Furkan Ermağ Blog

Etiket: Technology

  • Stanford AI Experts Predict What Will Happen in 2026: From Hype to Evaluation

    Stanford AI Experts Predict What Will Happen in 2026: From Hype to Evaluation

    After years of fast expansion and billion-dollar bets, 2026 may mark the moment artificial intelligence confronts its actual utility. Stanford faculty across computer science, medicine, law, and economics converge on a striking theme: The era of AI evangelism is giving way to an era of AI evaluation.

    Standardized Benchmarks for Legal AI

    One of the most significant shifts predicted for 2026 is the move toward standardized benchmarks for legal and regulated applications of AI. As AI is increasingly deployed in law, healthcare, and finance, there’s growing recognition that we need objective ways to measure performance.

    • Legal performance metrics: Standardized tests for AI used in legal research and case analysis
    • Healthcare accuracy benchmarks: Objective measurements for diagnostic and treatment AI systems
    • Financial risk assessments: Consistent frameworks for evaluating AI in investment and trading
    • Regulatory compliance tests: Standardized checks for AI meeting legal and ethical requirements

    The End of AI Evangelism

    The era of uncritical enthusiasm for AI is ending. Organizations are becoming more discerning about where AI actually provides value versus where it’s hype. This shift represents a maturing of the AI market and industry approach.

    After years of fast expansion and billion-dollar bets, 2026 may mark the moment artificial intelligence confronts its actual utility. The era of AI evangelism is giving way to an era of AI evaluation.

    — Stanford HAI Experts Predictions 2026

    AI Evaluation Becomes Critical

    Evaluation frameworks will become essential for organizations deploying AI in 2026:

    • ROI measurement: Quantifying actual business value of AI deployments
    • Cost-benefit analysis: Comparing AI benefits to implementation and ongoing costs
    • Performance monitoring: Continuous tracking of AI system accuracy and effectiveness
    • Failure analysis: Understanding when and why AI systems don’t meet expectations

    Across Disciplines Convergence

    Stanford experts from multiple disciplines—computer science, medicine, law, and economics—all point to the same conclusion: 2026 will be about rigorous, critical evaluation of AI rather than enthusiastic adoption.

    • Computer Science: Focus on technical evaluation, limitations, and architecture improvements
    • Medicine: Clinical validation, patient safety, and regulatory approval of AI tools
    • Law: Ethical frameworks, bias assessment, and liability considerations
    • Economics: Cost-benefit analysis, employment impacts, and market efficiency studies

    What This Means for Organizations

    The shift from evangelism to evaluation has profound implications for organizations:

    • More strategic adoption: AI deployments will be based on proven value, not hype
    • Better governance: Formal evaluation frameworks become part of AI governance
    • Reduced waste: Organizations will invest less in ineffective AI projects
    • Greater accountability: Clear metrics and evaluation increase responsibility

    The Rise of Critical AI Research

    Academic and industry research will increasingly focus on critical analysis rather than advancement for its own sake. This includes:

    • Limitation studies: Honest assessment of what AI can and cannot do
    • Failure analysis: Systematic study of when and why AI systems fail
    • Ethics research: Rigorous examination of societal impacts and biases
    • Safety science: Technical work on making AI systems more reliable and controllable

    What This Means for You

    For individuals and professionals, the era of AI evaluation means:

    • Skepticism is valuable: Critical thinking about AI claims becomes more important
    • Evidence-based decisions: Choose AI tools based on proven performance, not marketing
    • Continuous learning: Stay informed about AI limitations and emerging research
    • Balanced approach: Enthusiastic but critical attitude toward AI adoption

    Looking Ahead

    2026 represents a maturation point for AI as an industry and a technology. The shift from evangelism to evaluation is healthy—it means we’re moving beyond the initial excitement phase and into a period of careful consideration and thoughtful deployment.

    This doesn’t mean AI won’t advance or transform industries. It means that AI will advance more thoughtfully, with more rigorous testing, better evaluation, and more realistic expectations. And that’s exactly what we need for responsible AI development.

