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SparkLabs Primer on Artificial Intelligence 2025

Our primer on artificial intelligence (AI)! To help those who want a basic understanding of AI, its primary attributes and future trends, you can read through our deck here!

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SparkLabs Group Since 2013, SparkLabs has invested in over 600 startups with a $56 billion in total post money valuation. The Korea Association of Health partnered with SparkLabs to operate MediOpenLab in 2024 and beyond. SparkBioLab and its portfolio companies will have access to MediCheck service's data from over 2 million health check-ups every year. Google chose SparkLabs Korea to jointly operate a Green Tech Accelerator in Busan, South Korea from 2021 to 2022. Facebook selected SparkLabs Korea to run their Innovation Lab Korea from 2018 to 2019. SparkLabs Group was the only venture capital firm (or any investment company) in the world to be recognized in Newsweek’s 2019 Top 100 Smart City Partners Awards
SparkLabs Group SparkLabs has been investing in AI since 2016. We are investors in OpenAI, Vectara, Kneron, Anthropic, Lucidya, xAI and others. One of nine investment firms invited to participate in OpenAI’s recent fundraising round that closed in January 2024. SparkLabs Group has a partnership with UMG, the world’s leading music company with presence in more than 60 territories, to scout and develop the next wave of digital music startups in Asia. As of Dec 2024, we have invested in 323 startups through its accelerator programs across the globe. 280 have survived or were successful exits, which is a survival rate of 86.7% since 2013. This is testament to the team’s dedication to our startup founders.
Alison Gopnik Cognitive Scientist “One of the fascinating things about the search for AI is that it’s been so hard to predict which parts would be easy or hard. At first, we thought that the quintessential preoccupations of the officially smart few, like playing chess or proving theorems—the corridas of nerd machismo—would prove to be hardest for computers. In fact, they turn out to be easy. Things every dummy can do, like recognizing objects or picking them up, are much harder. And it turns out to be much easier to simulate the reasoning of a highly trained adult expert than to mimic the ordinary learning of every baby.”
Introduction to AI Our Primer for AI
Artificial Intelligence (AI) technology represents one of the most transformative innovations of the 21st century, reshaping the way we interact with machines, process information, and solve complex problems. At its core, AI involves the creation of algorithms and systems capable of performing tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and understanding natural language. From self-driving cars to personalized recommendations, AI's applications are vast and varied, influencing industries as diverse as healthcare, finance, manufacturing, and entertainment. It will eventually touch every aspect of our lives in the coming decade.
The rapid advancements in AI technology are driven by breakthroughs in machine learning, deep learning, natural language processing, and computer vision. These advancements enable machines to not only process vast amounts of data but also to adapt and improve their performance over time. As AI continues to evolve, it raises profound questions about the future of work, ethics, and societal impacts. Understanding the foundations, current capabilities, and potential future developments of AI is essential for anyone seeking to navigate the opportunities and challenges posed by this cutting-edge technology.
“AI models are advancing at an extraordinary pace, but turning that raw capability into real economic value is only just beginning. There’s a vast and complex gap between what these models can theoretically do and what it takes to actually deliver production-grade outcomes. Bridging that gap is where the opportunity lies, and it’s where we’re seeing some of the most impactful innovation today. Whether it’s working with companies like Ramp, Webflow, or HubSpot, the lesson is clear: applying AI effectively requires deep integration, rigorous calibration, and thoughtful design. At AirOps, we’re focused on helping enterprise content teams drive visibility in AI-powered search experiences like ChatGPT and Google AI by managing content at scale, because the future of discovery is conversational, and those who show up well in those interfaces will win.” Alex Halliday Founder & CEO at AirOps Former VP of Product at Bungalow Former Head of Product at MasterClass Venture Partner at SparkLabs Global Ventures
Timeline of Major AI Milestones From Foundational Ideas to Frontier Models (1950-2025) by SparkLabs Group
The building blocks of Artificial Intelligence (AI) are the foundational components and technologies that enable AI systems to function effectively. These include: DATA Data is the fuel for AI systems. Large volumes of structured and unstructured data are required to train AI models. This data can include text, images, audio, video, and sensor data, among others. Importance: The quality, quantity, and diversity of data directly impact the performance and accuracy of AI systems.
