Intel has been interested in entering the foundry (semiconductor contract manufacturing) space for a long time. For years, Intel proudly boasted of being at the forefront of semiconductor technology — being first to market with the FinFET and smaller and smaller process geometries.
So it’s interesting how, with the exception of the RibbonFET (the successor to the FinFET), almost all of Intel’s manufacturing technology announcements (see whitepaper) in it’s whitepaper to appeal to prospective foundry customers, all of it’s announcements pertain to packaging / “back end” technologies.
I think it’s both a recognition that they are no longer the furthest ahead in that race, as well as recognition that Moore’s Law scaling has diminishing returns for many applications. Now, a major cost and performance driver is technology that was once considered easily outsourced to low cost assemblers in Asia is now front and center.
IN AN EXCLUSIVE INTERVIEW ahead of an invite-only event today in San Jose, Intel outlined new chip technologies it will offer its foundry customers by sharing a glimpse into its future data-center processors. The advances include more dense logic and a 16-fold increase in the connectivity within 3D-stacked chips, and they will be among the first top-end technologies the company has ever shared with chip architects from other companies.
Immune cell therapy — the use of modified immune cells directly to control cancer and autoimmune disease — has shown incredible results in liquid tumors (cancers of the blood and bone marrow like lymphoma, leukemia, etc), but has stumbled in addressing solid tumors.
Iovance, which recently had its drug lifileucel approved by the FDA to treat advanced melanoma, has demonstrated an interesting spin on the cellular path which may prove to be effective in solid tumors. They extract Tumor-Infiltrating Lymphocytes (TILs), immune cells that are already “trying” to attack a solid tumor directly. Iovance then treats those TILs with their own proprietary process to expand the number of those cells and “further activate” them (to resist a tumor’s efforts to inactivate immune cells that may come after them) before reintroducing them to the patient.
This is logistically very challenging (not dissimilar to what patients awaiting other cell therapies or Vertex’s new sickle cell treatment need to go through) as it also requires chemotherapy for lymphocyte depletion in the patient prior to reintroduction of the activated TILs. But, the upshot is that you now have an expanded population of cells known to be predisposed to attacking a solid tumor that can now resist the tumor’s immune suppression efforts.
To me, the beauty of this method is that it can work across tumor types. Iovance’s process (from what I’ve gleamed from their posters & presentations) works by getting more and more activated immune cells. Because they’re derived from the patient, these cells are already predisposed to attack the particular molecular targets of their tumor.
This is contrast to most other immune cell therapy approaches (like CAR-T) where the process is inherently target-specific (i.e. get cells that go after this particular marker on this particular tumor) and each new target / tumor requires R&D work to validate. Couple this with the fact that TILs are already the body’s first line of defense against solid tumors and you may have an interesting platform for immune cell therapy in solid tumors.
The devil’s in the details and requires more clinical study on more cancer types, but suffice to say, I think this is incredibly exciting!
Its clearance is the “culmination of scientific and clinical research efforts,” said Peter Marks, director of the FDA’s Center for Biologics Evaluation and Research, in a statement.
Van Brummelen suggests that Bianchini’s schooling in economics might have been key to his invention, because he wasn’t embedded in sexagesimal numbers from early in his career, as other astronomers were. But his approach was perhaps too revolutionary to catch on at first. “In order to understand what Bianchini was doing, you had to learn a completely new system of arithmetic,” he says.
A century and a half later, however, “decimal notation was in the air”. Astronomers working with smaller and smaller subdivisions were inventing different systems, desperate for ways to simplify complex calculations. Clavius’s work influenced later popularizers of decimal fractions, such as Flemish mathematician Simon Stevin, as well as Scottish astronomer and inventor of logarithms John Napier, who adopted the decimal point. Chabás argues that historians should reassess Bianchini’s importance. Although he has been “eclipsed” by other figures, there’s clearly “a path of ideas”, he says, leading back to Bianchini.
