Tuesday, September 16, 2025

Plant-Based Meat Products Show Substitution is Hard

It is a truism that substitute products, such as plant-based proteins that mimic meat, must have some obvious value that induces consumers to switch. That might be lower price, clear product advantages or something else.


So far, plant-based meat substitutes have not gotten the formula right. 




source: Good Food Institute

Many would-be buyers arguably still would prefer to substitute plant-based protein for meat. But product improvements and price issues have to be addressed.

So far, though advocates might be repeat buyers, the typical shopper has not found the expected product advantages, nor any cost advantage. 


Plant-based meat has carried a large price gap versus conventional meat of as much as 80 percent. Not many product alternatives with comparable characteristics or value to the target product are going to succeed with a price premium that large. 


But for many buyers, product attributes are not “equal.” Taste and texture seem to discourage buyers.  


Macro pressures (inflation, cost sensitivity) do not help, either. 


Year

Plant-based meat & seafood retail sales (USD)

Unit sales (million units)

Total plant-based retail food sales (USD)

Household penetration (% of U.S. households buying plant-based meat & seafood)

2022

$1.43 B (approx.) — industry retail estimate. The Good Food Institute

270 million units (GFI/SPINS). The Good Food Institute

$8.2 B (GFI/SPINS dataset for 2022). The Good Food Institute

19% (household penetration, 2022). The Good Food Institute

2023

$1.26 B (approx., derived from reported year-over-year change). The Good Food Institute

— (category showed declines vs 2022; SPINS/GFI tracked a drop but official 2023 unit number is in the GFI dataset). The Good Food Institute

$8.1 B (GFI/PBFA reporting for 2023). The Good Food Institute

15% (penetration, 2023). The Good Food Institute

2024

$1.17 B (GFI market overview / SPINS-based figure). The Good Food Institute

195 million units (GFI report: 195M units sold in 2024). The Good Food Institute

≈ $8.1 B (category roughly stable overall; plant-based meat & milk declines offset other growth). The Good Food InstituteThe Food Institute

13% (penetration, 2024). The Good Food Institute


But work continues on a number of fronts, experts say. So early obstacles might be overcome, optimists will insist. And some might argue that a few use cases, such as plant-based substitutes for ground meat (hamburger patties, sausages) have proven most successful so far. 


Category

Method

Purpose

Example Brands / Products

Taste

Protein purification & low-heat processing

Remove off-flavors (beany, grassy, bitter) from pea/soy proteins

Ripple Foods (pea milk), Beyond Meat’s pea protein refinement


Enzymatic treatment of proteins

Break down bitter peptides, improve solubility

Ingredion (Versawave proteins), Burcon NutraScience


Yeast & mushroom extracts

Boost umami and savory meat-like flavor

Quorn (mycoprotein), Unilever The Vegetarian Butcher


Maillard reaction precursors

Create cooked-meat aroma during cooking

Impossible Burger uses amino acids + sugars for aroma


Cultured fats for flavor release

Provide authentic meat fat flavor during cooking

Mission Barns, Lypid

Texture

High-moisture extrusion (HME)

Align protein fibers to mimic muscle

Beyond Meat, MorningStar Farms Incogmeato, Nestlé Sensational Burger


Shear-cell technology

Create long fibrous structures without high temp/pressure

Nutreco / Rival Foods partnership


Blending multiple proteins

Adjust chewiness & elasticity

Gardein (pea + wheat + soy), Lightlife


Encapsulated fats & emulsions

Simulate marbling & juiciness

Lypid PhytoFat, Beyond Meat marbling


Layered component assembly

Build steak/chicken textures with different layers

Meati (mycelium-based “whole cut”), Juicy Marbles (plant-based steak)

Visual realism

Natural colorants

Raw-to-cooked color shift

Impossible (soy leghemoglobin), Beyond (beet juice extract)


