How do we go from Breakthrough to Broad Impact with AI?
Our world is being rapidly reshaped by Artificial Intelligence (AI), a technological transformation that follows a predictable arc. This arc moves through three key phases: an aspirational stage of theoretical ideas, followed by innovation breakthroughs and an exploitative phase where power and access are concentrated, leading finally to a crucial democratization phase, where benefits are broadly shared and development is ethical. AI is largely in the exploitive phase and it is up to us when and how we advance towards democratization. Looking back at 2025 first half we are not moving fast enough.
This is a conceptual framework illustrating stages of adoption and societal impact inspired by Technological Revolutions and Techno-Economic Paradigms by Carlota Perez1: Perez describes long cycles of technological revolutions, often characterized by an "installation period" where new technologies emerge followed by financial speculation, often leading to bubbles, followed by a "deployment period" where the technology becomes widely adopted and drives real economic growth, ultimately resulting in a new way of life.
Consider the historical journey of another transformational general purpose technology electricity: Its aspirational beginnings (late 1700s - early 1800s, ~50-70 years) saw scientific curiosity. The exploitative phase (late 1800s - early 1900s, ~30-40 years) emerged with commercial power plants and monopolies, limiting access. Finally, democratization (mid-1900s onwards, ~40-50 years) made it a universal utility through initiatives like the Rural Electrification Act23456.
Similarly, the automobile followed this path. Its aspirational period (late 1800s - early 1900s, ~15-20 years) featured experimental, expensive vehicles7. The exploitative phase (1900s - 1930s, ~20-30 years) took off with mass production and centralized manufacturing, raising concerns about urban impact. Democratization then accelerated (mid-1900s onwards, ~30-40 years) as continuous improvements and infrastructure made cars affordable and ubiquitous, fundamentally reshaping society 89.
AI's aspirational phase, marked by its formal birth in the 1950s (e.g., the 1956 Dartmouth Conference where "Artificial Intelligence" was coined) and initial theoretical pursuits, spanned approximately 50 years until the early 2000s1011. The exploitative phase then gained significant momentum from the early 2000s with the rise of machine learning and big data, dramatically accelerating with the deep learning revolution around 20121213. We are currently ~20-25 years into this exploitative phase, where AI's immense power is heavily concentrated among a few tech giants, leading to pressing concerns about data exploitation, algorithmic bias, resource centralization, knowledge monopolization, and job displacement anxieties14.
There is a prevailing belief that innovation automatically leads to democratization; however, history proves that it can take decades and focused work to get there. Moreover, even recent breakthroughs like the internet have shown that broad technological adoption does not inherently guarantee equitable prosperity15.
Given AI's immense transformative potential is believed to be far exceeding electricity, there is no clear precedent for how long it will take us to move beyond the exploitation phase. The key is that it is not a given that democratization of AI will happen at all unless we choose that path.
Looking back at 2025 first half, innovation and investment is where we made the most progress, however I’d argue that the emerging areas - energy use in data centers, workforce transformation and agent interplay, copyright and IP laws for AI Era, responsible AI practice, and education and learning in the AI era, is where we need a lot more attention and progress in the second half if we want any realistic shot at advancing towards the democratization phase.
2025 First Half Significant Progress:
We saw a booming AI landscape driven by significant innovation and investment:
Advanced AI Models & Capabilities: The frontier continues to expand with next-generation models like OpenAI's GPT-5 (expected mid-to-late 2025), Apple Intelligence, Anthropic's Claude 4 (Opus 4 and Sonnet 4), and Google Gemini, pushing boundaries in multimodal AI, advanced reasoning, and on-device capabilities, all hinting at a future of more sophisticated, integrated AI systems. These advancements in multimodal AI are especially notable, enhancing AI's ability to understand and generate diverse content.
DeepSeek's "Bigger is Better" Disruption: Intriguingly, DeepSeek is challenging the notion that larger AI models are always superior. Their rise suggests that efficiency, novel architectures, or specialized approaches can offer compelling alternatives, potentially enabling smaller players to compete without massive computational resources and aiding AI's democratization16.
Vibe Coding's Dual Impact: Vibe Coding has revolutionized software development, offering immense speed and flexibility for rapid code generation across languages. However, this blessing introduces the significant curse of testing AI-generated code infused with hallucination, demanding rigorous validation, a challenge already seen with tools like Microsoft's Copilot.