Since its inception, generative AI (gen AI) has been evolving. Already, we have seen developers such as OpenAI and Meta move away from large models to include smaller and less expensive ones, improving AI models to do the same or more using less. Prompt engineering is changing as models such as ChatGPT get more intelligent and better able to understand the nuances of human language. As LLMs are trained on more specific information, they can provide deep expertise for specialized industries, becoming always-on agents ready to help complete tasks.

AI is not a flash-in-the-pan technology. It's not a phase. Over 60 countries have developed national AI strategies to harness AI's benefits while mitigating risks. This means substantial investments in research and development, reviewing and adapting relevant policy standards and regulatory frameworks and ensuring the technology doesn’t decimate the fair labor market and international cooperation.

It is becoming easier for humans and machines to communicate, enabling AI users to accomplish more with greater proficiency. AI is projected to add USD 4.4 trillion (link resides outside of ibm.com) to the global economy through continued exploration and optimization.

Between now and 2034, AI will become a fixture in many aspects of our personal and business lives. Generative AI models such as GPT-4 have shown immense promise in the short time they've been available for public consumption, but their limitations have also become well known. As a result, the future of AI is being defined by a shift toward both open source large-scale models for experimentation and the development of smaller, more efficient models to spur ease of use and facilitate a lower cost.

Initiatives such as Llama 3.1, an open source AI model with 400 billion parameters and Mistral Large 2, released for research purposes, illustrate the trend of fostering community collaboration in AI projects while maintaining commercial rights. The growing interest in smaller models has led to the creation of models such as the 11 billion parameter mini GPT 4o-mini, which is fast and cost-effective. It won't be long before there's a model suitable for embedding in devices such as smartphones, especially as the cost continues to decrease.

This movement reflects a transition from exclusively large, closed models to more accessible and versatile AI solutions. While smaller models offer affordability and efficiency, there remains a public demand for more powerful AI systems, indicating there will likely be a balanced approach in AI development to attempt to prioritize both scalability and accessibility. These new models deliver greater precision with fewer resources, making them ideal for enterprises needing bespoke content creation or complex problem-solving capabilities.

AI has influenced the development of several core technologies. AI plays a pivotal role in advancing computer vision by enabling more accurate image and video analysis, which is essential for technologies such as autonomous vehicles and medical diagnostics. In natural language processing (NLP), AI enhances the ability of machines to comprehend and generate human language, improving communication interfaces and enabling more sophisticated translation and sentiment analysis tools.

AI supercharges predictive and big data analytics by processing and interpreting vast amounts of data to forecast trends and inform decisions. In robotics, the development of more autonomous and adaptable machines simplifies tasks such as assembly, exploration and service delivery. Also, AI-driven innovations on the Internet of Things (IoT) enhance the connectivity and intelligence of devices, leading to smarter homes, cities and industrial systems.

Here are some of the advancements in AI that we should see in ten years:

The fledgling field of multimodal AI will be thoroughly tested and refined by 2034. Unimodal AI focuses on a single data type, such as NLP or computer vision. In contrast, multimodal AI more closely resembles how humans communicate by understanding data across visuals, voice, facial expressions and vocal inflections. This technology will integrate text, voice, images, videos and other data to create more intuitive interactions between humans and computer systems. It has the potential to power advanced virtual assistants and chatbots that understand complex queries and can provide bespoke text, visual aids or video tutorials in response.

AI will become even more integrated into personal and professional spheres, driven by user-friendly platforms that allow nonexperts to use AI for business, individual tasks, research and creative projects. These platforms, similar to today's website builders, will enable entrepreneurs, educators and small businesses to develop custom AI solutions without requiring deep technical expertise.

API-driven AI and microservices will allow businesses to integrate advanced AI functions into their existing systems in a modular fashion. This approach will speed up the development of custom applications without requiring extensive AI expertise.

For enterprises, easier model creation means faster innovation cycles, with custom AI tools for every business function. No-code and low-code platforms will allow non-technical users to create AI models by using drag-and-drop components, plug-and-play modules or guided workflows. As many of these platforms will be LLM-based, users can also query up an AI model using prompts.

Auto-ML platforms are rapidly improving, automating tasks such as data preprocessing, feature selection and hyperparameter tuning. Over the next decade, Auto-ML will become even more user-friendly and accessible, allowing people to create high-performing AI models quickly without specialized expertise. Cloud-based AI services will also provide businesses with prebuilt AI models that can be customized, integrated and scaled as needed.

For hobbyists, accessible AI tools will foster a new wave of individual innovation, allowing them to develop AI applications for personal projects or side businesses.

Open-source development can foster transparency, while careful governance and ethical guidelines might help maintain high-security standards and build trust in AI-driven processes. The culmination of this ease of access might be a fully voice-controlled multimodal virtual assistant capable of creating visual, text, audio or visual assets on demand.

Though very speculative, if an Artificial General Intelligence (AGI) system emerges by 2034, we might see the dawn of AI systems that can autonomously generate, curate and refine their own training datasets, enabling self-improvement and adaptation without human intervention.

