Current Engineering Approaches and Trends

Yesterday during our MIT meeting we spent a good part of the afternoon discussing the implications of AI’s scaling laws – the idea that making models larger, training on more data, and using more compute yields better performance, but only when grown together. This finding fueled our current race to build ever-larger models.However, the scale-everything approach that seemed to be the solution may now be reaching its limits.Exponential cost & need for power means that each small gain demands exponentially more compute and energy meaning that this path alone may become unsustainable. Another factor is the quality plateau where better perplexity doesn’t equal true understanding. Even as models get bigger and excel at benchmarks, they still hallucinate information and fail basic logic. Despite the hype, pure scaling hasn’t produced artificial general intelligence(AGI) yet – disproving the mantra that “scale is all you need”. Big models can display emergent skills, but crucial capabilities like commonsense reasoning remain absent until now. The next model may consume all high-quality text data by 2026–2032, and training the next giant might cost around $100B some sources say. So the future of AI will be truly defined by scale + innovation – combining big models with new strategies: -Hybrid systems: Combining large neural networks with other AI approaches (symbolic reasoning, external and private knowledge, etc.) to overcome the limits of pure scaling. -Architectural breakthroughs: New model designs (multimodal, modular, sparse, etc.) that get more out of fewer parameters making AI more efficient instead of just bigger. -New training paradigms: Models that learn continuously or interactively (via reinforcement learning, human feedback, etc.) instead of relying on one-off training runs. In the next 3–5 years, expect a shift from brute-force growth to more efficient methods. AI leaders will prioritize optimized models and smarter infrastructure over sheer scale looking for the opportunity to enable true AGI. #ai #artificialintelligence #digital

Stanford University researchers released a new AI report, partnering with the likes of Accenture, McKinsey & Company, OpenAI, and others, highlighting technical breakthroughs, trends, and market opportunities with large language models (LLMs).  Since the report is 500+ pages!!! (link in comments), sharing a handful of the insights below: 1. Rise of Multimodal AI: We're moving beyond text-only models. AI systems are becoming increasingly adept at handling diverse data types, including images, audio, and video, alongside text. This opens up possibilities for apps in areas like robotics, healthcare, and creative industries. Imagine AI systems that can understand and generate realistic 3D environments or diagnose diseases from medical scans. 2. AI for Scientific Discovery: AI is transforming scientific research. Models like GNoME are accelerating materials discovery, while others are tackling complex challenges in drug development. Expect AI to play a growing role in scientific breakthroughs, leading to new materials and more effective medicines. 3. AI and Robotics Synergy: The combination of AI and robotics is giving rise to a new generation of intelligent robots. Models like PaLM-E are enabling robots to understand and respond to complex commands, learn from their environment, and perform tasks with greater dexterity. Expect to see AI-powered robots playing a larger role in manufacturing, logistics, healthcare, and our homes. 4. AI for Personalized Experiences: AI is enabling hyper-personalization in areas like education, healthcare, and entertainment. Imagine educational platforms that adapt to your learning style, healthcare systems that provide personalized treatment plans, and entertainment experiences that cater to your unique preferences. 5. Democratization of AI: Open-source models (e.g., Llama 3 just released) and platforms like Hugging Face are empowering a wider range of developers and researchers to build and experiment with AI. This democratization of AI will foster greater innovation and lead to a more diverse range of applications.

Gartner's 2024 Top Strategic Tech Trends in #SoftwareEngineering just dropped! Based on analyzing client inquiries on software engineering, the top trends we've identified are: - Software Engineering Intelligence: Tools that measure developer productivity and developer experience are increasingly used, especially to measure to impact of generative AI on productivity and DevEx. - AI-Augmented Development: We project that by 2028, 75% of enterprise software engineers will use AI coding assistants, up from less than 10% a year ago. However, AI-augmented development goes beyond coding assistants, and now covers requirements analysis, testing, and refactoring. - Green Software Engineering: Green software engineering helps leaders to deliver mission-critical software in a sustainable way. This can include using more energy-efficient languages such as Rust, or an Internal Developer Portal which helps developers make energy-aware choices. - Platform Engineering: No surprise here, since this has been a top trend for a number of years now. But we hear our clients questioning the ROI of platform engineering more closely, as it moves beyond the peak of the Hype Cycle. - Cloud Development Environments: These provide remote, ready-to-use access to a hosted development environment. This decoupling of the development workspace from the physical workstation enables a low-friction, consistent developer experience and faster developer onboarding. Cloud Development Environments are a new entry in our Top Trends for Software Engineering for 2024, and it will be interesting to chart their progress. Congratulations to Joachim Herschmann, Manjunath (Manju) Bhat , Frank O'Connor, Arun Batchu, and Bill Blosen for leading this report. Gartner clients can access the full report here: https://lnkd.in/enkQvyas [Gartner subscription required]