Algorithms Algorithms are the mathematical procedures or sets of rules that AI systems use to process data and make decisions. They include various types of models, such as machine learning algorithms, deep learning models, and reinforcement learning frameworks. Importance: Algorithms determine how AI systems learn from data and how they generalize to new, unseen data.
Machine Learning Machine Learning (ML) is a subset of AI that focuses on developing algorithms that enable computers to learn from data and improve their performance over time without being explicitly programmed. Importance: ML is the backbone of modern AI, allowing systems to identify patterns, make predictions, and adapt to new information. Neural Networks Neural networks are a key component of deep learning, a subset of machine learning. Inspired by the human brain, neural networks consist of interconnected layers of nodes (neurons) that process data in complex ways to recognize patterns and make decisions. Importance: Neural networks are essential for tasks such as image and speech recognition, natural language processing, and more complex decision-making processes.
Natural Language Processing (NLP) NLP is a field of AI that focuses on the interaction between computers and human language. It involves the development of algorithms that allow machines to understand, interpret, and generate human language. Importance: NLP is crucial for applications such as chatbots, language translation, sentiment analysis, and voice-activated assistants.
Computer Vision Computer vision enables machines to interpret and make decisions based on visual data, such as images and videos. It involves techniques for image recognition, object detection, and image segmentation. Importance: Computer vision is vital for applications like autonomous vehicles, facial recognition, and medical imaging.
Ethical and Regulatory Frameworks These frameworks guide the responsible development and deployment of AI, addressing issues such as bias, fairness, transparency, and accountability. Importance: As AI becomes more integrated into society, ensuring that it is developed and used ethically is critical for gaining public trust and preventing harm.
On regulation… “I believe we should be very slow to regulate AI and very mindful of the 2nd and 3rd order effects that could come from any regulations we enact. Many of the potential harms of AI are already addressable under existing laws. We shouldn't just start regulating everything without knowing why we're doing it. It will become very clear over time what new problems arise that actually need new regulations.” Advances for AI over the next 3 years… “Major improvements in LLM memory will occur, and they will have access to your files. I expect we'll have a version of ChatGPT that has access to all your files and remembers everything you've said before, making AI an incredibly powerful personal assistant.” Nathan Lands Founder & CEO at Lore Co-Creator & Host at The Next Wave Podcast Venture Partner at SparkLabs AI
Computing Power Advanced AI systems require significant computational resources to process large datasets and perform complex calculations. This is provided by powerful processors, GPUs (Graphics Processing Units), and distributed computing systems. Importance: The availability of high-performance computing power enables the training and operation of sophisticated AI models. Together, these building blocks form the foundation of AI technology, enabling the creation of systems that can learn, reason, perceive, and interact in ways that were once the exclusive domain of humans.