As generative AI becomes more centralized within organizations, companies might start to offer "AI hallucination insurance." Despite extensive training, AI models can deliver incorrect or misleading results. These errors often stem from insufficient training data, incorrect assumptions or biases in the training data.

Such insurance would protect financial institutions, the medical industry, the legal industry and others against unexpected, inaccurate or harmful AI outputs. Insurers might cover financial and reputational risks associated with these errors, similar to how they handle financial fraud and data breaches.

AI decision-making and prediction modeling will advance to the point where AI systems function as strategic business partners, helping executives make informed decisions and automate complex tasks. These AI systems will integrate real-time data analysis, contextual awareness and personalized insights to offer tailored recommendations, such as financial planning and customer outreach, that align with business goals.

Improved NLP allows AI to participate in conversations with leadership, offering advice based on predictive modeling and scenario planning. Businesses will rely on AI to simulate potential outcomes, manage cross-department collaboration and refine strategies based on continuous learning. These AI partners will enable small businesses to scale faster and operate with efficiencies similar to large enterprises.

Quantum AI, using the unique properties of qubits, might shatter the limitations of classical AI by solving problems that were previously unsolvable due to computational constraints. Complex material simulations, vast supply chain optimization and exponentially larger datasets might become feasible in real time. This might transform fields of scientific research, where AI will push the boundaries of discovery in physics, biology and climate science by modeling scenarios that would take classical computers millennia to process.

A major hurdle in AI advancement has been the enormous time, energy and cost involved in training massive models, such as large language models (LLMs) and neural networks. Current hardware requirements are nearing the limits of conventional computing infrastructure, which is why innovation will focus on enhancing hardware or creating entirely new architectures. Quantum computing offers a promising avenue for AI innovation, as it might drastically reduce the time and resources needed to train and run large AI models.

Bitnet models use ternary parameters, a base-3 system with 3 digits to represent information. This approach addresses the energy problem by enabling AI to process information more efficiently, relying on multiple states rather than binary data (0s and 1s). This might result in faster computations with less power consumption.

Y Combinator-backed startups and other companies are investing in specialized silicon hardware tailored for bitnet models, which might dramatically accelerate AI training times and reduce operational costs. This trend suggests that future AI systems will combine quantum computing, bitnet models and specialized hardware to overcome computational limits.

AI regulations and ethical standards will have to advance significantly for AI ubiquity to become a reality. Driven by frameworks such as the EU AI Act, a key development will be the creation of rigorous risk management systems, classifying AI into risk tiers and imposing stricter requirements on high-risk AI. AI models, especially generative and large-scale ones, might need to meet transparency, robustness and cybersecurity standards. These frameworks are likely to expand globally, following the EU AI Act, which sets standards for healthcare, finance and critical infrastructure sectors.

Ethical considerations will shape regulations, including bans on systems that pose unacceptable risks, such as social scoring and remote biometric identification in public spaces. AI systems will be required to include human oversight, protect fundamental rights, address issues such as bias and fairness and guarantee responsible deployment.

AI that proactively anticipates needs and makes decisions autonomously will likely become a core part of personal and business life. Agentic AI refers to systems composed of specialized agents that operate independently, each handling specific tasks. These agents interact with data, systems and people to complete multistep workflows, enabling businesses to automate complex processes such as customer support or network diagnostics. Unlike monolithic large language models (LLMs), agentic AI adapts to real-time environments, using simpler decision-making algorithms and feedback loops to learn and improve.

A key advantage of agentic AI is its division of labor between the LLM, which handles general tasks and domain-specific agents, which provide deep expertise. This division helps mitigate LLM limitations. For example, in a telecommunications company, an LLM might categorize a customer inquiry, while specialized agents retrieve account information, diagnose issues and formulate a solution in real time.

By 2034, these agentic AI systems might become central to managing everything from business workflows to smart homes. Their ability to autonomously anticipate needs, make decisions and learn from their environment might make them more efficient and cost-effective, complementing the general capabilities of LLMs and increasing AI's accessibility across industries.

As human-generated data becomes scarce, enterprises are already pivoting to synthetic data—artificial datasets that mimic real-world patterns without the same resource limitations or ethical concerns. This approach will become the standard for training AI, enhancing model accuracy while promoting data diversity. AI training data will include satellite imagery, biometric data, audio logs and IoT sensor data.

The rise of customized models will be a key AI trend, with organizations using proprietary datasets to train AI tailored to their specific needs. These models, designed for content generation, customer interaction and process optimization, can outperform general-purpose LLMs by aligning closely with an organization's unique data and context. Companies will invest in data quality assurance so both real and synthetic data meet high standards of reliability, accuracy, and diversity, maintaining AI performance and ethical robustness.

The challenge of "shadow AI"—unauthorized AI tools used by employees—will push organizations to implement stricter data governance, guaranteeing that only approved AI systems access sensitive, proprietary data.