Visible marbling inclusions

Mimic animal fat streaks

Juicy Marbles, Chunk Foods


Heat-reactive appearance

Browning/grill mark simulation

MorningStar Grillers, Beyond Cookout Burger

Advanced / Hybrid

Precision fermentation

Produce animal-identical proteins (heme, whey, casein)

Impossible Foods (heme), Perfect Day (whey protein)


Cultured fat inclusion

Use real animal fat grown in bioreactors

Mission Barns, Hoxton Farms


3D food printing

Layer plant proteins for whole-muscle cuts

Redefine Meat, NovaMeat


Enzyme cross-linking

Modify protein gels for bite & elasticity

Enzymtec, R&D at Kerry


Hybrid plant + cultivated meat

Improve taste & realism while reducing animal content

Eat Just GOOD Meat, Upside Foods (future planned blends)


Product substitution can take any number of forms, including changes of technology, price, value, performance, problem solved or different materials or construction. 

Technology-driven substitution

New Product

Replaced Product

Why It Succeeded

Smartphones

Feature phones, standalone cameras, MP3 players, GPS devices

Combined multiple devices in one; convenience outweighed cost

Streaming services (Netflix, Spotify)

DVD rentals, CDs, broadcast TV, radio

On-demand access, personalization, lower friction

LED lighting

Incandescent & CFL bulbs

Lower energy use, longer lifespan, better performance

Digital photography

Film cameras & film processing

Instant review, no film cost, easy sharing

Price/value substitution

New Product

Replaced Product

Why It Succeeded

Private-label grocery brands

National branded packaged goods

Comparable quality at lower price; retail shelf control

Refurbished enterprise IT hardware

New OEM hardware

Lower capex; acceptable reliability for many workloads

Budget airlines (Southwest, Ryanair)

Full-service carriers

Lower fares, point-to-point routes

Performance/feature substitution

New Product

Replaced Product

Why It Succeeded

Cordless power tools

Corded power tools

Mobility, convenience, battery improvements

Electric vehicles

Internal combustion engine vehicles (for some segments)

Lower running costs, performance, environmental positioning

Solid-state drives (SSD)

Hard disk drives (HDD) in laptops

Faster performance, lower power, durability

Business process / B2B substitution

New Product / Service

Replaced Product / Service

Why It Succeeded

Cloud computing (AWS, Azure)

On-premise servers

Elastic scaling, reduced capex, speed of deployment

SaaS CRM (Salesforce)

Installed CRM software

Lower IT overhead, constant updates, remote access

E-procurement platforms

Paper-based or email-based purchasing

Speed, transparency, auditability

VoIP telephony

Traditional PBX systems

Cost savings, integration with software platforms

Material & ingredient substitution

New Product

Replaced Product

Why It Succeeded

Aluminum cans

Glass bottles for beverages

Lighter, cheaper to transport, unbreakable

Synthetic rubber

Natural rubber

Stable supply, price stability, performance in varied conditions

Plant-based milks (soy, almond, oat)

Dairy milk (for some buyers)

Lactose-free, perceived health/environmental benefits


Artificial General Intelligence is Not Here, but Could be "Close," (for Such a Major Technology)

How far away is artificial general intelligence? Further away than many now claim, says Google DeepMind CEO Dennis Hassabis. But some might take heart that he estimates we might actually get to AGI in five to 10 years.

For many technology watchers, that would be a breathtakingly fast development for something as sophisticated as AGI.

Granted, "how long" it takes any new technology to develop depends on how we choose to set the "start" date. Optimists might take a shorter view; pessimists might include lots of seminal research in the broader AI field to see the "start" date. 

And then there is the matter of the degree of deployment or use that qualifies as the commercial arrival date. What percentage of humans have to use AGI regularly to allow us to claim commercial deployment at some reasonable scale? 



Monday, September 15, 2025

Of Thee I Sing

For many of us, this still is the promise of America. It's an idea. We need to stand for her. Let the haters hate.

Who "Needs" 6G to be Revolutiionary?

