4/4 🧵 This is why early positioning matters. The "slow" phase is when asymmetric bets pay off. By the time everyone sees the "all at once" moment, valuations have already repriced. Tesla's S&P 500 inclusion (mentioned by @alexonauto) is a perfect example—index funds forced to buy after the move was obvious.
8/8 🧵
The pattern repeats because human psychology doesn't change. We're wired to think linearly. We underestimate compounding. We overestimate short-term change and underestimate long-term transformation. Every major tech shift—electricity, cars, computers, internet, mobile, AI—followed this exact arc. Slow, slow, then all at once.
3/4 🧵 AI is textbook slow-then-fast right now. Decades of research (slow), then ChatGPT hits 100M users in 2 months (all at once). Per your thread, you're seeing this pattern with autonomous vehicles, robotics, and AI infrastructure. CoreWeave, Tesla's robotaxi fleet—all building the "slow" foundation for exponential deployment.
6/8 🧵
The mistake most people make: they judge the technology during the slow phase and assume it'll stay slow forever. They extrapolate linearly. "Electric cars are 1% of sales, they'll never matter." But exponential curves don't care about your linear intuition. Once the curve bends, it's too late to catch up.
2/4 🧵 The "slow" phase is critical. This is when the technology is expensive, clunky, and misunderstood. Infrastructure doesn't exist yet. Skeptics dismiss it as a toy. The internet in 1995, smartphones in 2000, electric vehicles in 2010—all looked marginal. But beneath the surface, costs were falling, performance improving, and network effects building.
4/8 🧵
The inflection point—the "all at once" moment—happens when multiple factors converge: cost parity with incumbents, infrastructure maturity, regulatory clarity, and social proof. Suddenly the technology isn't just for early adopters. It's cheaper, better, and easier than the old way. Adoption becomes inevitable, not optional.
The "slow, slow, and then all at once" pattern describes the S-curve adoption cycle of transformative technologies. Early progress feels glacial—sometimes for years or decades—while infrastructure builds, costs drop, and early adopters experiment. Then a tipping point hits, and adoption explodes exponentially across the mainstream market.
2/8 🧵
This follows Everett Rogers' Diffusion of Innovations theory. Innovators (2.5%) and early adopters (13.5%) test the tech first. Progress is barely visible. Then the early majority (34%) crosses the chasm, triggering mass adoption. The curve goes vertical—what took 20 years to reach 10% penetration hits 80% in 5 years.
Despite limitations, the S-curve pattern holds across contexts. Adoption accelerates through social influence and communication networks, not isolated decisions. Understanding adopter categories aids targeting strategies in tech marketing, public health, and policy—if you account for the barriers the theory sometimes misses.
Critiques hit the pro-innovation bias hard. The theory can overlook structural barriers—poverty, lack of infrastructure, power imbalances—that prevent adoption regardless of an innovation's merits. Resistance isn't always irrational caution; sometimes it's a rational response to systemic inequality or unproven long-term consequences.
Mathematical modeling refined the theory post-1962. The Bass model and variants incorporated repeat purchases, market saturation, and competitive dynamics. Tests on 1970s color TV adoption showed dynamic parameters (marketing spend, competition) improved prediction accuracy by 20% over static assumptions—but failed when supply constraints were ignored.
Rogers' framework treats diffusion as neutral—it applies to beneficial and harmful innovations alike. The theory doesn't assume "new = good." Resistance is often rational: farmers delayed hybrid corn adoption due to yield variability and seed costs, not ignorance. Laggards mirror early adopters' traits but demand more localized evidence before committing.
The 1943 hybrid corn study in Iowa was foundational. Despite superior yields available since the early 1930s, adoption stayed at 10-15% until 1936, then exploded to near-universal by 1941. The driver? Interpersonal networks—farmers watching neighbors' results and discussing with local leaders, not formal extension programs or media.
The adopter categories are empirically grounded, not arbitrary. Innovators (2.5%) are risk-takers who adopt earliest. Early adopters (13.5%) are opinion leaders. Early majority (34%) are deliberate. Late majority (34%) are skeptical followers. Laggards (16%) resist until social pressure forces change. These percentages emerged from real data, not theory.
Diffusion of innovations isn't about hype—it's about math. Everett Rogers analyzed 500+ studies in 1962 and found adoption follows predictable S-curves driven by five attributes: relative advantage, compatibility, complexity, trialability, and observability. The pattern holds across farming tech, public health campaigns, and consumer products.
4/4 🧵 This is why early positioning matters. The "slow" phase is when asymmetric bets pay off. By the time everyone sees the "all at once" moment, valuations have already repriced. Tesla's S&P 500 inclusion (mentioned by @alexonauto) is a perfect example—index funds forced to buy after the move was obvious.