As AI continues to evolve, several ambitious "moonshot" ideas are emerging to address current limitations and push the boundaries of what artificial intelligence can achieve. One such moonshot is post-Moore computing¹, which aims to move beyond traditional von Neumann architecture as GPUs and TPUs near their physical and practical limits.

With AI models becoming increasingly complex and data-intensive, new computing paradigms are needed. Innovations in neuromorphic computing², which mimics the neural structure of the human brain, are at the forefront of this transition. Also, optical computing³, which uses light instead of electrical signals to process information, offers promising avenues for enhancing computational efficiency and scalability.

Another significant moonshot is the development of a distributed Internet of AI⁴, or federated AI, which envisions a distributed and decentralized AI infrastructure. Unlike traditional centralized AI models that rely on vast data centers, federated AI operates across multiple devices and locations, processing data locally to enhance privacy and reduce latency.

By enabling smartphones, IoT gadgets and edge computing nodes to collaborate and share insights without transmitting raw data, federated AI fosters a more secure and scalable AI ecosystem. Current research focuses on developing efficient algorithms and protocols for seamless collaboration among distributed models, facilitating real-time learning while maintaining high data integrity and privacy standards.

Another pivotal area of experimentation addresses the inherent limitations of the transformer architecture's attention mechanism⁵. Transformers rely on an attention mechanism with a context window to process relevant parts of the input data, such as previous tokens in a conversation. However, as the context window expands to incorporate more historical data, the computational complexity increases quadratically, making it inefficient and costly.

To overcome this challenge, researchers are exploring approaches such as linearizing the attention mechanism or introducing more efficient windowing techniques, allowing transformers to handle larger context windows without the exponential increase in computational resources. This advancement would allow AI models to better understand and incorporate extensive past interactions, leading to more coherent and contextually relevant responses.

Imagine starting your day in 2034. A voice-controlled intelligent assistant, connected to every aspect of your life, greets you with your family meal plan for the week, tailored to everyone’s preferences. It will notify you of the current state of your pantry, ordering groceries when necessary. Your commute becomes automatic as your virtual chauffeur navigates the most efficient route to work, adjusting for traffic and weather in real-time.

At work, an AI partner sifts through daily tasks and provides you with actionable insights, help with routine tasks and acts as a dynamic, proactive knowledge database. On a personal level, AI-embedded technology can craft bespoke entertainment, generating stories, music or visual art customized for your tastes. If you want to learn something, the AI can provide video tutorials tailored to your learning style, integrating text, images and voice.

As AI adoption spreads and the technology evolves, its impact on global operations will be immense. Here are some major implications of advanced AI technology:

AI will play a dual role in climate action by simultaneously contributing to rising energy demands and serving as a tool for mitigation. The computational resources required to train and deploy large AI models significantly increase energy consumption, exacerbating carbon emissions if the energy sources are not sustainable. Alternatively, AI can enhance climate initiatives by optimizing energy usage in various sectors, improving climate modeling and predictions and enabling innovative solutions for renewable energy, carbon capture and environmental monitoring.

In manufacturing, AI-powered robots can perform complex assembly tasks with precision, boosting production rates and reducing defects. In healthcare, automated diagnostic tools assist doctors in identifying diseases more accurately and swiftly. AI-driven process automation and machine learning in finance, logistics and customer experience can streamline operations, reduce costs and improve service quality. By handling repetitive tasks, AI allows human workers to focus on strategic and creative endeavors, fostering innovation and productivity.

The rise of AI-driven automation will inevitably lead to job displacement, particularly in industries that rely heavily on repetitive and manual tasks. Roles such as data entry, assembly line work and routine customer service may see significant reductions as machines and algorithms take over these functions. However, it will also create opportunities in AI development, data analysis and cybersecurity. The demand for AI maintenance, oversight and ethical governance skills will grow, providing avenues for workforce reskilling.

Gen AI has made it easier to create deepfakes—realistic but fake audio, video and images—used to spread false information and manipulate public opinion. This poses challenges for information integrity and media trust. Addressing this requires advanced detection tools, public education and possibly legal measures to hold creators of malicious deepfakes accountable.

People anthropomorphize AI, forming emotional attachments and complex social dynamics, as seen with the ELIZA Effect⁶ and other AI companions. Over the next decade, these relationships might become more profound, raising psychological and ethical questions. Society must promote healthy interactions with increasingly human-like machines and help individuals discern genuine human interactions from AI-driven ones.

As AI-generated content dominates the internet—estimated to comprise around 50% of online material—the availability of human-generated data decreases. Researchers predict that by 2026, public data for training large AI models might run out. To address this, the AI community is exploring synthetic data generation and novel data sources, such as IoT devices and simulations, to diversify AI training inputs. These strategies are essential for sustaining AI advancements and ensuring that models remain capable in an increasingly data-saturated digital landscape.

As AI continues to progress and the focus shifts toward more cost-efficient models that enable tailored solutions for individuals and enterprises, trust and security must remain paramount.

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