By about 2030, standards bodies and suppliers will have gotten quite a ways down the road of preparing the next generation of mobile networks to succeed 5G. There will be claims about how “revolutionary” it might be, as we heard about 3G, 4G and 5G before. 


So "who" in the value chain gets most "value" from such claims about "revolutionary new features?" Consumers, app suppliers and users perhaps will benefit, but incrementally, in the form of higher speeds and lower latency.


Infrastructure suppliers and service providers, on the other hand, "need" to make such claims. Without dramatic new features, it is hard for infra suppliers to sell new networks. Without the promise of important new features, service providers will have a hard time convincing regulators to grant spectrum to build the new networks.


In other words, 6G will largely be "nice to have" for consumers and app providers. The promise of 6G progress will be "must have" if the networks are to be built.


There will be requirements for additional spectrum, as always has been the case when a mobile next-generation network has launched. The issue is how much new spectrum might be required. 


And even if the general rule is that users consume more data, and therefore use more bandwidth, over time, there are some questions about the degree to which mobile operators will need to spend heavily on new spectrum, though governments who make money selling spectrum will prefer higher amounts and costlier prices. 


Mobile service providers obviously will want to limit their investment in new spectrum resources. Keep in mind that they have other avenues for doing so. They can create smaller cells; they can use more-efficient radios and network elements; better air interfaces and reclaim spectrum supporting older networks that are decommissioned (2G and 3G being the best examples at the moment). 


But offloading demand to fixed networks has become a huge tool as well. 


Wi-Fi handles 70 percent to 80 percent of total smartphone data consumption, with the exact figure varying by region. Wi-Fi data consumption in the United States is about  85 percent to 90 percent, for example, while lower in emerging markets (around 50 percent to 70 percent), according to estimates fromCisco, Ericsson, and OpenSignal, for example. 


Year

Wi-Fi Data Consumption (EB/month)

Mobile Network Data Consumption (EB/month)

Percentage on Wi-Fi

2024

383

164

70%

2025

460

197

70%


Beyond the Wi-Fi role, technologists and operators have gotten better at using older platforms to ease the transition to a next generation of networks, even if that means not all the touted features are available. Network slicing on 5G networks requires “standalone” platforms that in many cases are lightly deployed at the moment, for example. 


On the other hand, the faster speeds and higher bandwidth, plus lower latency of every next-generation network already is producing commercial revenue in significant amounts, such as using 5G platforms to support fixed wireless for home broadband. 


That might not be among the futuristic capabilities 5G was supposed to provide, but it has created new revenue and product possibilities. 


So perhaps some skepticism about the market “need” for 6G, and the resources needed to support it, are reasonable. Already, 6G is touted as supporting a new array of sensory information such as touch, taste and smell. 


Some of us would be that if such innovations actually arrive, it will be about the time 7G arrives, as that has been the pattern for past next-generation network innovations as well: the promised futuristic apps need twice as long to reach commercial success as predicted. 


So promised 3G innovations don’t arrive until 4G; 4G innovations don’t arrive until 5G. That isn’t to deny the practical advantages for each next-generation network: more capacity and lower latency. 


But those improvements are akin to the need fixed network operators have to upgrade copper access to optical fiber; satellite providers to upgrade from geostationary platforms to low-earth-orbit constellations, all of which support higher capacity networks. 


Mobile networks will need to continue to evolve to support higher speeds. The “revolutionary new applications and use cases”  might ultimately be less important. 


"Everyone" Wants More Housing. Just Not "In My Backyard"

Land use regulations including zoning laws, density restrictions, minimum lot sizes, height limits, rules on parking spaces and growth controls increase the cost of housing and limit its construction.

That matters if the U.S. housing supply is 4.7 million homes short of demand. It’s just basic supply and demand economics. And focusing on supply matters.

Local zoning, land use, density regulations and so forth often limit the amount, type, and location of new housing development, effectively constraining supply even as population growth, urbanization, and economic demand for housing rise.

This supply shortage pushes up prices and rents, making housing less affordable, particularly for low- and middle-income households.