“Getting computers to understand and find human knowledge has always been a challenge. Legacy keyword search systems are inflexible and require rich knowledge graphs + language rules to work well, which makes them very expensive to build / maintain. The new generation of Gen AI algorithms allow us to understand and find information semantically across languages, which leads to much more powerful systems at significantly lower costs. At Vectara we specialize in making RAG work smoothly with high quality results and reduced hallucinations. We also address a number of other regulated industry requirements like access control, explainability, data privacy, and many others.” Amr Awadallah Co-Founder & CEO at Vectara. Co-Founder & former Chief Technology Officer at Cloudera (multi-billion dollar IPO) Advisor at SparkLabs Saudi Arabia & SparkLabs AI
The Three Types of AI From Narrow to Superintelligence Al that specializes in a single task (e.g., language translation, facial recognition). ARTIFICIAL NARROW INTELLIGENCE (ANI) Al with the ability to understand, learn, and apply knowledge across a wide range of tasks like a human. ARTIFICIAL GENERAL INTELLIGENCE (AGI) Al that surpasses human intelligence in all aspects, including creativity, problem- solving, and emotions. ARTIFICIAL SUPERINTELLIGENCE (ASI)
Artificial General Intelligence (AGI) is generally believed to be 5 or more years away from becoming a reality. But this is often a moving target when asking various “experts” such as Elon Musk who recently said it will be achieve by the end of 2026. Three Types of Artificial Intelligence Estimates to Achieving Artificial Superintelligence (ASI) Estimated Time of Arrival Ray Kurzweil (futurist) Approximately by 2045 (Singularity prediction) Elon Musk ASI could come within 5–10 years, if unchecked Demis Hassabis (DeepMind) AGI is achievable within a decade, ASI afterwards OpenAI researchers (2023 survey) 10–50 years, median estimate around 30 years Kree Supreme Intelligence (Marvel Comics)
AI Lifecycle The AI lifecycle is a structured, iterative process guiding the development, deployment, and maintenance of AI systems. It begins with the Design Phase, where the problem is clearly defined, relevant stakeholders are engaged, and data is collected, explored, and prepared. This foundational stage ensures alignment with business objectives, feasibility, and ethical considerations. Next is the Develop Phase, where AI models are built and evaluated. This includes choosing the right algorithm, designing model architecture, training with high-quality data, and assessing performance using appropriate metrics. Techniques such as regularization, hyperparameter tuning, and cross-validation are applied to optimize results. The final stage is Deploy Phase, where the trained model is integrated into production environments. Here, the model is exposed to real-world data and continuously monitored to detect drift, performance degradation, or anomalies. This lifecycle ensures AI solutions remain effective, secure, and aligned with organizational goals, while enabling retraining and updates as needed through feedback loops.
Basic Computing of Artificial Intelligence WHAT IS A TENSOR? A tensor is a mathematical object that can be viewed as an array of data. The rank of a tensor simply refers to the number of dimensions in the array. In the context of data science, tensors are multi-dimensional arrays of numbers that represent complex data. They are the fundamental data structures used in machine learning and deep learning frameworks. DEFINITION OF A TENSOR In a strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector space. However, in machine learning, the term "tensor" informally refers to two different concepts that organize and represent data. Data may be organized in a multidimensional array (M-way array) that is informally referred to as a "data tensor". This data can be analyzed using artificial neural networks or tensor methods. APPLICATIONS OF TENSORS Tensors are particularly useful for handling complex data such as images, audio, and text. For example, a color image can be represented as a 3D tensor with dimensions corresponding to height, width, and color channels (red, green, and blue). Similarly, a video can be represented as a 4D tensor with time being the additional dimension.
Let’s create an example of the above content through natural language processing. Hi King Hi Queen Hi Jack When there are words, the word is expressed in vector by one-hot encoding as follows Since the word is expressed as a vector, it seems that the sentence can be expressed as a matrix based on the vector of the word. Example The sentence is expressed as a matrix as above. However, we usually input a corpus as input to the neural network, and the corpus can be expressed as follows: hi king hi queen hi jack [ [[1,0,0,0], [0,1,0,0] ], [[1,0,0,0], [0,0,1,0]] , [[1,0,0,0], [0,0,1,0]] ] The words in the above corpus are composed of 4 dimensions. Each sentence is composed of 2 words. Since there are a total of 3 sentences, this model can be considered as a 3-dimensional tensor of size (3, 2, 4) Basic Computing of Artificial Intelligence
When extracting objects such as people, animals, and objects from an image, a huge number of convolutions (overlapping integrals) occur. The convolution operation itself consists of a very large number of multiplications. Convolution A key operation required for Al inference or learning is tensor convolution (overlapping integral) operation. This convolution operation allows us to roughly determine the shape of the border in the image along the direction specified by the filter. In deep learning, this task is called feature extraction (a method of distinguishing objects using a pattern recognition algorithm). The learning process of a deep learning model involves modifying the weight of each filter to make the feature map (resulting value resulting from the calculation) through multiple convolution operations as similar to the correct answer as possible.
