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Tuesday, November 25, 2025

1995 and 2025: What is Different for Software Startups?

So some of us were around in 1995 to 2000 and working with startups, writing business plans and so forth. Compared to 2025, the software startup process looks really different: faster, cheaper, but maybe also with different metrics and processes for validating whether a market for the proposed product exists. 


And that matters, since some research suggests 35 percent of startups fail because customers did not actually want the product or solution. That might sound crazy, but it happens. I’ve lived it. 

 

source: Parallel 


So market validation is the process of testing whether real people in your target audience are willing to engage with or pay for your product idea, before you build it. These days, standard methods include data from landing pages, interviews, prototypes or paid pilots. If nobody signs up or pays, you’ve learned something valuable without wasting months of work.


Proof of concept metrics are different now, for example. 


Proof of Concept

1995

2025

What is "Proof"?

The team and the idea: A founding team with a strong pedigree and a convincing narrative about a revolutionary product.

Product-market fit: Concrete evidence that customers are using, loving, and paying for the solution without excessive sales or marketing effort.

Early Traction

Non-monetary milestones: Building the alpha/beta, acquiring a large number of free users, or securing a strategic partnership.

Monetary and retention milestones: $X in monthly recurring revenue, low logo/revenue churn rate, high user engagement (Daily Active Users), and successful paid pilots with defined use-cases.

The Ask

"We need money to build the product and launch the marketing campaign."

"We have proven we can acquire and retain customers efficiently. We need money to scale the proven go-to-market engine."

Defensibility

Proprietary tech: often a new database, networking protocol, or a patent-pending system.

Data and network effects: proprietary data sets, AI models, high switching costs, or platform-driven network effects (where the product gets better as more people use it).



Basically, market validation or proof of concept is the process of turning assumptions into evidence. 


Validation was more time-consuming, expensive and required larger teams back in 1995. 


Back then, during the dot-com bubble, money was not the problem, as odd as that seemed to me at the time. 


Back then, building software required $5 million and 20 people. Remember, this was before cloud computing and Amazon Web Services. We had to buy all our own “compute” platform before we could start. 


Faster, leaner compute also means founders can demonstrate traction much earlier than was possible in 1995. Prototypes can be created and launched much faster. 


Feature

1995

2025

Primary Method

Business plan and total addressable market: Focus on a compelling vision, "build it and they will come," and a massive Total Addressable Market (TAM) estimate.

Lean startup and minimum viable product: Focus on iterative, real-world customer experiments. 

Cost of Validation

High: Required significant capital for dedicated servers, software licenses, in-house development, and formal market research.

Low (Near-Zero): Can use no-code/low-code tools (Webflow, Figma), cloud services (AWS/Azure/GCP), and affordable digital advertising.

Proof of Interest

Anecdotal: Focus groups, customer surveys, or Letters of Intent from large enterprises.

Behavioral and quantified: landing page conversion rate (waitlist sign-ups), pre-orders/paid pilots, and discovery interviews with 10–12 Ideal Customer Profile prospects.

Technology Focus

The product: The sheer ability to build complex, custom software was a barrier to entry and a selling point.

The value prop and distribution: The focus is on solving a specific, painful problem and proving a repeatable go-to-market (GTM) strategy.



Then it took 18 months to 24 months before founders had anything to show investors or customers.


Therefore founders needed a compelling enough story to raise millions based on a business plan alone. More to the point, they needed a compelling PowerPoint presentation. 


So everyone spent lots of time creating revenue projections (always up and to the right). And lots of us created some version of “Huge market of X size and we will get one percent.” 


Management team credentials were important, as everyone knew the hockey stick projections were illusory.


So successful founders were the ones who could raise money, based on past experience in related markets or simply because they had been successful before. It remains unclear if the best ideas won out. It is more correct to say the best-funded ideas often won. 


In 2025, with access to AWS, Google Cloud and Azure, everything is faster and cheaper. 


No-code tools and AI-assisted software development reduce validation time because creators can ship initial products fast and start learning from users in months, not quarters. 


“Traction” was important in 1995 as well, but that was largely “eyeballs” rather than revenue, in the consumer products space. Hence the focus on the audience: monthly active users, for example. 


Metric

1995

2025

Revenue Focus

Gross revenue/gross merchandise value: Total sales volume was the key (e-commerce transactions).

Recurring revenue: monthly/annual recurring revenue (MRR/ARR). High-quality, predictable revenue streams are prioritized.

Growth Metrics

Eyeball/User Count: Total users, website traffic, or simple month-over-month growth rate.

Net revenue retention (NRR): Measures revenue growth from existing customers (expansion minus churn). A >100% NRR is highly sought after.

Customer Value

N/A (or simple margin): Little focus on long-term value, as the business model was often ad- or transaction-based.

Unit economics: The relationship between Customer Lifetime Value (CLTV) and Customer Acquisition Cost (CAC). VCs typically want a CLTV:CAC ratio of 3:1 or better.

Financial Health

Cash on Hand: Focus on how long the runway lasted until the next raise (often called "burn rate").