For instance, regulations can impose lengthy permitting processes, environmental reviews, or inclusionary requirements that raise development costs, which are then passed on to buyers or renters.

But the bigger problem is simply that such rules are among many reasons more housing is not created.

 

To be sure, the rationale often is compelling: preserving community character, protecting the environment, or preventing urban sprawl.

But those very same rules create disincentives to build affordable housing, as they all restrict housing density or volume. 

Study Title

Authors

Year

Methodology and Key Findings

The Effect of Land Use Regulation on Housing and Land Prices

Keith R. Ihlanfeldt

2007

Used an endogenous index of regulatory restrictiveness across over 100 Florida cities; found greater restrictiveness increases house prices, decreases land prices, and leads to larger new homes.

The Effects of Land Use Regulation on the Price of Housing: What Do We Know? What Can We Learn?

John M. Quigley and Larry A. Rosenthal

2005

Reviewed empirical literature using surveys, econometric models (e.g., OLS, hedonic pricing), and regulatory indices; regulations like zoning and growth boundaries are associated with higher prices, but causality is not firmly established due to endogeneity and data limitations.

Land-Use Reforms and Housing Costs

Christina Plerhoples Stacy et al.

2023

Analyzed a panel dataset of 180 reforms in 1,136 U.S. cities (2000–2019) using machine learning, manual coding, and fixed-effects models; loosening restrictions increases supply by 0.8% over 3–9 years (mainly high-end units), while tightening raises median rents and reduces affordable units.

How Land-Use Regulation Undermines Affordable Housing

Sanford Ikeda and Emily Washington

2015

Reviewed literature and urban policy data; regulations reduce supply relative to free-market levels, increase costs (e.g., 10%+ "regulatory tax" in major cities), and disproportionately affect low-income households, potentially lowering GDP by limiting growth in productive areas.

Regulation and Housing Supply

Joseph Gyourko and Raven Molloy

2014

Literature review with surveys, panel data, and regression analyses (e.g., OLS, instrumental variables); strong positive link between regulation and prices (17–22% increases), reduced construction (4–22%), and lower supply elasticity, leading to volatility.

Zoning, Land-Use Planning, and Housing Affordability

Randal O'Toole

2017

Regression analysis of court decisions as proxies for regulation intensity (2000–2010 data); rising land-use and zoning regulations correlate with higher home prices in 44 and 36 states, respectively, with federal aid flowing more to restrictive states.

The Impact of Zoning on Housing Affordability

Edward L. Glaeser and Joseph Gyourko

2002

Compared house prices to construction costs across U.S. markets; zoning drives prices above costs in high-regulation areas (e.g., NYC, California), suggesting supply restrictions exacerbate affordability issues more than demand alone.

Do Restrictive Land Use Regulations Make Housing More Expensive Everywhere?

John Landis and Vincent J. Reina

2021

Examined 336 metro areas with multiple stringency measures and growth variables; restrictive regulations pervasively raise home values and rents, especially in growing/prosperous economies, but effects on supply vary by market.


"Not in my backyard" remains an important problem as well. We might say we want "X" as a policy. But almost nobody says they want "X" implemented in their own neighborhood. "Do it to somebody else" is the inevitable real-world, up close and personal answer, whatever the loftier images people have of themselves.

And that generally applies to all of us, as we ponder the impact of land use rules. It makes a huge difference whether we are among the "haves" or the "have nots."

Density will not appeal to those already living in low-density areas.

Conversely, density might make all the difference for those entering the owned-housing market.

To be crude, do land use planning rules preserve "privilege" for some at the expense of many others? Of course they do.

Is College Still Worth the Time and Expense?

Though U.S. residents increasingly question whether a college education is worth the time and expense,  it likely remains true that for any number of “white collar” or “office” jobs, a college degree does matter as a signaling device and sorting procedure for employers. 


In other words, a college degree acts as a signal to employers that a person possesses "unobservable" qualities like perseverance, conscientiousness, and the ability to delay gratification. That is why, in many cases, the actual degree subject matter does not matter. 


