Key Emerging Technologies Shaping 2025

O'Reilly's Technology Trends for 2025 report, published today, is based on analyzed data from 2.8 million users on its learning platform, and giving insights into the most popular technology topics consumed - identifying emerging trends that could influence business decisions in the year ahead. The outlook for AI technologies is marked by dramatic growth in key areas. The percentages describe the growth in interest or usage of specific areas within the field: Prompt Engineering surged by 456%, AI Principles by 386%, and Generative AI by 289%. Additionally, the use of GitHub Copilot skyrocketed by 471%, highlighting a robust interest in tools that boost productivity. In terms of security, there was a significant 44% increase in interest in governance, risk, and compliance, accompanied by heightened attention to application security and the zero trust model. While traditional programming languages such as Python and Java experienced declines, data engineering skills witnessed a 29% increase, underscoring their essential role in powering AI applications. * * * Based on these numbers, the report analyses the Technology Trends for 2025 in the field of AI: I. Diverse AI Models: Unlike previous years when ChatGPT dominated, the field now includes a variety of strong contenders like Claude, Google’s Gemini, and Llama. These models have broadened the AI landscape and are each finding their niches within different user bases. II. Skill Growth: There has been a significant increase in interest and development in AI skills, notably in Machine Learning, Artificial Intelligence, Natural Language Processing, Generative AI, AI Principles, and Prompt Engineering. These skills are seeing varying levels of growth, with Prompt Engineering experiencing the most substantial surge. III. Shift in Platform Focus: Interest in GPT has declined as the industry moves away from platform-specific knowledge towards more generalized, foundational AI understanding. This shift reflects a maturation in the industry as developers seek capabilities that are applicable across various models. IV. Future Trends: The report anticipates potential disillusionment with AI, a phenomenon more sociological than technical, often due to overhyped expectations. Nonetheless, advancements continue, particularly in making AI interactions more intuitive and reducing the need for complex prompts. V. Development Tools and Data Engineering: Tools like LangChain and retrieval-augmented generation (RAG) are highlighted as key to building more sophisticated AI applications that can handle private data more securely and efficiently. Moreover, the importance of data engineering skills is underscored, supporting AI applications with robust data infrastructure. * * * The insights of the report can guide strategic planning, investment decisions, and curriculum development, and overall, offer a valuable snapshot of the technology landscape.

I always share a post each year talking about my predictions in technology. Here are my general technology trends for 2025. 🔺 Wider Adoption of Generative AI 🔹 Domain-specific models: We’ll see more specialized generators trained on targeted data (e.g., legal, medical, scientific) that can produce highly accurate and context-specific content. 🔹 Hybrid approaches: Enterprises will use generative AI alongside rule-based or traditional ML methods to achieve more reliable outcomes, minimizing hallucinations and biases. 🔺 Rise of Multimodal Systems 🔹 Unified AI experiences: Instead of siloed text, image, audio, and video models, we’ll see integrated systems that seamlessly handle multiple data types. This leads to richer applications, from next-gen customer support to advanced robotics. 🔹 Context-aware processing: AI will better understand real-world context, combining visual, audio, and textual cues to offer smarter responses and predictions. 🔺 Advances in Explainability and Trust 🔹 Regulatory frameworks: With stricter AI regulations on the horizon, model explainability and audibility will become core requirements, especially in finance, healthcare, and government. 🔹 AI “nutrition labels”: Standardized ways of conveying model biases, training datasets, and reliability will help build user trust and improve transparency. 🔺 Edge and On-Device AI 🔹 Lower latency, better privacy: More powerful AI models will run directly on phones, wearables, and IoT devices, reducing dependence on the cloud for tasks like speech recognition, image processing, and anomaly detection. 🔹 Specialized hardware: Continued investment in AI accelerators, TPUs, and neuromorphic chips will enable high-performance AI at the edge. 🔺 Human-AI Teaming and Augmented Decision-Making 🔹 Decision intelligence platforms: AI will shift from purely providing recommendations to working interactively with humans to explore complex problems—reducing cognitive load, but keeping humans in the loop. 🔹 Collaborative coding and content creation: AI co-pilots will expand from code generation and text drafting to more sophisticated collaboration, shaping design, research, and strategic planning. 🔺 Rapid Growth of AI as a Service (AIaaS) 🔹 “No-code” and “low-code” tools: Tools that allow non-technical users to deploy custom AI solutions will proliferate, lowering barriers to entry and accelerating adoption across industries. 🔺 Emphasis on Ethical and Responsible AI 🔹 Bias mitigation: Tools and techniques to detect and reduce bias will grow more advanced, spurred by public scrutiny and regulatory demands. 🔹 Standards for accountability: Organizations will create ethics boards and formal guidelines to ensure AI alignment with corporate values and social responsibility. 🔺 Quantum Computing Experiments 🔹 Hybrid quantum-classical models: Though still early-stage, breakthroughs in quantum hardware could lead to specialized quantum-assisted AI algorithms.