This one chart (published in Canary Media) illustrates both the case for optimism for our ability to deal with climate change as well as a clear case of how geopolitical pressures can dramatically impact energy choices: the rapid increase in use of renewable energy (mainly at the expense of fossil fuels) as source of electricity in the EU.
The cleanest sources of electricity could soon make up the largest share of electricity generation in the European Union. Wind and solar made huge strides last year, producing more than one-quarter of the EU’s electricity for the first time, while fossil fuel generation plummeted. Power-sector emissions fell by a record 19 percent in the region last year.
While much attention is (rightly) focused on the role of TSMC (and its rivals Samsung and Intel) in “leading edge” semiconductor technology, the opportunity at the so-called “lagging edge” — older semiconductor process technologies which continue to be used — is oftentimes completely ignored.
The reality of the foundry model is that fab capacity is expensive to build and so the bulk of the profit made on a given process technology investment is when it’s years old. This is a natural consequence of three things:
Very few semiconductor designers have the R&D budget or the need to be early adopters of the most advanced technologies. (That is primarily relegated to the sexiest advanced CPUs, FPGAs, and GPUs, but ignores the huge bulk of the rest of the semiconductor market)
Because only a small handful of foundries can supply “leading edge” technologies and because new technologies have a “yield ramp” (where the technology goes from low yield to higher as the foundry gets more experience), new process technologies are meaningfully more expensive.
Some products have extremely long lives and need to be supported for decade-plus (i.e. automotive, industrial, and military immediately come to mind)
As a result, it was very rational for GlobalFoundries (formerly AMD’s in-house fab) to abandon producing advanced semiconductor technologies in 2018 to focus on building a profitable business at the lagging edge. Foundries like UMC and SMIC have largely made the same choice.
This means giving up on some opportunities (those that require newer technologies) — as GlobalFoundries is finding recently in areas like communications and data center — but provided you have the service capability and capacity, can still lead to not only a profitable outcome, but one which is still incredibly important to the increasingly strategic semiconductor space.
When GlobalFoundries abandoned development of its 7 nm-class process technology in 2018 and refocused on specialty process technologies, it ceased pathfinding, research, and development of all technologies related to bleeding-edge sub-10nm nodes. At the time, this was the correct (and arguably only) move for the company, which was bleeding money and trailing behind both TSMC and Samsung in the bleeding-edge node race. But in the competitive fab market, that trade-off for reduced investment was going to eventually have consequences further down the road, and it looks like those consequences are finally starting to impact the company. In a recent earnings call, GlobalFoundries disclosed that some of the company’s clients are leaving for other foundries, as they adopt sub-10nm technologies faster than GlobalFoundries expected.
Every standard products company (like NVIDIA) eventually gets lured by the prospect of gaining large volumes and high margins of a custom products business.
And every custom products business wishes they could get into standard products to cut their dependency on a small handful of customers and pursue larger volumes.
Given the above and the fact that NVIDIA did used to effectively build custom products (i.e. for game consoles and for some of its dedicated autonomous vehicle and media streamer projects) and the efforts by cloud vendors like Amazon and Microsoft to build their own Artificial Intelligence silicon it shouldn’t be a surprise to anyone that they’re pursuing this.
Or that they may eventually leave this market behind as well.
While using NVIDIA’s A100 and H100 processors for AI and high-performance computing (HPC) instances, major cloud service providers (CSPs) like Amazon Web Services, Google, and Microsoft are also advancing their custom processors to meet specific AI and general computing needs. This strategy enables them to cut costs as well as tailor capabilities and power consumption of their hardware to their particular needs. As a result, while NVIDIA’s AI and HPC GPUs remain indispensable for many applications, an increasing portion of workloads now run on custom-designed silicon, which means lost business opportunities for NVIDIA. This shift towards bespoke silicon solutions is widespread and the market is expanding quickly. Essentially, instead of fighting custom silicon trend, NVIDIA wants to join it.