It is the personal habits employers believe a college graduate possesses that matter. 


Study

Key Findings

How it Connects to "Signaling"

Spence (1973), "Job Market Signaling" 

Seminal work in economics that introduced the signaling theory as a concept to explain labor market outcomes. It posits that education is a signal of a worker's innate ability to employers.

The model shows that in a world of imperfect information, high-ability workers invest in education to signal their productivity to employers and distinguish themselves from low-ability workers. The degree itself is the signal, not the knowledge it contains.

Tyler, Murnane, and Willett (2000)

Explored the "sheepskin effect," which is the wage premium associated with completing a degree, compared to just having a similar number of years of schooling.

Found that there is a significant jump in earnings for those who complete their degree (the "sheepskin") versus those who drop out with similar total years of schooling. This suggests that the signal of completion is more valuable than the simple accumulation of credits or knowledge.

Bingley, et al. (2015)

Using a panel of Danish twins, researchers found that the wage return to education decreases when controlling for unobserved factors like "ability," suggesting these factors are highly correlated with schooling.

By comparing identical twins with different educational attainments, the study attempts to isolate the effect of education from genetic and shared environmental factors (which are strong proxies for unobservable skills). The finding that the return to schooling is smaller when accounting for these factors supports the idea that the degree is partly a signal for pre-existing ability.

Arteaga (2018)

Found that a reform at a Colombian university that reduced the "human capital" content of a degree (i.e., less specific knowledge) had a significant negative impact on the graduates' earnings, especially for those from lower-reputation institutions.

This study shows a blend of both human capital and signaling theories. It suggests that while the degree serves as a general signal of quality, the specific skills gained (human capital) are also important. The "signaling" effect is especially crucial for graduates of less prestigious schools, who rely more on their degree to gain access to higher-paying firms and better job matches.


That noted, lots of well-paying jobs do not require such signaling mechanisms.


Sunday, September 14, 2025

If You Want to Make the World a Better Place, Take a Look at Yourself and Make the Change

Michael Jackson: "I'm starting with the man in the mirror: I'm asking him to change his ways. And no message could have been any clearer:

If you wanna make the world a better place
Take a look at yourself and then make a change.

We have met the enemy. It is us. Each of us. 

Even if AI Creates Abundance, Does it End Zero-Sum Economics?

AI's ability to reduce the cost of intelligence nearly to zero might be thought  to fundamentally challenge zero-sum economic principles (what one entity gains another loses) in many domains, including content. 


But it will probably be a complex matter, as has been the case for the way internet-fueled abundance affected the economics of content markets where scarcity has been the rule. 


Consider the impact on content. The internet fundamentally transformed content economics by collapsing the barriers that once made content creation and distribution expensive and exclusive. 


Traditional media businesses built their models around scarcity: limited time, space and distribution real estate. The internet changed all that, enabling much lower cost content creation and distribution. 


The key point is that scarcity-based content business models became vulnerable when the internet removed the underlying scarcity. 


But new forms of value creation emerged around curation, community, convenience, and trust. So sources of value shifted, in many cases. But abundance has not actually eroded zero-sum economics, just reshaped it. 


Consider attention, which is genuinely finite and zero-sum. Each minute spent watching YouTube is a minute not spent reading newspapers or watching television. Traditional media's loss of audience attention directly translates to digital platforms' gain. 


This explains why established media companies have struggled so intensely with the digital transition. They're fighting for shares of a fixed attention economy.


However, other aspects of digital markets can be positive-sum. The internet enabled entirely new forms of value creation that didn't exist before. Wikipedia didn't just redistribute encyclopedia sales to volunteers - it created an entirely new model of collaborative knowledge. 


It isn’t yet clear where AI might actually break the zero-sum model, as AI doesn't fully escape fixed-resource constraints. 


Zero-sum games will persist in many areas, since not all resources become abundant, and might well persist for other reasons, such as a shift in scarcity value to attention. 