  • Edge AI Moves from Hype to Reality in 2026: What You Need to Know

    Edge AI Moves from Hype to Reality in 2026: What You Need to Know

    For years, edge AI has been promised as the future of artificial intelligence deployment. In 2026, according to IBM researchers, that promise finally becomes reality. Edge AI—running AI models on devices rather than in the cloud—offers compelling advantages that make it essential for many applications.

    What is Edge AI?

    Edge AI refers to processing AI computations locally on devices such as smartphones, cameras, sensors, and IoT devices, rather than sending data to cloud servers for processing. This fundamental shift in AI architecture has profound implications for how AI is deployed.

    Key Advantages of Edge AI

    The move to edge AI offers several critical benefits that are driving adoption:

    • Lower latency: Processing happens instantly without network round-trip to cloud servers, enabling real-time applications
    • Enhanced privacy: Sensitive data never leaves the device, addressing privacy and compliance concerns
    • Reduced bandwidth: Only processed results or summaries need to be transmitted, not raw data
    • Better reliability: AI works even without internet connectivity or during network outages
    • Lower costs: No ongoing cloud computing fees for inference workloads

    Hardware Enablers

    Several hardware developments are making edge AI practical in 2026:

    • NPUs become standard: Neural Processing Units are now built into most new smartphones and laptops
    • Efficient small models: Small Language Models (SLMs) can run on modest hardware while maintaining good performance
    • Quantization breakthroughs: Models can be compressed to run efficiently on edge devices
    • Chiplet designs: Specialized AI chips can be integrated with standard processors

    Edge AI will move from hype to reality in 2026. The convergence of efficient models, hardware advances, and practical applications makes this the year edge AI goes mainstream.

    — IBM AI Hardware Center Research

    Use Cases Driving Adoption

    Certain applications are particularly well-suited to edge AI and are driving adoption:

    • Smart cameras: Real-time object detection and recognition without cloud dependency
    • Health monitoring: Wearables that analyze health data locally for privacy and immediacy
    • Industrial IoT: Predictive maintenance and quality control at the factory floor
    • Automotive: Self-driving features that need instant decision-making capabilities
    • Smart home: Voice assistants and automation that work offline

    Challenges and Solutions

    While edge AI offers compelling benefits, organizations face challenges in implementation:

    • Model selection: Choosing the right model size for edge deployment requires careful evaluation
    • Hardware diversity: Supporting many different device types increases complexity
    • Model updates: Keeping edge models updated across many devices is challenging
    • Performance vs. accuracy: Balancing model efficiency with acceptable accuracy requires optimization

    What This Means for Businesses

    Businesses should prepare for the edge AI transition:

    • Evaluate use cases: Identify which applications benefit from edge deployment
    • Invest in edge skills: Develop expertise in edge AI frameworks and deployment
    • Design for offline: Build applications that work gracefully with limited or no connectivity
    • Plan for updates: Establish processes for updating models across edge devices

    The Future is Hybrid

    While edge AI will grow dramatically, it’s not replacing cloud AI. The future is hybrid: using edge AI for low-latency, privacy-sensitive applications, and cloud AI for complex processing and training. Organizations that master this hybrid approach will be best positioned for the AI landscape of 2026 and beyond.

    Edge AI is finally ready for prime time. The hype was justified—the technology just needed to catch up to the promise. In 2026, it finally does.

  • Cross-Functional Super Agents Will Emerge in 2026 AI Landscape

    Cross-Functional “Super Agents” Will Emerge in 2026 AI Landscape

    “We’ve moved past the era of single-purpose agents,” says Chris Hay, Distinguished Engineer at IBM. In 2024, agents were small and specialized: email writer, research helper. But now, with reasoning capabilities, agents can plan, call tools, and complete complex tasks. In 2026, we’re seeing the rise of what I call ‘super agents’.”

    From Specialized to General-Purpose

    The evolution from single-purpose agents to super agents represents a fundamental shift in AI capabilities. Where previous AI agents could handle one specific task—like writing emails or summarizing documents—super agents can coordinate across multiple functions and tools.

