Part 1: Pivotal Technological Trends Shaping 2035
- AGI’s Critical Path: Grounding in the Physical World
Artificial General Intelligence (AGI), the most transformative force of the decade, will rely on real-world interaction to overcome core bottlenecks. By connecting physical entities with their environments in real time, AGI will integrate perception, cognition, decision-making, and action—enabling intelligent agents to learn like humans, adapt to complex scenarios, and solve multifaceted tasks. Progress will focus on three pillars: accumulating multimodal data, refining core capabilities, and advancing cognitive principles.
- AI: From Execution Tools to Strategic Partners
Intelligent agents will drive industrial paradigm shifts, growing into a $10 trillion industry by 2035. In the short term, they will boost operational efficiency like customer service, sales, and office assistants (forming a $100 billion sector). Mid-term, they will revolutionize production—e.g., accelerating AI-driven drug R&D and professional consulting—to cut costs and create a $1 trillion industry. Long-term, they will reshape products: AI PCs will redefine offices, and embodied robots will become personal assistants, scaling to a $10 trillion market.
- Software Reinvented: Human-Machine Collaborative Development
Large models and intelligent agents are restructuring software development beyond tool upgrades. Users will generate “malleable software” via natural language (replacing basic app development), while professional fields (e.g., operating systems, industrial control) will integrate AI into toolchains to boost efficiency. The core lies in balancing “intelligence and governance”: AI handles execution and automation, while humans oversee design, value judgment, and oversight—avoiding full “AI replacement.”
- Immersive Interaction: Beyond Sight and Sound
Multimodal technology will drive 3D, intuitive human-machine interaction. Short-term, micro-gesture tech will gain traction; long-term, brain-computer interfaces will merge into multimodal frameworks, enabling “intuitive collaboration.” Future devices (e.g., gesture-sensing rings, ultra-light 3D glasses) will add touch, smell, and taste to digital experiences—creating true full-sensory immersion.
- Embodied Intelligence: Unlocking Trillion-Dollar Sectors
Three industries will lead:
Autonomous Driving: L4-level trials launch by 2027, scale in key scenarios by 2030, and dominate most use cases by 2035 (with L5 commercial pilots).
Intelligent Robots: Mass production will push household robot prices below $10,000 by 2035, ending cost barriers and triggering explosive growth.
Low-Altitude Economy: High-energy-density solid-state batteries and hybrid power (solar, hydrogen, electricity) will solve flight-duration limits, making private urban aircraft a reality.
- Beyond Von Neumann: New Computing for AI Demand
Traditional Von Neumann architecture (separating memory and computation) faces energy efficiency limits—exacerbated by slowing Moore’s Law. The next decade will see a “quantitative to qualitative” shift: innovations in materials, structures, and paradigms (e.g., quantum computing, edge computing) will build a “post-Moore” model. This will deliver efficient, low-carbon computing for AI, digital economies, and scientific research—easing global energy pressure.
- Intelligent Data: From Storage to Activation
By 2035, “warm data” (frequently accessed) will make up 70% of total data, replacing the traditional three-tier storage structure with a 3:7 “warm-cold” split. Data value will no longer depend on scale or format but on dynamic activation, scenario-based supply, and semantic storage—turning data into “new fuel” for civilizational progress.
- Token-Powered Energy: AI as the “Nervous System”
Global energy infrastructure will become a core constraint for AI—requiring token-based management. AI will enable tokens to embed programmable human intent, giving every energy network unit “perception-decision-action” capabilities. By 2035, wind/solar will replace fossil fuels as the main power source; thermal storage capacity will hit 500GW (with 80% efficiency); and hydrogen will form a $1.5 trillion market (for heavy transport and industrial fuel).
Part 2: Transforming Daily Life & Industry
Healthcare: Preventive “Computational Health”
Sensors and wearables will predict personal health in real time, with AI preventing 80% of chronic diseases to extend healthy lifespans. Cross-modal AI will integrate medical imaging, genomics, and electronic records to personalize treatments; virtual clinical trials will speed up drug launches and match patients to optimal therapies. Healthcare will shift from “treating illness” to a core social function—prioritizing quality of life over lifespan extension.
Education: Human-Machine Co-Learning
By 2035, AI will deeply merge with education to form “co-teaching, co-education, co-learning” models. Over 1 billion students will use intelligent learning assistants daily; 50 million digital twin teachers will support classes; and 80% of smart classrooms will upgrade to “intelligent twin” spaces. This will narrow educational gaps, optimize resources, and elevate global learning quality.
Transportation: The “Third Space” on the Move
Private car use will drop 15% by 2035. Mobility as a Service (MaaS) platforms will cover 80% of EU core cities, boosting commuting efficiency by 15–30%. Robotaxis (via seamless scheduling) and air taxis will turn travel into customizable, enjoyable experiences—redefining how people perceive time and space.
Manufacturing: Intelligent, Service-Driven Production
ICT will reshape R&D, production, and supply chains: Quantum computing + generative AI will enable “thinking R&D” (e.g., AI discovering new superconducting materials); embodied agents (via end-edge-cloud collaboration) will automate 90% of tasks (e.g., robotic arms switching processes in seconds); humans will act as “global orchestrators” using mixed-reality interfaces. Supply chains will form a seamless sea-land-air network—cutting forecasting errors and logistics costs.
Part 3: Coexisting with AI—Sustainability & Governance
Key Challenges
Technological Imbalance: AI benefits may concentrate in a few countries/companies, widening the “AI powerhouse vs. weak nation” divide and social inequality.
Ethical & Value Gaps: AI decision-making lacks transparency; ensuring alignment with human values becomes critical as autonomy grows.
Security Risks: Interconnected AI systems face heightened cyberattacks, with AI itself expanding attack surfaces.
Solutions
Ethics & Security First: Embed “AI for Good” into values—ensuring AI is interpretable, traceable, and accountable. Build security frameworks for ICT infrastructure, single agents, and multi-agent collaboration (e.g., auditable agent actions, encrypted data sharing).
Inclusive & Sustainable AI: Treat AI as “intelligent world infrastructure”—use it to empower education/healthcare and bridge digital divides. Reduce AI’s carbon footprint via energy-efficient algorithms/hardware and renewable energy; govern to cut biases (gender, race, language) in deployment.
By 2035, this synergy of technology and governance will turn the “intelligent world” into a sustainable, inclusive reality—redefining how humans live, work, and collaborate.
Source: Global Trade Hub
About APE
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