Fascinating data from the BLS on which jobs have the greatest share of a particular gender or race. The following two charts are from the WSJ article I linked. I never would have guessed that speech-language pathologists (women), property appraisers (white), postal service workers (black), or medical scientists (Asian) would have such a preponderance of a particular group.
The Bureau of Labor Statistics each year publishes data looking at the gender and racial composition of hundreds of occupations, offering a snapshot of how workers sort themselves into many of the most important jobs in the country.
There are sociology textbooks’ worth of explanations for these numbers. One clear conclusion: Many occupations skew heavily toward one gender or race, leading to a workforce where 96.7% of preschool and kindergarten teachers are women, two-thirds of manicurists and pedicurists are Asian, and 92.4% of pilots and flight engineers are white.
Commercial real estate (and, by extension, community banks) are in a world of hurt as hybrid/remote work, higher interest rates, and property bubbles deflating/popping collide…
Many banks still prefer to work out deals with existing landlords, such as offering loan extensions in return for capital reinvestments toward building upgrades. Still, that approach may not be viable in many cases; big companies from Blackstone to a unit of Pacific Investment Management Co. have walked away from or defaulted on properties they don’t want to pour more money into. In some cases, buildings may be worth even less today than the land they sit on.
“When people hand back keys, that’s not the end of it — the equity is wiped but the debt is also massively impaired,” said Dan Zwirn, CEO of asset manager Arena Investors, which invests in real estate debt. “You’re talking about getting close to land value. In certain cases people are going to start demolishing things.”
One of the core assumptions of modern financial planning and finance is that stocks have better returns over the long-run than bonds.
The reason “seems” obvious: stocks are riskier. There is, after all, a greater chance of going to zero since bond investors come before stock investors in a legal line to get paid out after a company fails. Furthermore, stocks let an investor participate in the upside (if a company grows rapidly) whereas bonds limits your upside to the interest payments.
A fascinating article by Santa Clara University Professor Edward McQuarrie published in late 2023 in Financial Analysts Journal puts that entire foundation into doubt. McQuarrie collects a tremendous amount of data to compute total US stock and bond returns going back to 1792 using newly available historical records and data from periodicals from that timeframe. The result is a lot more data including:
coverage of bonds and stocks traded outside of New York
coverage of companies which failed (such as The Second Bank of the United States which, at one point, was ~30% of total US market capitalization and unceremoniously failed after its charter was not renewed)
includes data on dividends (which were omitted in many prior studies)
calculates results on a capitalization-weighted basis (as opposed to price-weighted / equal-weighted which is easier to do but less accurately conveys returns investors actually see)
The data is fascinating, as it shows that, contrary to the opinion of most “financial experts” today, it is not true that stocks always beat bonds in the long-run. In fact, much better performance for stocks in the US seems to be mainly a 1940s-1980s phenomena (see Figure 1 from the paper below)
Stock and bond performance (normalized to $1 in 1792, and renormalized in 1982) on a logarithmic scale Source: Figure 1, McQuarrie et al
Put another way, if you had looked at stocks vs bonds in 1862, the sensible thing to tell someone was “well, some years stocks do better, some years bonds do better, but over the long haul, it seems bonds do better (see Table 1 from the paper below).
The exact opposite of what you would tell them today / having only looked at the post-War world.
This problem is compounded if you look at non-US stock returns where, even after excluding select stock market performance periods due to war (i.e. Germany and Japan following World War II), focusing even on the last 5 decades shows comparable performance for non-US stocks as non-US government bonds.
Even assumptions viewed as sacred, like how stocks and bonds can balance each other out because their returns are poorly correlated, shows huge variation over history — with the two assets being highly correlated pre-Great Depression, but much less so (and swinging wildly) afterwards (see Figure 6 below)
Stock and Bond Correlation over Time Source: Figure 6, McQuarrie et al
Now neither I nor the paper’s author are suggesting you change your fundamental investment strategy as you plan for the long-term (I, for one, intend to continue allocating a significant fraction of my family’s assets to stocks for now).