    Hay predicts that “in 2026, I see agent control planes and multi-agent dashboards becoming real. You’ll kick off tasks from one place, and those agents will operate across environments—your browser, your editor, your inbox—without you having to manage a dozen separate tools.”

    Cross-Environment Operation

    One of the defining characteristics of super agents will be their ability to operate across different environments. This means:

    • Browser automation: Agents can navigate and interact with websites
    • Document editing: Direct manipulation of files in your editor
    • Communication: Reading and writing emails and messages
    • System integration: Connecting to APIs, databases, and internal tools
    • Workflow coordination: Managing multi-step processes across different applications

    Adaptive User Interfaces

    Forget static software in user experience and user interface. Hay predicts we’ll see interfaces and apps that can adapt to any scenario, making every user an AI composer. This means software that reshapes itself based on context, task, and user preferences.

    We’ve moved past the era of single-purpose agents. In 2026, I see agent control planes and multi-agent dashboards becoming real.

    — Chris Hay, Distinguished Engineer, IBM

    Every User Becomes an AI Composer

    The vision for 2026 is that everyone becomes an AI composer—someone who can orchestrate AI agents to accomplish complex tasks without needing to be a programmer or AI expert. This democratization of AI capabilities has profound implications:

    • Lower barrier to entry: More people can leverage AI power without technical skills
    • Increased productivity: Complex workflows that previously required teams can be managed by individuals
    • Empowerment: Non-technical users can build sophisticated automation
    • New workflows: Entirely new ways of working emerge when anyone can orchestrate AI

    The Control Plane Wars

    “Whoever owns that front door to super agent will shape the market,” Hay predicts. This means we’re likely to see competition for controlling the interface between humans and AI agents. The company that builds the most intuitive, powerful, and open platform for managing AI agents will have tremendous influence.

    Think of it like the operating system wars of the past—but for AI. The company that controls the AI OS for agents will have significant strategic advantage.

    Practical Impact

    For businesses and individuals, the rise of super agents means:

    • Centralized AI management: One place to coordinate all AI activities
    • Unified workflows: No more context switching between different AI tools
    • Scalable automation: Complex business processes can be automated end-to-end
    • Reduced technical debt: Less custom integration work needed as agents handle cross-platform tasks

    Looking Ahead

    The era of single-purpose AI agents is ending. In 2026, super agents that can work across environments, coordinate multiple tasks, and adapt to any scenario will emerge. This represents a significant step toward the long-promised vision of AI as a capable assistant that can genuinely help with complex, real-world work.

    The race to build the super agent platform is on. And the winners will shape how we all interact with AI for years to come.

  • Systems, Not Models, Will Define AI Leadership in 2026

    Systems, Not Models, Will Define AI Leadership in 2026

    In 2026, competition in AI will shift from building the best models to creating the best systems. According to Gabe Goodhart, Chief Architect of AI Open Innovation at IBM, “We’re going to hit a bit of a commodity point. It’s a buyer’s market. You can pick the model that fits your use case just right and be off to the races. The model itself is not going to be the main differentiator.”

    From Models to Orchestration

    What matters now is orchestration: combining models, tools, and workflows into cohesive systems. “If you go to ChatGPT, you are not talking to an AI model,” Goodhart explains. “You are talking to a software system that includes tools for searching the web, doing all sorts of different individual scripted programmatic tasks, and most likely an agentic loop.”

    This shift represents a fundamental change in how companies should approach AI. Rather than investing in building proprietary models, organizations should focus on integrating models into effective systems that solve real business problems.

    The model itself is not going to be the main differentiator. What matters is orchestration: combining models, tools, and workflows.

