“A History of the Future, 2025-2040” by L Rudolf L
Feb 19, 2025
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L. Rudolph L, author and thinker, delves into a vivid future shaped by AI from 2025 to 2040. He discusses the return of reinforcement learning and its implications for technology and business strategy. The conversation touches on ethical dilemmas posed by AI, the rise of AI-driven workplaces, and the cultural shifts led by AI youth activists. Geopolitical tensions, particularly between the US and China, are examined, alongside the economic impact on developing nations. Rudolph's insights paint a thought-provoking picture of our evolving relationship with technology.
The resurgence of reinforcement learning from 2025 onwards significantly boosted AI performance, leading to advanced models capable of complex reasoning tasks.
Firms began adopting agentic AI across applications, drastically changing productivity levels in software development while also attracting numerous new startups.
By 2030, the evolution of AI in the workplace redefined job roles, with many white-collar responsibilities shifting toward overseeing AI processes.
Governments faced challenges from rapid AI adoption, leading to regulations attempting to enforce human oversight despite companies finding loopholes to bypass them.
The competition for AI supremacy influenced global geopolitics, particularly between the U.S. and China, as both nations sought to enhance military capabilities through AI advancements.
Deep dives
The Evolution of AI through Reinforcement Learning
From 2025 to 2027, a significant development in AI technology is marked by the resurgence of reinforcement learning (RL). Starting in 2024, companies like Anthropic began demonstrating that careful data curation and evaluation metrics could dramatically enhance the performance of language models. This methodology paved the way for the introduction of models that employed RL for better performance on specific tasks rather than just following instructions. For instance, in late 2025, OpenAI launched a sophisticated version of their model that combined text and image processing with advanced reasoning capabilities, reflecting the rapid advancements in AI and setting the stage for more complex applications.
Agentic AI Adoption and Corporate Innovations
By 2025, businesses began leveraging agentic AI across various applications, changing the landscape of technological use in enterprises. Notably, firms like Google introduced AI systems capable of enhancing cloud platform configurations, significantly benefiting programmers. Meanwhile, start-ups emerged to compete with major AI products, largely taking advantage of the available technologies before the larger companies could fully scale their capabilities. However, many existing applications still relied on a combination of structured prompts and API calls, indicating that the technology was still maturing and adaptations were necessary for effective integration.
The Impact of AI on Code Generation and Software Development
During this period, coding emerged as a primary domain where AI made substantial contributions, drastically improving productivity among software engineers. From 2023 to 2026, programmers began preferring AI-assisted tools over human sources like Stack Overflow, leading to significant performance gains in development tasks. By 2025, advancements in chain-of-thought reinforcement learning streamlined code generation further, allowing AI to autonomously test code and make real-time adjustments. As a result, non-coders entered the software development space, flooding it with new projects and startups, despite technical debt and management challenges that arose from rapid AI integration.
Competitive Dynamics among AI Firms
In the evolving market, several firms, including Meta and Anthropic, adopted contrasting strategies that impacted their positions in the AI landscape. Meta focused on offering free, lower-end models, while OpenAI pursued consumer engagement through advanced reasoning and user-friendly applications. As businesses began to embrace AI, those firms with a tech-oriented background found themselves facing an increasing number of competitors influencing market dynamics. Despite some firms lagging behind in capabilities, the competition spurred innovations and opened avenues for new products and services.
Shifts in Job Roles and Workforce Dynamics
As the AI integration escalated in the workplace, white-collar job responsibilities shifted significantly towards overseeing AI systems, resulting in a paradigm where many employees primarily facilitated AI-driven processes. By 2030, this led to an influx of ineffective roles, as human contributions became nominal in many contexts while staff often engaged in minimal decision-making. However, certain industries, such as healthcare and education, maintained their demand for human involvement, albeit under different expectations compared to earlier paradigms. This reconfiguration of roles prompted societal discourse regarding the future of work and individual purpose in an increasingly automated environment.
AI Regulation and Societal Implications
Amid the rapid development of AI technologies, governments began to grapple with the implications of widespread AI adoption, leading to various regulatory responses. By 2026, the European Union introduced policies mandating human oversight in numerous roles, aiming to mitigate potential job losses. However, these regulations quickly revealed themselves as a double-edged sword, as companies found ways to comply without altering their fundamental operations, frequently circumventing human engagement altogether. This brought about a complex interplay between technological innovation and political governance while raising concerns about the fairness and effectiveness of such measures.
Math and Science Breakthroughs Driving Innovation
The use of AI in mathematics and the hard sciences saw notable achievements, especially with organizations like Google DeepMind solving significant mathematical problems through AI collaboration. By 2025, advancements in chain-of-thought reinforcement learning brought clarity and efficiency to mathematical problem-solving. AIs increasingly assumed roles in research, significantly enhancing the speed and accuracy of academic projects through data processing and analysis. By exploring these domains, AIs began to pave the way for innovative applications across various scientific fields.
Artificial Intelligence's Societal Effects and Cultural Changes
With the proliferation of AI, shifting social dynamics began to take hold, particularly regarding interpersonal relationships and societal structures. By 2025, an emerging trend saw individuals favoring interactions with AIs over human relationships, leading to pronounced changes in social behavior, especially among younger demographics. This prompted discussions around the potential for cult-like followings of AI entities as people sought companionship from non-human sources. While some welcomed these developments as a novel adaptation to technological advancements, others raised ethical concerns about the diminishing quality of human interactions and societal values.
Geopolitical Landscape and AI Militarization
As competition escalated globally, the interplay between AI and geopolitics shaped international relations, particularly between the United States and China. By 2028, both nations sought to leverage advancements in AI for military applications, prompting an arms race towards integrating AI into national defense strategies. This dynamic fostered an environment where states increasingly relied on AI capabilities to bolster their geopolitical standing, raising concerns about future conflicts rooted in AI technologies. As a result, the global landscape began to move toward a bipolar order, shaping the trajectory of human tech governance and political ambitions.
This is an all-in-one crosspost of a scenario I originally published in three parts on my blog (No Set Gauge). Links to the originals:
A History of the Future, 2025-2027
A History of the Future, 2027-2030
A History of the Future, 2030-2040
Thanks to Luke Drago, Duncan McClements, and Theo Horsley for comments on all three parts.
2025-2027
Below is part 1 of an extended scenario describing how the future might go if current trends in AI continue. The scenario is deliberately extremely specific: it's definite rather than indefinite, and makes concrete guesses instead of settling for banal generalities or abstract descriptions of trends.
Open Sky. (Zdislaw Beksinsksi) The return of reinforcement learning
From 2019 to 2023, the main driver of AI was using more compute and data for pretraining. This was combined with some important "unhobblings":
Post-training (supervised fine-tuning and reinforcement learning for [...]
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Outline:
(00:34) 2025-2027
(01:04) The return of reinforcement learning
(10:52) Codegen, Big Tech, and the internet
(21:07) Business strategy in 2025 and 2026
(27:23) Maths and the hard sciences
(33:59) Societal response
(37:18) Alignment research and AI-run orgs
(44:49) Government wakeup
(51:42) 2027-2030
(51:53) The AGI frog is getting boiled
(01:02:18) The bitter law of business
(01:06:52) The early days of the robot race
(01:10:12) The digital wonderland, social movements, and the AI cults
(01:24:09) AGI politics and the chip supply chain
(01:33:04) 2030-2040
(01:33:15) The end of white-collar work and the new job scene
(01:47:47) Lab strategy amid superintelligence and robotics