But, beyond some wild theorizing on why these changes have occurred throughout history, what this has reminded me is that the future can be wildly unknowable. Things can work one way and then suddenly stop. As McQuarrie pointed out recently in a response to a Morningstar commenter, “The rate of death from disease and epidemics stayed at a relatively high and constant level from 1793 to 1920. Then advances in modern medicine fundamentally and permanently altered the trajectory … or so it seemed until COVID-19 hit in February 2020.”
If stocks are risky, investors will demand a premium to invest. But if stocks cease to be risky once held for a long enough period—if stocks are certain to have strong returns after 20 years and certain to outperform bonds—then investors have no reason to expect a premium over these longer periods, given that no shortfall risk had to be assumed. The expanded historical record shows that stocks can perform poorly in absolute terms and underperform bonds, whether the holding period is 20, 30, 50, or 100 years. That documentation of risk resolves the conundrum.
While much of the commentary has been about Figma’s rapid rise and InVision’s inability to respond, I saw this post on Twitter/X from one of InVision’s founders Clark Valberg about what happened. The screenshotted message he left is well-worth a read. It is a great (if slightly self-serving / biased) retrospective.
As someone who was a mere bystander during the events (as a newly minted Product Manager working with designers), it felt very true to the moment.
I remember being blown away by how the entire product design community moved to Sketch (from largely Adobe-based solutions) and then, seemingly overnight, from Sketch to Figma.
While it’s fair to criticize the leadership for not seeing web-based design as a place to invest, I think the piece just highlights how because it wasn’t a direct competitor to InDesign (but to Sketch & Adobe XD) and because the idea of web-based wasn’t on anyone’s radar at the time, it became a lethal blind spot for the company. It’s Tech Strategy 101 and perfectly highlights Andy Grove’s old saying: “(in technology,) only the paranoid survive”.
Hey Jason…
“Clark from InVision” here…
I’ve been somewhat removed from the InVision business since transitioning out ~2 years ago, and this is the first time I’ve reacted to the latest news publicly. I’m choosing to do so here because in many ways your post is a full-circle moment for me. MANY (perhaps most) of the underlying philosophies that drove InVision from the very beginning were inspired by my co-founder @BenNadel and I reading and re-reading Getting Real. It was our early-stage hymnal.
Apologies for steam of consciousness rant and admitted inherent bias — I’m a founder after all 🙂
We have a Nissan Ariya and currently DON’T have a home charger (yet — waiting on solar which is another boondoggle for another post). As we live in a town with abundant EVGo chargers (and the Ariya came with 1 yr of free EVGo charging), we thought we could manage.
When it works, its amazing. But it doesn’t … a frustrating proportion of the time. And, as a result, we’ve become oddly superstitious about which chargers we go to and when.
I’m glad the charging companies are aware and are trying to address the problem. As someone who’s had to ship and support product, I also recognize that creating charging infrastructure in all kinds of settings which need to handle all kinds of electric vehicles is not trivial.
But, it’s damn frustrating to not be able to count on these (rest assured, we will be installing our own home charger soon), so I do hope that future Federal monies will have strict uptime requirements and penalties. Absent this, vehicle electrification becomes incredibly difficult outside of the surburban homeowner market.
J.D. Power reported in August that 20 percent of all non-Tesla EV drivers in its most recent study said they visited a charger but did not charge their vehicle, whether because the charger was inoperable or because of long wait times to use it, up from 15 percent in the first quarter of 2021.
Fear of inadequate public charging has now overtaken “range anxiety” as the chief concern about EVs among the car-buying public, according to J.D. Power. “Although the majority of EV charging occurs at home” — about 80 percent of it, according to industry data — “public charging needs to provide a much better experience across the board, not just for the users of today, but also to alleviate the concerns of skeptical future customers,” said Brent Gruber, executive director of J.D. Power’s global automotive practice.
The collapse of China’s massive property bubble is under way and it is wreaking havoc as significant amounts of the debt raised by Chinese property builders is from offshore investors.