    — Gabe Goodhart, Chief Architect, AI Open Innovation, IBM

    Cooperative Model Routing

    In 2026, we’ll see more cooperative model routing systems. “You’ll have smaller models that can do lots of things and delegate to bigger models when needed,” Goodhart predicts. This approach offers several advantages:

    • Cost efficiency: Small models handle routine tasks, reserving expensive large models for complex queries
    • Speed: Faster responses for common use cases that don’t require full model capabilities
    • Flexibility: Easy to swap models in or out as better options become available
    • Reliability: If one model fails, the system can route to alternatives

    The Winner Takes the System Level

    “Whoever nails that system-level integration will shape the market,” Goodhart says. This means the companies that dominate AI won’t be those with the best individual models, but those with the most sophisticated systems for orchestrating and integrating multiple models, tools, and workflows.

    This has profound implications for AI strategy:

    • Platform thinking: Companies need to think like platform builders, not just model developers
    • Integration capabilities: The ability to connect AI systems with existing business infrastructure becomes critical
    • User experience: The interface between humans and AI systems becomes a key differentiator
    • Tooling: Building and maintaining the tools that AI agents use becomes more important than the models themselves

    Practical Implications

    For businesses, this shift means rethinking AI investment priorities:

    • Less focus on training: Training custom models becomes less important for most organizations
    • More focus on integration: Integrating existing models into business workflows becomes the priority
    • Tool development: Building the tools and connectors that make AI useful becomes crucial
    • System architecture: Designing robust AI systems that can route between models becomes a core competency

    The New Competitive Landscape

    In 2026, the companies that win will be those that build the best systems, not necessarily the best models. This democratizes AI in some ways—any company can access similar models—but raises the bar for system-level innovation.

    The era of model competition is giving way to the era of system competition. And the companies that understand this shift and invest accordingly will be the ones leading the next phase of AI evolution.

  • Hardware Efficiency Will Become the New Scaling Strategy in 2026 AI Development

    Hardware Efficiency Will Become the New Scaling Strategy in 2026 AI Development

    After years of brute-force scaling, 2026 will mark a fundamental shift in how AI is developed and deployed. According to Kaoutar El Maghraoui, Principal Research Scientist and Manager at IBM’s AI Hardware Center, “2026 will be the year of frontier versus efficient model classes. Next to huge models with billions of parameters, efficient, hardware-aware models running on modest accelerators will appear.”

    The End of Unlimited Scaling

    In 2025, demand for AI computing power outran supply chain capacity, forcing companies to optimize around compute availability. This pressure split hardware strategies into two camps: scale-up with superchips like H200, B200, and GB200—or scale-out with edge optimizations, quantization breakthroughs, and small LLMs.

    “We can’t keep scaling compute, so the industry must scale efficiency instead,” El Maghraoui explains. This represents a fundamental philosophical shift from “bigger is better” to “smarter is better.”

    2026 will be the year of frontier versus efficient model classes. We can’t keep scaling compute, so the industry must scale efficiency instead.

    — Kaoutar El Maghraoui, Principal Research Scientist, IBM

    Edge AI Moves from Hype to Reality

    The focus on hardware efficiency will accelerate the deployment of AI at the edge—running AI models on devices rather than in the cloud. Edge AI offers several critical advantages:

    • Lower latency: Processing happens locally without needing to send data to the cloud
    • Better privacy: Sensitive data never leaves the device
    • Reduced costs: No cloud computing fees for inference
    • Offline capability: AI works even without internet connectivity

    New Hardware Architectures Emerge

    The hardware race won’t only be about GPUs anymore. El Maghraoui predicts several new types of AI hardware will mature in 2026:

    • ASIC-based accelerators: Application-specific integrated circuits optimized for AI workloads
    • Chiplet designs: Modular chip architectures that can be customized for specific tasks
    • Analog inference: Analog computing approaches that dramatically reduce power consumption
    • Quantum-assisted optimizers: Hybrid quantum-classical systems for optimization problems
    • New chip classes for agentic workloads: Specialized hardware designed for AI agent workflows

    Implications for Developers

    This shift toward efficiency has significant implications for software developers:

    • Model selection matters: Developers must choose the right model size for each use case rather than defaulting to the largest available
    • Optimization becomes critical: Quantization, pruning, and distillation techniques become standard practices
    • Hardware awareness: Understanding deployment constraints becomes part of model development
    • Edge deployment skills: Experience with on-device AI frameworks like TensorFlow Lite and ONNX becomes valuable