🚀 Gartner’s Top 10 Strategic Tech Trends for 2025 Gartner just dropped its top tech trends for 2025, spotlighting where the future is headed. If you want to stay ahead in a shifting landscape, here’s the scoop: 1. Agentic AI 🤖 Autonomous AI systems are here. They can make decisions on their own and take over complex tasks. This means better efficiency and fewer manual processes. There are multiple platforms including OpenAI’s SWARM multi-agent infrastructure making agent creation and products more accessible. If you think AI exploded, just wait.. 2. AI Governance Platforms 🧑⚖️ With AI taking on bigger roles, governance platforms are crucial. They manage compliance, ethics, and transparency, which are non-negotiable in today’s landscape. Look to Nebuly or Liminal in this space. 3. Disinformation Security 🔒 Misinformation is a real threat. Tools that detect and tackle false information are essential to keeping data and communication secure. I just posted yesterday about Google’s watermark, not perfect but closer. 4. Post-Quantum Cryptography 🧠 Quantum computing is advancing fast, putting current cryptographic methods at risk. Post-quantum cryptography is all about future-proofing sensitive data. 5. Ambient Intelligence 🌍 Low-cost sensors are being embedded into environments to collect data and automate processes. But privacy concerns come with the territory. 6. Energy-Efficient Computing 🌱 Sustainability matters. Energy-efficient hardware and software solutions cut down on IT’s carbon footprint and help businesses meet their green goals. 7. Hybrid Computing ⚙️ By blending traditional and emerging tech, hybrid models offer flexibility and performance to tackle complex tasks in dynamic environments. 8. Spatial Computing 🕶️ Augmented and virtual reality are merging the digital and physical worlds. This shift is reshaping experiences from remote collaboration to product interaction. 9. Polyfunctional Robots 🤖 Labor costs are rising. Versatile robots that can handle multiple tasks are the solution, especially in manufacturing and logistics. Tesla and others already experimenting and launching. 10. Neurological Enhancement 🧠💡 Brain-machine interfaces are no longer sci-fi. They’re making strides in education, safety, and performance enhancement. Impact on GTM For GTM leaders, these trends are key to driving growth. Agentic AI improves customer engagement and speeds up sales cycles. AI Governance builds trust through secure and ethical practices. Disinformation Security safeguards your brand’s credibility. Hybrid and Spatial Computing create new channels and ways to connect with customers. Neurological Enhancements elevate training and insights with smarter tools. I’ll add one of my own which is TRUST. The more the digital experience can be cloned or created by AI, the more in person events will come back full swing so people can trust the person in front of them. What do you think?

For security leaders navigating the rapid rise of AI in the enterprise, here are 5 key themes I see coming in 2025: 1. Compliance is coming for GenAI. The EU AI Act and other/existing/emerging U.S. data privacy regulations are set to bring major challenges for organizations. Expect audits, risk assessments, and new governance frameworks to be a hot talking point and a headache for a while. 2. Third-party risk is evolving. Many firms and employees are choosing to use outsourced GenAI applications vs building their own and I think that trend will only increase in 2025.  Building and maintaining custom GenAI implementations requires a huge investment in talent and maintenance that few will pursue.  As such, third party risk, which was never really fixed, is on the up once more. Sure, you can block ChatGPT and buy an enterprise subscription to Copilot, but are you really going to block Grammarly, Canva, DocuSign, or LinkedIn? 3. The rise of small language models for enterprise. Outside of the foundational models, a lot of interesting enterprise startups are building their own, custom, domain-specific models, trained on data sets for particular purposes that are not well addressed or too slow and expensive with the OpenAI’s of this world.  Expect to see more of this as they become mainstream in 2025, with huge benefits but further exacerbated third party risk. 4. Data is the new perimeter. With heavy funding flowing into the space, data security has been thrust into the limelight. I’d wager it will be a top 3 priority for CISOs and CIOs this year. 5. Agentic overload. You may be sick of seeing the word ”agentic”, but AI agents are gaining traction fast, offering real productivity boosts for overloaded enterprise teams in many functions. Given the competitive pressures to roll these out rapidly, I expect to see more than one high profile failure of automated agents taking decisions for a company that result in a major business failure. What did I miss?