Because of (well-founded) concerns on how Chinese Mainland courts would treat foreign concerns, most of these agreements have historically been conducted under Hong Kong law. As a result, foreign creditors have (understandably) hauled their deadbeat Chinese property builder debtors to court there.
While the judgements (especially from Linda Chan, the subject of this Bloomberg article) are unsurprisingly against the Chinese property builders (who have been slow to release credible debt restructuring plans), the big question remains whether the Mainland Chinese government will actually enforce these rulings. It certainly would make life harder on (at least until recently very well-connected) Chinese property builders at a moment of weakness in the sector.
But, failure to do so would also hurt the Chinese government’s goal of encouraging more foreign investment: after all, why would you invest in a country where you can’t trust the legal paper?
Never before has there been such a wave of Chinese corporate defaults on bonds sold to foreign investors. And never in recent memory has a bankruptcy judge in Hong Kong, the de-facto home for such cases, earned a reputation for holding deadbeat companies to account quite like Chan.
Chan, 54, has displayed an unwavering determination to give creditors a fair shot at recouping as much of their money as they can. One morning in early May, she shocked the packed courtroom by suddenly ordering the liquidation of Jiayuan. She had peppered the company’s lawyers that day as they tried, unsuccessfully, to explain why they needed more time to iron out their debt restructuring proposal.
And then, late last month, Chan put lawyers for Evergrande, the most indebted developer of them all, on notice: Either turn over a concrete restructuring proposal in five weeks or face the same fate as Jiayuan.
It’s both unsurprising but also astonishing at the same time.
Amazon.com has grabbed the crown of biggest delivery business in the U.S., surpassing both UPS and FedEx in parcel volumes.
The Seattle e-commerce giant delivered more packages to U.S. homes in 2022 than UPS, after eclipsing FedEx in 2020, and it is on track to widen the gap this year, according to internal Amazon data and people familiar with the matter. The U.S. Postal Service is still the biggest parcel service by volume; it handles hundreds of millions of packages for all three companies.
Market phase transitions have a tendency to be incredibly disruptive to market participants. A company or market segment used to be the “alpha wolf” can suddenly find themselves an outsider in a short time. Look at how quickly Research in Motion (makers of the Blackberry) went from industry darling to laggard after Apple’s iPhone transformed the phone market.
Something similar is happening in the high performance computing (HPC) world (colloquially known as supercomputers). Built to do the highly complex calculations needed to simulate complex physical phenomena, HPC was, for years, the “Formula One” of the computing world. New memory, networking, and processor technologies oftentimes got their start in HPC, as it was the application that was most in need of pushing the edge (and had the cash to spend on exotic new hardware to do it).
The use of GPUs (graphical processing units) outside of games, for example, was a HPC calling card. NVIDIA’s CUDA framework which has helped give it such a lead in the AI semiconductor race was originally built to accelerate the types of computations that HPC could benefit from.
The success of Deep Learning as the chosen approach for AI benefited greatly from this initial work in HPC, as the math required to make deep learning worked was similar enough that existing GPUs and programming frameworks could be adapted. And, as a result, HPC benefited as well, as more interest and investment flowed into the space.
But, we’re now seeing a market transition. Unlike with HPC which performs mathematical operations requiring every last iota of precision on mostly dense matrices, AI inference works on sparse matrices and does not require much precision at all. This has resulted in a shift in industry away from software and hardware that works for both HPC and AI and towards the much larger AI market specifically.
The HPC community is used to being first, and we always considered ourselves as the F1 racing team of computing. We invent the turbochargers and fuel injection and the carbon fiber and then we put that into more general purpose vehicles, to use an analogy. I worry that the HPC community has sort of taken the backseat when it comes to AI and is not leading the charge. Like you, I’m seeing a lot of this AI stuff being led out of the hyperscalers and clouds. And we’ve got to find a way to take that back and carve our own use cases. There are a lot more HPC sites around the world than there are cloud sites, and we have got access to all a lot of data.