“Game AI has been stuck in the past for decades. Traditional systems rely on rigid scripts and predetermined responses, but modern AI unlocks something fundamentally different: characters that think, adapt, and form genuine connections with players. At Glade, we're building the infrastructure to make this possible at scale. Our flagship product, GladeCore, is a lightweight, on-device AI system that eliminates cloud dependency, delivering ultra-low latency AI characters with zero variable costs and no privacy concerns for developers. It's battle-tested in our award-winning game Cursed Crown, and we're now helping other studios create the next generation of truly intelligent games.” Daniel Fang Founder & CEO at Glade (YC S23)
CPU vs GPU vs NPU The fundamental difference between CPU, GPU and TPU is the way these circuits are engineered and the way they process the instructions. The below diagram explains how these three chips do arithmetic calculations. CPU which is designed to handle scalar calculations can do 1x1 data unit. On the other hand, GPUs are specifically designed to do vector calculations that is 1xN data unit. NPU does tensor calculations which can go up to NxN calculations. When we look from the no. of operations these chips can handle, CPU can handle tens of operation per cycle while GPU can handle tens of thousands of operation per cycle and NPU can handle up to 128000 operations per cycle. CPU: Parallelized scalar multiplication GPU: Parallelized vector multiplication NPU: Parallelized tensor multiplication
The Advantages of Using NPUs NPUs are faster and more cost-effective than GPUs The tensor processing capabilities of NPUs are specifically designed for efficient execution of tensor operations, leveraging specialized tensor cores to accelerate the computation of deep learning models. While CPUs and GPUs can also perform these operations, NPUs complete them much faster and with significantly lower energy costs Enhanced Memory, Reduced Power Use, and Smaller Size The NPU is specifically designed for artificial intelligence and machine learning tasks. This memory is integrated within the NPU chip itself, which helps reduce data transfer times and lower power consumption compared to systems that use external memory. Additionally, the efficient use of memory space allows for smaller chip designs. These features enable faster and more efficient AI processing through NPUs. The compact form factor also makes NPUs particularly well-suited for use in on-device AI applications, such as smartphones and AR/VR devices. SAPEON’s NPU outperformed GPUs in the MLPerf AI Datacenter Benchmark Test Source: SAPEON’s X330 AI NPU Inference Chips are Now More Important Than Learning Chips In particular, NPUs are becoming increasingly important in the inference market. At the point of launching AI services, what matters most for service companies is no longer chips designed for learning; instead, the focus has shifted to chips for inference. The applications being developed are diverse, including image generation, disease detection (such as cancer and tumors), and assessments of loan repayment capabilities. The use of specialized inference chips tailored to each application inevitably improves the quality of the services provided.
Data Without data, you're just another person with an opinion. Data is the foundation of modern artificial intelligence, enabling machines to learn, adapt, and make informed decisions. AI systems require vast amounts of structured and unstructured data, including text, images, audio, and sensor readings, to function effectively. High-quality data enhances model accuracy, while insufficient or biased data can lead to poor performance. From training large language models to enabling real-time decision-making, data serves as the fuel that powers innovation, personalization, and automation in AI. Synthetic Data: A Solution to Data Limitations AI systems rely heavily on vast amounts of high-quality data to perform effectively. However, real-world data is often constrained by privacy regulations, high acquisition costs, and limited availability—particularly in sensitive sectors like healthcare and finance. To overcome these challenges, synthetic data has emerged as a promising alternative. Generated through algorithms rather than collected from real-world events, synthetic data can closely mimic the statistical properties of real data without compromising individual privacy. It is increasingly used to augment training in fields like autonomous driving, natural language processing, and medical imaging, especially where data is scarce or sensitive. Synthetic data enables customizable, diverse datasets and allows simulation of rare or risky scenarios. However, its utility depends on quality, realism, and variability. Poorly generated data may reduce model performance, and validation against real data remains essential. As tools advance, synthetic data will play a growing role in scalable and ethical AI development.