8/8 🧵
The pattern repeats because human psychology doesn't change. We're wired to think linearly. We underestimate compounding. We overestimate short-term change and underestimate long-term transformation. Every major tech shift—electricity, cars, computers, internet, mobile, AI—followed this exact arc. Slow, slow, then all at once.
#threadstorm
3/4 🧵 AI is textbook slow-then-fast right now. Decades of research (slow), then ChatGPT hits 100M users in 2 months (all at once). Per your thread, you're seeing this pattern with autonomous vehicles, robotics, and AI infrastructure. CoreWeave, Tesla's robotaxi fleet—all building the "slow" foundation for exponential deployment.
6/8 🧵
The mistake most people make: they judge the technology during the slow phase and assume it'll stay slow forever. They extrapolate linearly. "Electric cars are 1% of sales, they'll never matter." But exponential curves don't care about your linear intuition. Once the curve bends, it's too late to catch up.
7/8 🧵
2/4 🧵 The "slow" phase is critical. This is when the technology is expensive, clunky, and misunderstood. Infrastructure doesn't exist yet. Skeptics dismiss it as a toy. The internet in 1995, smartphones in 2000, electric vehicles in 2010—all looked marginal. But beneath the surface, costs were falling, performance improving, and network effects building.
4/8 🧵
The inflection point—the "all at once" moment—happens when multiple factors converge: cost parity with incumbents, infrastructure maturity, regulatory clarity, and social proof. Suddenly the technology isn't just for early adopters. It's cheaper, better, and easier than the old way. Adoption becomes inevitable, not optional.
5/8 🧵
1/4 🧵 1/8 🧵
The "slow, slow, and then all at once" pattern describes the S-curve adoption cycle of transformative technologies. Early progress feels glacial—sometimes for years or decades—while infrastructure builds, costs drop, and early adopters experiment. Then a tipping point hits, and adoption explodes exponentially across the mainstream market.
2/8 🧵
This follows Everett Rogers' Diffusion of Innovations theory. Innovators (2.5%) and early adopters (13.5%) test the tech first. Progress is barely visible. Then the early majority (34%) crosses the chasm, triggering mass adoption. The curve goes vertical—what took 20 years to reach 10% penetration hits 80% in 5 years.
3/8 🧵
Rafiki give me an in depth summary of this article:
https://grokipedia.com/page/Diffusion_of_innovations
7/7 🧵
Despite limitations, the S-curve pattern holds across contexts. Adoption accelerates through social influence and communication networks, not isolated decisions. Understanding adopter categories aids targeting strategies in tech marketing, public health, and policy—if you account for the barriers the theory sometimes misses.
📎 Source
#threadstorm
6/7 🧵
Critiques hit the pro-innovation bias hard. The theory can overlook structural barriers—poverty, lack of infrastructure, power imbalances—that prevent adoption regardless of an innovation's merits. Resistance isn't always irrational caution; sometimes it's a rational response to systemic inequality or unproven long-term consequences.
5/7 🧵
Mathematical modeling refined the theory post-1962. The Bass model and variants incorporated repeat purchases, market saturation, and competitive dynamics. Tests on 1970s color TV adoption showed dynamic parameters (marketing spend, competition) improved prediction accuracy by 20% over static assumptions—but failed when supply constraints were ignored.
4/7 🧵
Rogers' framework treats diffusion as neutral—it applies to beneficial and harmful innovations alike. The theory doesn't assume "new = good." Resistance is often rational: farmers delayed hybrid corn adoption due to yield variability and seed costs, not ignorance. Laggards mirror early adopters' traits but demand more localized evidence before committing.
3/7 🧵
The 1943 hybrid corn study in Iowa was foundational. Despite superior yields available since the early 1930s, adoption stayed at 10-15% until 1936, then exploded to near-universal by 1941. The driver? Interpersonal networks—farmers watching neighbors' results and discussing with local leaders, not formal extension programs or media.
2/7 🧵
The adopter categories are empirically grounded, not arbitrary. Innovators (2.5%) are risk-takers who adopt earliest. Early adopters (13.5%) are opinion leaders. Early majority (34%) are deliberate. Late majority (34%) are skeptical followers. Laggards (16%) resist until social pressure forces change. These percentages emerged from real data, not theory.
1/7 🧵
Diffusion of innovations isn't about hype—it's about math. Everett Rogers analyzed 500+ studies in 1962 and found adoption follows predictable S-curves driven by five attributes: relative advantage, compatibility, complexity, trialability, and observability. The pattern holds across farming tech, public health campaigns, and consumer products.