I’m over two months late to seeing this study, but a brilliant study design (use insurance data to measure rate of bodily injury and property damage) and strong, noteworthy conclusion (doesn’t matter how you cut it, Waymo’s autonomous vehicle service resulted in fewer injuries per mile and less property damage per mile than human drivers in the same area) make this worthwhile to return to! Short and sweet paper from researchers from Waymo, Swiss Re (the re-insurer), and Stanford that is well worth the 10 minute read!
When TO and RO datasets were combined, totaling 39,096,826 miles, there was a significant reduction in bodily injury claims frequency by 93% (0.08 vs 1.09 claims per million miles), TO+ROBI 95% CI [0.02, 0.22], Baseline 95% CI [1.08, 1.09]. Property damage claims frequency was significantly reduced by 93% (0.23 vs 3.17 claims per million miles), TO+ROPDL 95% CI [0.11, 0.44], Baseline 95% CI [3.16, 3.18].
My good friend Danny Goodman (and Co-Founder at Swarm Aero) recently wrote a great essay on how AI can help with America’s defense. He outlines 3 opportunities:
“Affordable mass”: Balancing/augmenting America’s historical strategy of pursuing only extremely expensive, long-lived “exquisite” assets (e.g. F-35’s, aircraft carriers) with autonomous and lower cost units which can safely increase sensor capability &, if it comes to it, serve as alternative targets to help safeguard human operators
Smarter war planning: Leveraging modeling & simulation to devise better tactics and strategies (think AlphaCraft on steroids)
Smarter procurement: Using AI to evaluate how programs and budget line items will actually impact America’s defensive capabilities to provide objectivity in budgeting
With the proper rules in place, AI is poised to be a transformative force that will strengthen America’s national defense. It will give our military new weapons systems and capabilities, smarter ways to plan for increasingly complex conflicts, and better ways to decide what to build and buy, and when. Along the way, it will help save both taxpayer dollars and, more importantly, lives.
As a parent myself, few things throw off my work day as much as a wrench in my childcare — like a kid being sick and needing to come home or a school/childcare center being closed for the day. The time required to change plans while balancing work, the desire to check-in on your child throughout the work day to make sure they’re doing okay… and this is as someone with a fair amount of work flexibility, a spouse who also has flexibility, and nearby family who can pitch in.
Childcare, while expensive, is a vital piece of the infrastructure that makes my and my spouse’s careers possible — and hence the (hopefully positive 😇) economic impact we have possible. It’s made me very sympathetic to the notion that we need to take childcare policy much more seriously — something that I think played out for millions of households when COVID disrupted schooling and childcare plans.
Census data suggest that, as things are, the child-care industry nationwide has been operating in the red for two straight years. Now, as programs still stressed by the pandemic lose a major source of public funds, many programs around the country are considering closure. When these businesses do shut down, they can send shock waves throughout their local economies. The shuttered child-care business sheds jobs; parents that relied on that business lose care arrangements for their kids, which in turn disrupts parents’ ability to work; and the employers of those parents must then scramble to adjust for lost workforce hours.
While each of those can feel like an individual misfortune, they are all part of a larger system of how our country cares for our young while adults work — or fails to do so. And the ripple effects can be enormous. Here’s one story of what happened downstream when a single day-care center in Wisconsin shut its doors.
Silicon nerd 🤓 that I am, I have gone through multiple cycles of excited-then-disappointed for Windows-on-ARM, especially considering the success of ChromeOS with ARM, the Apple M1/M2 (Apple’s own ARM silicon which now powers its laptops), and AWS Graviton (Amazon’s own ARM chip for its cloud computing services).
I may just be setting myself up for disappointment here but these (admittedly vendor-provided) specs for their new Snapdragon X (based on technology they acquired from Nuvia and are currently being sued for by ARM) look very impressive. Biased as they may be, the fact that these chips are performing in the same performance range as Intel/AMD/Apple’s silicon on single-threaded benchmarks (not to mention the multi-threaded applications which work well with the Snapdragon X’s 12 cores) hopefully bodes well for the state of CPU competition in the PC market!