Burn multiple: A measure of how much cash a company burns to generate $1 of new ARR. VCs look for low multiples (often <2x for B2B/SaaS) to prove efficient growth.


We might debate the relative importance of the various inputs or metrics. Founding teams and experience still seem to matter. But it might also arguably be the case that all the new tools could allow some startups to gain traction even in the absence of “founding team” experience. 


If a founder or founding team can go from concept to production-grade system with real paying customers in days to weeks to months, everything downstream can change as well. 


Success metrics could change from the number of validated users to actual revenue already being earned. 


When people can tell an AI engine what it is they want, and the AI can build the software, even fairly complex solutions, technical expertise in software development, access to capital or high-level domain experience might be less critical. 


Deep understanding of a particular process, coupled with sophisticated AI software development tools accessible using a natural language query process, might be enough, in many cases, to enable startups founded by people without all the traditional screening advantages venture capitalists look for.


Friday, September 26, 2025

AI Impact Will Come Mostly from Consumer Products and Services, Not Enterprise

It is fair enough to raise questions about whether the coming investment in AI compute infrastructure is matched to new AI revenues that investment is expected to generate. 


“Two trillion dollars in annual revenue is what’s needed to fund computing power needed to meet anticipated AI demand by 2030,” according to researchers at Bain and Company. “However, even with AI-related savings, the world is still $800 billion short to keep pace with demand.”


Bain’s sixth annual Global Technology Report predicts that, by 2030, global incremental AI compute requirements could reach 200 gigawatts, with the United States accounting for half of the capability. 


So here’s the thinking: even if companies in the U.S. market shifted all of their on-premise information technology budgets to cloud and reinvested the savings from applying AI in sales, marketing, customer support, and research and development into capital spending on new data centers, the amount would still fall short of the revenue needed to fund the full investment, as AI’s compute demand grows at more than twice the rate of Moore’s Law, Bain argues. 


The return on investment arguably looks different if we look at AI impact on consumer products, though. 


PwC estimates that up to $9.1 trillion of the total global GDP gain from AI by 2030 will come from consumption-side effects (increased demand due to personalized, higher-quality products and services). 


In other words, productivity improvements are part of the story, but not the whole story. 


AI-Influenced Consumer Spending: A report by Cognizant and Oxford Economics projects that U.S. consumers who embrace AI could drive $4.4 trillion in AI-influenced consumer spending in the US alone by 2030.


The global consumer AI market size is projected to reach approximately $674.49 billion by 2030, growing at a CAGR of 28.3% (NextMSC forecast). 


Feature

Bain Argument (B2B/Enterprise Focus)

Consumer AI (B2C/Consumption Focus)

Primary Metric

Annual revenue needed to fund AI compute capital expenditure ($2T needed, $800B shortfall).

Increased consumer spending and consumption-side GDP boost (e.g., $4.4T influenced spending in the United States, $9.1T global GDP from consumption).

Key Conclusions

Supply-side funding shortfall to build the necessary data centers and computing power.

Demand-side explosion creating massive new market value and consumption.



Study Name

Date

Publisher(s)

Key Conclusion on Consumer Impact

Web Link

Sizing the Prize

Oct 2017

PwC

AI will boost global GDP by $15.7 trillion by 2030. Crucially, $9.1 trillion (58%) of this gain will come from consumption-side effects (increased consumer demand for personalized, higher-quality products and services).

https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf 

New Minds, New Markets

Jan 2025

Cognizant & Oxford Economics

Consumers who embrace AI could drive $4.4 trillion in AI-influenced consumer spending in the U.S. by 2030, accounting for 46% of total U.S. spending. AI will revolutionize the purchase journey (Learn, Buy, Use).

https://investors.cognizant.com/news-and-events/news/news-details/2025/Cognizant-Study-Shows-Consumers-Who-Embrace-AI-Could-Drive-4.4-Trillion-in-Spending-Over-Five-Years/default.aspx 

The economic potential of generative AI

June 2023

McKinsey Global Institute

Generative AI could add an equivalent of $400 billion to $660 billion annually to the retail and consumer packaged goods sectors across the 63 use cases analyzed globally.

McKinsey 

The State of Consumer AI

June 2025

Menlo Ventures

The consumer AI market has reached $12 billion in the 2.5 years since generative AI went public. The low conversion rate (3% paying for premium) indicates a massive monetization opportunity, especially for specialized AI tools and Voice AI.

https://menlovc.com/perspective/2025-the-state-of-consumer-ai/ 

AI's transformation of consumer industries

Apr 2025

World Economic Forum (WEF)

GenAI could yield an extra $1.2 trillion in economic value across seven geographies within consumer industries by 2038. Projected impacts include a 10−20% revenue uplift and a 60% reduction in content production costs.

https://www.weforum.org/stories/2025/04/ai-transformation-consumer-industries-wef-report/ 


The point is that we do not yet know the size of markets and benefits of AI, to evaluate against the cost of computing infrastructure to support AI use cases. But enterprise impact is likely the lesser of the drivers. Consumer products and services are where most of the returns are likely to happen. 


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


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