the localized nature of the crime.

“This is a group of people who really have a lot on their minds,” the politician says.

A pedophilia and zoophilia scandal in the Lower Silesian Voivodeship has shocked Poland. Przemyslaw L., 45, was sentenced to 25 years in prison for sexual offenses committed against underage girls, bestiality, and recording these acts on film and in photographs.

According to Do Rzeczy, his ex-wife, Kamila L., a former Civic Platform activist, was sentenced to 6.5 years in prison for failing to provide assistance to her minor daughter from a previous relationship, who was a victim of rape, and for complicity in animal abuse.

Przemyslaw Czarnek, the PiS candidate for prime minister, commented on the shocking case and its possible political consequences on Telewizja Republika on Wednesday.

“This is a very serious scandal that will, in my opinion, sink the Civic Platform. I spoke about the Civic Platform and their absolutely scandalous behavior three years ago, when there was a debate on the vote of no confidence in me. And I shouted from the parliamentary podium that these people should be feared, because these people from the Civic Platform, the mayors of cities from the Civic Platform, finance associations and organizations with enormous public funds—over a billion zloty a year—that simply deal with dramatic issues,” said the former Minister of Education and Science.

“I mentioned programs that were simply perverted by their very name,” he added.

As Czarnek pointed out, “this is a community of people who really have a lot going for them, financing these kinds of communities that commit these kinds of shameless, dramatic, criminal, anti-human actions against children, and against animals as well, because we are dealing with zoophilia there as well.”

Politicians from the Civic Coalition also commented on the situation.

“It’s difficult to hold someone accountable; it’s a situation that can happen to anyone. Anyone can have a neighbor like that whom they know nothing about until the police and prosecutors get involved,” one Civic Coalition MP told Wirtualna Polska.

The interviewees emphasize that the case involves a former Civic Coalition (KO) activist and a very low-level figure. The police and prosecutors acted, and the perpetrators were brought to justice, so it’s difficult to speak of the scandal’s political context. “It’s a local issue, perhaps also highlighted by local disputes between the mayor and the Civic Coalition,” notes the Civic Coalition politician.

Word has surfaced that Prime Minister Tusk is offering clarification.

“It’s more likely at the local level, not the central level, not from Deputy Marshal Monika Wielichowska; it’s hard to blame her,” says a senior Civic Coalition (KO) politician.

Wielichowska supported Kamila L. when she was campaigning for the regional position.

“This is certainly an inconvenient matter for Monika. She’s not handling it well,” the source adds.

Read more here...

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