Global Market Analysis
The global artificial intelligence (AI) market has seen unprecedented growth over the past decade, driven by rapid technological advancements and increasing investment. AI is transforming industries by enhancing efficiency, automating processes, and enabling data-driven decision-making. As businesses and governments continue to adopt AI solutions, the market is expected to expand further, shaping the future of innovation and economic development. Global AI market
Global AI market
Introduction to the Global AI Market Artificial Intelligence (AI) has experienced exponential growth, driven by rapid advancements in machine learning, robotics, and automation. As industries increasingly integrate AI-driven solutions, the market is expanding at an acc elerating pace, with key sectors like machine learning, natural language processing, and autonomous systems lea ding the way. The adoption of AI is no longer confined to tech companies—industries such as healthcare, finance, manufacturing, and transportation are leveraging AI to enhance efficiency and innovation. With continuous invest ment and breakthroughs in AI hardware and software, the global AI market is expected to sustain its upward traject ory, reshaping economies and industries in the years to come.
Global AI market Drivers • The market is experiencing growth due to an increasing demand for faster and more reliable hardware, prompted by the vast quantities of data generated daily. • The necessity for highly parallel architectures and specialized hardware to manage high-performance computations essential for neural network operations is fostering the adoption of accelerators in the market. • The extensive integration of artificial intelligence software in various industries, which enhances business processes, improves decision-making efficiency, and drives innovation, is contributing significantly to market expansion. • The escalating requirement for expert guidance and comprehensive support in deploying and utilizing artificial intelligence solutions is boosting the demand for professional services within the market. • The importance of support and maintenance services is growing, driven by their role in ensuring the long-term success of artificial intelligence initiatives across various businesses.
Global AI market The artificial intelligence (AI) market is categorized by three primary offerings: hardware, software, and services. Each segment addresses different facets of AI needs and contributes to the overall expansion of the artificial intelligence market. AI Market Segmentation Hardware: This segment includes accelerators, processors, memory, and network components. Processors are further divided into microprocessor units, graphics processing units, field-programmable gate arrays, and other types of processors. The demand for advanced hardware that mimics the intelligence and efficiency of the human brain is a significant driver for growth in this segment. Software: The software sector of the AI market is segmented by type and deployment method. Software types include pre-trained models, customizable AI solutions, edge AI, and AI marketplaces. Deployment methods are categorized into cloud-based and on-premises solutions. Services: This offering is split into professional and managed services. Professional services encompass consulting, deployment and integration, as well as support and maintenance services.
Market Size Global AI market Source: Grand View Research
and architectural designs to meet the growing processing requirements of artificial intelligence systems. This competition in the market has led to continuous product launches and advancements in hardware development and software platforms for machine learning algorithms and programs. The artificial intelligence hardware market is projected to experience significant growth, driven by the increasing need for high- performance hardware platforms capable of running complex artificial intelligence software. The presence of major artificial intelligence companies in North America has positioned the region as a key market for AI-related hardware. Global AI market The artificial intelligence market has been segmented, by hardware, into accelerators, processors, memory, and network. The demand for faster and more reliable hardware in the field of AI is increasing due to the massive amount of data being generated daily. Established companies such as Intel, IBM, NVIDIA, and Samsung, as well as startups, are actively developing new hardware Hardware Source: Grand View Research
”Material handling applications are increasingly adopting automation to address global labor shortages. AI is pivotal in unlocking solutions to previously unsolved automation challenges across the supply chain—from factories and large regional distribution centers to smaller warehouses. One of the key untapped problems, the loading and unloading of truck trailers, can now be addressed thanks to recent AI advancements. Gideon revolutionizes the loading and unloading bay with autonomous forklifts, leveraging AI for human-like perception and real-world execution.” Josip Cesic Co-founder & CEO at Gideon *Gideon raised $48 million from Toyota Industries, Prologis Ventures, Koch Disruptive Technologies, DB Schenker, Taavet Hinrikus (Co-founder of TransferWise) and others.