🚀 McKinsey & Company Tech Trends 2025: What Business Leaders Must Know Now. The future is arriving faster than expected—and AI is at the core of it. McKinsey’s Technology Trends Outlook 2025 is a must-read for executives, founders, and technologists looking to stay ahead. The report evaluates 15 breakthrough technologies based on adoption, investment, talent availability, and real-world momentum. Here are the key insights and strategic takeaways 👇 🔮 1. #AI is the Central Force AI is not just one of many trends—it’s a foundational technology driving others. From developer productivity to robotics, AI is now integrated across industries and functions. Use cases have matured beyond experimentation into real-world value creation. 🧠 2. Generative & Agentic AI: From Tools to Teammates Generative AI continues to surge, but Agentic AI —tools that can reason and take action autonomously—is emerging as the next frontier. These systems will move from responding to prompts to completing tasks, triggering a shift in business automation. ⚙️ 3. Next-Gen Software Development AI-assisted development environments are accelerating time-to-code and shifting how engineering teams function. Companies investing here are cutting product cycles by up to 30%, according to McKinsey insights. 📡 4. Advanced Connectivity Fuels Edge Innovation With maturing 5G, low-Earth-orbit satellites, and edge computing, advanced connectivity is unlocking real-time applications across manufacturing, logistics, and smart infrastructure. This isn't future-talk—deployment is accelerating now. 🔬 5. Applied AI in Real Operations AI-powered vision systems, robotics, and simulation tools are already optimizing everything from warehousing to agriculture. What’s new? These tools are being used at scale, not just in pilot programs. 📊 6. Trust Architecture & Responsible AI As AI grows more autonomous, McKinsey emphasizes trust architecture—governance, risk controls, and ethical design must evolve in tandem. Regulation is coming fast. Companies that prepare early will lead with confidence. 🌱 7. Sustainable Tech: From Buzzword to Bottom Line Tech is finally aligning with sustainability goals. Energy-efficient compute, circular hardware design, and green cloud are becoming investment priorities, not side projects. 💡 Leadership Takeaways ✅ Embed AI as a horizontal strategy, not a vertical investment ✅ Invest in next-gen developer tools to stay agile ✅ Build or upskill talent to lead agentic workflows ✅ Establish clear AI governance frameworks early ✅ Use advanced connectivity to optimize operations ✅ Don’t overlook trust, ethics, and sustainability—they are competitive differentiators. #McKinsey #AI #TechTrends2025 #AgenticAI #DigitalTransformation #FutureOfWork #TrustInTech #GenerativeAI #Sustainability #AILeadership #TechStrategy #BusinessInnovation 🤖📈🌐💼

The innovation gap is widening. 78% of organizations use AI, but only 1% are fully mature in their deployments. As I reflect on McKinsey & Company's Technology Trends 2025, three converging forces are reshaping entire industries—and how leaders must adapt. Autonomous intelligence is reshaping work itself. #Agentic #AI talent demand exploded 985% year-over-year as virtual teammates that anticipate and act emerge. Boston Dynamics introduced Electric Atlas, with Hyundai planning deployment as cobots in automotive manufacturing. McKinsey's QuantumBlack boosted analyst productivity 60% through agentic workflows. Physical-digital convergence accelerates. Robotics expands beyond manufacturing into logistics and healthcare. Immersive reality transforms training. Every workspace becomes intelligent, adapting in real-time. And physical devices become connected devices that extend capabilities beyond predictive maintenance. This isn't automation—it's responsive environments. Infrastructure determines competitive position. AI-ready data center capacity will grow 33% annually through 2030, with tech giants collectively directing hundreds of billions annually toward AI infrastructure—Google, Amazon, Meta, and Microsoft each projected to spend $70-100+ billion on AI capital expenditures in 2025. This isn't just about technology—it's about building unassailable competitive moats through computational control. The way I see it, there are three imperatives for leaders who want to get ahead: 1. Govern for scale, not just safety - From 2017-2023, trusted brands outperformed others by 245 percentage points in cumulative stock returns. Build AI governance that enables rapid deployment while managing enterprise risk. As Roger Roberts emphasizes: "Trust is no longer a soft issue; it's a business-critical asset...digital trust is the license to operate." Trust becomes your sustainable competitive advantage as autonomous systems handle mission-critical decisions. 2. Orchestrate human-machine collaboration - The talent shortage in critical AI skills is real. Train leaders to architect workflows where humans and AI systems amplify each other's capabilities. As Michael Chui notes: "Only 1 percent of companies reported that their use of AI is fully mature. There is still a lot of headroom for transforming companies and industries." 3. Secure computational sovereignty - Organizations that control specialized compute infrastructure will dictate industry terms. The winners will be those who master what Alex Singla calls the shift "from experimentation to scaled adoption, while building robust guardrails for trust and accountability." At Fortive, we're not just responding to this transformation—we're engineering it. By coupling the Fortive Business System (#FBS) with technology, we're integrating these breakthrough capabilities to accelerate innovation and deliver solutions that keep the world's critical infrastructure safe and productive. https://lnkd.in/eW3HMv5R