Single-threaded CPU performance (Config A is a high performance tuned offering, Config B is a “thin & light” configuration)Multi-threaded CPU performance (Config A is a high performance tuned offering, Config B is a “thin & light” configuration)
Overall, Qualcomm’s early benchmark disclosure offers an interesting first look at what to expect from their forthcoming laptop SoC. While the competitive performance comparisons are poorly-timed given that next-generation hardware is just around the corner from most of Qualcomm’s rivals, the fact that we’re talking about the Snapdragon X Elite in the same breath as the M2 or Raptor Lake is a major achievement for Qualcomm. Coming from the lackluster Snapdragon 8cx SoCs, which simply couldn’t compete on performance, the Snapdragon X Elite is clearly going to be a big step up in virtually every way.
Qualcomm Snapdragon X Elite Performance Preview: A First Look at What’s to Come Ryan Smith | Anandtech
Gene editing makes possible new therapies and actual cures (not just treatments) that were previously not. But, one thing that doesn’t get discussed a great deal is how these new gene editing-based therapies throw the “take two and call me in the morning” model out the window.
referral by hematologist (not to mention insurance approval!)
collection of cells (probably via bone marrow extraction)
(partial) myeloablation of the patient
shipping the cells to a manufacturing facility
manufacturing facility applies gene editing on the cells
shipping of cells back
infusion of the gene edited cells to the patient (so they hopefully engraft back in their bone marrow)
Each step is complicated and has their own set of risks. And, while there are many economic aspects of this that are similar to more traditional drug regimens (high price points, deep biological understanding of disease, complicated manufacturing [esp for biologicals], medical / insurance outreach, patient education, etc.), gene editing-based therapies (which can also include CAR-T therapy) now require a level of ongoing operational complexity that the biotech/pharmaceutical industries will need to adapt to if we want to bring these therapies to more people.
To make and administer the therapy is laborious, first requiring a referral from a hematologist. If the patient is eligible, their cells are collected and shipped to a manufacturing facility where they’re genetically edited to express a form of an essential protein called hemoglobin.
The cells are then shipped back to a treatment facility that infuses them into the patient’s bone marrow. But to make sure there’s enough room for these new cells, patients first undergo myeloablation — a chemotherapy regimen that can be very difficult on their bodies and comes with the risk of infertility. Older patients may not be healthy enough to receive this treatment.
“This is an extensive and expensive process,” Arbuckle said.
Something is wrong with the state of the Marvel Cinematic Universe (MCU).
In 2019, Disney/Marvel topped off an amazing decade-plus run of films with Avengers: Endgame, becoming (until Avatar was re-released in China) the highest grossing film of all time. This was in spite of an objectively complicated plot which required a deep understanding of all of Marvel Cinematic Universe continuity to follow.
And yet critics and fans (myself included! 🙋🏻♂️) loved it! It seemed like Marvel could do no wrong.
It doesn’t feel that way anymore. While I’ve personally enjoyed Black Panther: Wakanda Forever and Shang-Chi, this Time article does a good job of critiquing how complicated the MCU has become, so much so that a layperson can’t just watch one casually.
But it misses one additional thing which I think gets to the heart of why the MCU just doesn’t feel right anymore. The MCU is now so commercially large, that the scripts feel like they’re written by a committee of businesspeople (oh make sure you’re setting up this other show/movie! let’s get in an action scene with some kind of viral quip!) rather than writers/directors trying to tell an entertaining story for the sake of the story.
Does all this sound like gobbledygook? For years now, audiences have not been able to watch Marvel shows and movies casually. But watching Loki Season 2, I felt I could not even look down at my phone for a second without getting completely lost. Heck, even if you’re watching with rapt attention, you’ll probably have a difficult time keeping up with the convoluted time travel shenanigans. The various MacGuffins, Easter eggs, and pseudoscientific explanations of superpowers used to be fun. Now they feel like homework.