The artificial intelligence market for hardware has been segmented, by processor, into graphics processing units (GPUs), microprocessing units (MPUs), field programmable gate arrays (FPGAs), and other processors. The adoption of processors in the field of AI has witnessed significant growth as AI applications continue to advance. GPUs, known for their parallel processing Global AI market capabilities, have become a popular choice for accelerating AI workloads. They offer high performance and efficient execution of complex algorithms. MPUs and FPGAs are also being widely adopted in AI, providing specific advantages such as flexibility and power efficiency. The increasing demand for AI- driven solutions and the need for faster processing speeds have driven the adoption of these processors. As AI continues to evolve, processors play a vital role in enabling the development and deployment of advanced AI models and applications. Processors Source: Grand View Research
SEMICONDUCTOR COMPANIES MARKET REVENUE WORLDWIDE FROM 2009 TO 2023
INTEGRATED CIRCUIT (IC) DESIGN COMPANIES REVENUE WORLDWIDE FROM 2017 TO 2023, BY QUARTER
NVIDIA SPECIALIZED MARKET REVENUE WORLDWIDE FROM FISCAL YEAR 2019 TO 2024, BY QUARTER
AMD SEGMENT REVENUE WORLDWIDE FROM 2021 TO 2023, BY QUARTER
INTEL QUARTERLY REVENUE FROM 2021 TO 2023, BY SEGMENT
“With the explosive growth of AIGC, global demand for cloud AI computing has surged, placing immense pressure on energy infrastructure and carbon neutrality. As multimodal AIGC expands, challenges like latency, bandwidth, and privacy will increasingly limit the use of cloud AIGC. The shift of AI compute from the cloud to the edge is an inevitable trend. Kneron focuses on creating efficient edge AI inference chips that use less power and cost less. We have successfully launched a range of edge AI products across key industries including security, autonomous driving, and consumer electronics, leading in several specialized markets. Our next milestone is to launch next-generation chips that run complex AIGC models locally—enabling faster, more secure, and energy- efficient AI experiences.” Albert Liu Co-Founder & CEO at Kneron *Kneron has raised over $200 million from Sequoia China, Qualcomm, Horizon Ventures, Foxconn and others.
Global AI market Microchips Nvidia’s Dominance in the GPU Market The GPU (Graphics Processing Unit) market is a rapidly evolving segment of the semiconductor industry, playing the leading role in artificial intelligence (AI). NVIDIA’s dominance in the GPU market is the result of a multi-decade strategy that combined its early bets on AI, technical foresight, and an integrated software ecosystem. Source: IoT Analytics
Astera Labs & AI Connectivity Astera Labs specializes in high-speed connectivity solutions for AI cloud data centers, ensuring seamless AI infrastructure performance. Their Intelligent Connectivity Platform accelerates data transfer speeds, enhancing AI chip efficiency. Advantages By improving AI chip-to-chip connectivity, Astera Labs optimizes data movement across AI infrastructures. This reduces bottlenecks in AI workloads, ensuring efficient scaling for next- generation AI applications. Market Impact Astera Labs' IPO in 2024 saw shares surge by over 70%, reflecting strong investor confidence in AI semiconductor infrastructure. With a valuation exceeding $16 billion, the company is positioned as a key player in AI hardware despite not yet reaching profitability. Global AI market Microchips
SK Hynix’s HBM: Powering AI's Future High Bandwidth Memory (HBM) is essential for AI acceleration, offering significantly higher bandwidth and lower power consumption compared to traditional DRAM. It is designed to handle the intensive computational demands of modern AI models. NVIDIA integrates SK Hynix’s HBM to enhance AI model training and inference, reducing latency and increasing efficiency. Advantages HBM supports massive parallel processing, enabling faster computations crucial for deep learning. It allows quicker access to large datasets, improving AI workload efficiency. Additionally, its lower energy consumption makes AI chips more sustainable for large-scale applications. Impact on AI Hardware The integration of HBM in AI chips ensures that AI infrastructures operate at peak efficiency. It enhances GPU performance, allowing AI systems to process complex models with greater speed and scalability. Global AI market Microchips
Groq LPUs (Language Processing Units) Groq’s Language Processing Unit (LPU) introduces a new approach to AI hardware, providing an alternative to GPUs. Unlike GPUs designed for general parallel computing, LPUs focus on low-latency, high-speed AI inference, making them highly efficient for real-time AI applications. Advantages LPUs achieve up to 13 times faster inference speeds than Microsoft AI chips, improving responsiveness for AI-driven workloads. They optimize token processing speeds, enhancing AI assistants and other machine learning applications. LPUs reduce computational bottlenecks, making them ideal for large language models such as Llama 2 and GPT models. Future Potential As AI applications continue to evolve, Groq’s LPU technology is likely to play a significant role in shaping the future of AI hardware. By challenging the dominance of GPUs and TPUs in inference workloads, LPUs present a new class of AI accelerators focused on efficiency and speed. Global AI market Microchips
Global AI market Microchips AI Computing Moving to the Edge The current energy consumption of cloud AI is unsustainable unless the architecture and power availability changes, which is why some analysts believe more of AI will be moved to the edge via microchips. There is not enough energy in the current grid to meet AI’s projected needs.