    The Business Case for Efficiency

    Beyond technical necessity, efficiency offers compelling business benefits:

    • Cost reduction: Smaller models on efficient hardware cost significantly less to run
    • Scalability: Efficient models can be deployed at scale without infrastructure bottlenecks
    • Sustainability: Lower energy consumption reduces environmental impact and operating costs
    • Faster time-to-market: Efficient models can be deployed on existing hardware without massive infrastructure investments

    Looking Beyond GPUs

    While GPUs have been the workhorses of the AI revolution, 2026 will see diversification in AI hardware. Organizations that want to stay competitive will need to evaluate and potentially invest in these emerging hardware approaches. The companies that master efficient AI deployment—using the right hardware for the right workload—will have a significant advantage in the coming years.

    The era of “throwing more compute at the problem” is ending. Welcome to the era of doing more with less.

  • Quantum Computing Will Outperform Classical Computers in 2026: What This Means for the Future

    Quantum Computing Will Outperform Classical Computers in 2026: What This Means for the Future

    IBM has publicly stated that 2026 will mark the first time a quantum computer will be able to outperform a classical computer—the point at which a quantum computer can solve a problem better than all classical-only methods. This milestone represents a critical breakthrough in computing technology with far-reaching implications across multiple industries.

    Beyond Theory: Quantum Becomes Practical

    “We’ve moved past theory,” says Jamie Garcia, Director of Strategic Growth & Quantum Partnerships at IBM. “Today, we’re using industry’s best-available quantum computers for real use cases. While these aren’t production-scale problems, they’re signals where we expect value to increase as quantum continues maturing.”

    The convergence of quantum computing with AI is already happening. Tools like Qiskit Code Assistant are helping developers generate quantum code automatically. IBM is building a quantum-centric supercomputing architecture that combines quantum computing with powerful high-performance computing and AI infrastructure, supported by CPUs, GPUs, and other compute engines.

    Industry Impact and Applications

    Garcia highlights incredible progress in research across several critical areas where quantum computing will deliver breakthroughs in 2026:

    • Drug Development: Quantum computers can simulate molecular interactions at unprecedented scales, accelerating drug discovery and reducing development costs
    • Materials Science: New materials with superior properties can be discovered through quantum simulations
    • Financial Optimization: Complex portfolio optimization and risk assessment become more accurate and faster
    • Cryptography: Quantum-resistant encryption algorithms will become crucial as quantum threats emerge

    Hardware Partnerships and Infrastructure

    To push quantum computing forward, major hardware partnerships are forming. AMD and IBM are exploring how to integrate AMD CPUs, GPUs, and FPGAs with IBM quantum computers to efficiently accelerate a new class of emerging algorithms. These algorithms are outside the current reach of either paradigm working independently.

    The convergence of quantum with AI and classical computing is creating a new era of problem-solving capabilities that were previously impossible.

    — Jamie Garcia, Director, Strategic Growth & Quantum Partnerships, IBM

    What This Means for Businesses

    The quantum advantage in 2026 signals that businesses need to start preparing now. Organizations should:

    • Assess quantum readiness: Identify which business problems could benefit from quantum computing
    • Invest in quantum literacy: Train teams on quantum computing concepts and algorithms
    • Explore quantum-safe encryption: Prepare for post-quantum cryptography standards
    • Pilot quantum applications: Begin small-scale experiments with quantum algorithms

    The Path Forward

    While 2026 marks a significant milestone, the quantum revolution is just beginning. As quantum hardware continues to improve and more algorithms are developed, the gap between quantum and classical computing will widen. Businesses that start preparing now will be positioned to leverage quantum advantage as it becomes more practical and accessible.

    The quantum era is no longer a distant future—it’s arriving in 2026. The question is no longer “if” quantum computing will transform industries, but “when” and “how quickly” organizations will adapt to this new paradigm of computation.