In many cases, physical limits will remain, for example. There are only going to be so many National Football League regular-season games. There will only be one SuperBowl. 


So paradoxically, perhaps, the era of content abundance might not eliminate zero-sum economics as much as one would think. 


Where all major contestants in an industry (think law, marketing, manufacturing, transportation or finance, for example) adopt AI, there is no net economic advantage. Rivalry continues, and no firm gains a sustainable competitive advantage. 


Content abundance does not mean an end to strong zero-sum pressures in television, radio, movies or music. Popularity still will be quite unevenly distributed. 


In the professional content businesses (music, film, video, books, magazines, newspapers, radio) the issue is “what is scarce?” Traditionally, we might argue that content creation is expensive, and therefore scarce. 


In the era of digital content, it is human or audience attention that is finite, and therefore scarce, whereas the supply of content approaches infinity. That might continue to be true even if AI enables new content formats that gain favor with consumers. They will simply consume less content in other formats. 


Traditionally, content markets have had zero-sum elements because producing high-quality content required scarce resources like time, skill, and capital. 


AI, as did digital media and the internet before it, disrupts scarcity value by making content generation cheap, fast, and scalable.


The “oversupply” of content effectively collapses the scarcity that once gave content its intrinsic value. As the marginal cost of creating content approaches zero, the content itself becomes a commodity.


So a creator's success is no longer determined by their ability to create, but by their ability to get noticed i


The value proposition shifts from "what can I create?" to "what can I get people to pay attention to?"


That is the attraction of live sports content, for example. 


The point is that AI support for abundant content creation does not necessarily mean an end to zero-sum economic effects in media industries. Consumers have only so much time available to consume content.


Saturday, September 13, 2025

Who Knew GPS Spoofing was a Thing for Uber and Lyft?

Who even knew GPS spoofing was a thing? Uber and Lyft, for starters, as their business models hinge on accurate location information to connect riders and drivers or food and product buyers with sellers. But the ride sharing companies know some drivers manipulate their location data for personal financial gain, to the detriment of other honest drivers, passengers and the ride sharing companies. 


It is difficult to estimate how extensive such spoofing might be, although in likely rare cases, the financial damage can run to scores of millions of dollars.  


But GPS feeds can be disrupted for any number of reasons, such as a vehicle entering a tunnel. But there are other ways for GPS location data to be inaccurate in urban areas with lots of tall buildings or tree cover. The algorithms ride sharing companies use to detect such anomalies mean some honest drivers will occasionally be de-platformed by mistake. 


Some Uber and Lyft drivers manipulate their GPS location through "GPS spoofing" to increase their earnings, as the apps use location to determine trip requests, fares, and bonuses, which gives drivers an incentive to try and "game" the system. 


In some cases, a coordinated group of drivers in a specific area, such as an airport queue, will all go offline simultaneously, causing a sudden drop in the number of available drivers, triggering a "surge" in fares for that location.


In a similar way, cheaters can use GPS spoofing to place themselves virtually in the airport queue, even when they are miles away.


In other cases, drivers might use GPS spoofing or multiple accounts to exploit incentive programs. A driver could create multiple fake passenger accounts on the same device and use GPS spoofers to create fraudulent trips to meet quotas and collect bonuses.


There are a range of ways to do such things. Drivers can use "fake GPS" applications, which does not require technology skill. Others might use “rooted devices” with access to operating systems, to use more sophisticated spoofing methods that are harder for apps to detect. 


Some techniques use multiple phones. Scammers might use one phone with a spoofed location to get into an airport queue virtually, then use a second phone to accept and complete trips in their actual location.


And while the potential revenue might not always be worth the investment, dishonest drivers can GPS signal generators that transmit fake GPS signals.


As always is the case, virtually any technology can be used for good or evil; honest purposes or dishonest.


On the Use and Misuse of Principles, Theorems and Concepts

When financial commentators compile lists of "potential black swans," they misunderstand the concept. As explained by Taleb Nasim ...