Global AI market Microchips
“In the age of vibe coding, ephemeral software is becoming a competitive edge. These are tools built fast, used intensely, and thrown away without ceremony. At Setset, we build and discard internal AI tools weekly to solve product image challenges in e-commerce—delivering high-quality photography and video at scale, tailored to each brand’s aesthetic and technical setup. With faster timelines and lower costs, these bespoke systems outperform one-size- fits-all platforms. Software is no longer something you buy and consume, but something you create on the fly to meet the need of the moment.” Nils Westerlund Founder at SetSet.ai Former Growth Team Lead & Product Lead at Runway AI Venture Partner at SparkLabs AI
Artificial intelligence software utilizes advanced techniques like machine learning, deep learning, and natural language processing to mimic human intelligence. These programs are adept at analyzing complex data, recognizing patterns, and making predictions. They support a variety of applications, from chatbots and virtual assistants to autonomous systems, leveraging complex networks Global AI market to process data, learn, and enhance performance over time. Continuous improvements in AI software have led to innovations in fields like image recognition and speech synthesis, improving human- computer interactions and enabling personalized experiences. This software is transforming industries by streamlining decision-making and fostering innovation in sectors such as healthcare, finance, and manufacturing. Software Source: Grand View Research
Pre-trained models Pre-trained models have meticulously crafted models that have undergone rigorous training on vast datasets to excel at specific tasks, such as natural language processing, image recognition, or speech recognition. These models are endowed with learned features and patterns derived from the training data, negating the necessity to train models from scratch. To attain remarkable accuracy, pre-trained models are meticulously trained on extensive, high-quality datasets utilizing state-of-the- art techniques. Leveraging pre-trained models empowers developers and researchers to save substantial time and resources that would otherwise be required for training models from the ground up. This accessibility allows organizations and individuals with limited resources to achieve exceptional performance levels without extensive data or expensive computational infrastructure. Customizable AI Customizable AI is a specialized software type in the artificial intelligence market that allows users to customize and adapt AI models and algorithms to meet specific requirements. Unlike pre-packaged solutions, it offers flexibility and control over the underlying algorithms, data processing pipelines, and model architectures. With customizable AI, businesses and developers fine-tune models to their unique datasets, problem domains, and objectives. It tailor the training process, incorporate domain-specific knowledge, and adapt models to changing circumstances. Customizable AI empowers organizations to develop highly specialized and domain-specific artificial intelligence applications, improving accuracy and performance. It finds applications in healthcare, finance, manufacturing, and customer service, where customized AI solutions are vital for optimal results. It also provides a powerful tool for organizations to leverage AI's potential while addressing their specific needs and challenges. Global AI market Software
Edge AI Edge AI software encompasses a diverse range of machine learning algorithms executed on local hardware devices. Its purpose is to enable the deployment of AI algorithms directly on a device or machine. With Edge AI software, users access real-time data without the need for external systems or internet connectivity, as the processing occurs locally on the device itself. This decentralized approach allows faster and more efficient data processing, eliminating the dependency on network connections or cloud-based servers. By leveraging Edge AI software, organizations achieve real-time insights and perform AI tasks locally, enhancing responsiveness and privacy and reducing reliance on external connections. AI Marketplaces AI marketplaces are digital platforms within the AI market that facilitate the discovery, acquisition, and exchange of AI models, algorithms, and services. These marketplaces serve as centralized hubs for developers, data scientists, and businesses to access and leverage a wide range of AI resources. In AI marketplaces, users explore and obtain pre-built AI models and algorithms, accelerating the development process and reducing the need for extensive customization. Additionally, these platforms enable developers to showcase and monetize their AI creations, promoting collaboration and innovation. AI marketplaces also offer a suite of AI-related services, including data labeling, model training, and consulting, to support organizations in their AI journey. It also provides tools and frameworks for the seamless integration of AI capabilities into applications, empowering businesses to harness the power of artificial intelligence. By fostering knowledge sharing, resource accessibility, and commercialization of artificial intelligence innovations, AI marketplaces contribute to the widespread adoption and democratization of artificial intelligence technologies across industries. It plays a vital role in driving the advancement and application of AI, shaping the future of intelligent systems. Global AI market Software
AI software is deployed through various methods, including on-premises and cloud-based deployments. The choice depends on factors such as data security, available infrastructure, and workload complexity. On-premises deployment offers direct control and customization within the organization's infrastructure, suitable for strict security requirements. Cloud-based deployment utilizes remote servers, providing scalability, accessibility, and managed infrastructure. It is ideal for organizations seeking Global AI market Software flexibility and resource efficiency. The deployment method is chosen based on the organization's needs, ensuring optimal control, scalability, and resource utilization in AI software deployment. Source: Grand View Research
The services segment in the AI market encompasses both professional and managed services, playing a crucial role in the entire AI life cycle, including product upgrades, maintenance, training, and consulting. In the digital economy era, enterprises are actively seeking innovative solutions to enhance their business operations and optimize resources. Artificial Intelligence vendors offer diverse services, including support and maintenance, deployment and integration, and consulting, to effectively execute and manage the life cycle of artificial intelligence solutions. The growth of the services segment is primarily driven by the increasing complexity of artificial intelligence operations and the widespread adoption of artificial intelligence technologies across industries. Businesses increasingly rely on services that streamline operations, optimize resource utilization, and drive growth and profitability. Global AI market Software Source: Grand View Research
“’Text-to-software’ platforms are compressing the idea-to- MVP timeline from months to days. Builders such as Lovable and Bolt.new already let both technical and non-technical users chat their way to functional sites and apps, leading to a rapid recent increase in overall throughput for new software and products. At the same time, tools like Cursor are amplifying individual engineers and letting leaner teams ship more with fewer hires. People are building more and faster than ever. Cosmic takes the next logical step: our agent not only writes you a beautiful front-end but also handles your entire backend, like auth, database, payment processing, and more, so even non-technical users can launch, monetize, and iterate in a single weekend.” Sam Park Co-Founder & CEO at Cosmic (YC F24)
This section covers the segmentation of the artificial intelligence market based on vertical. The segments studied include BFSI, retail & eCommerce, automotive, transportation and logistics, government & defense, healthcare & life sciences, telecom, energy & utilities, manufacturing, IT/ITeS, media & entertainment, and other verticals (education, travel and hospitality, and construction). Global AI Market by Vertical Source: Grand View Research
While analyzing AI market offerings provides insights into the types of solutions available—such as software platforms, hardware components, and professional services—examining the market by vertical reveals how these solutions are being applied across different industries. This perspective highlights the growing demand for industry-specific AI applications, with sectors like healthcare, finance, and manufacturing increasingly adopting tailored AI tools to address their unique challenges and opportunities. Global AI Market by Vertical Source: Grand View Research
AI-powered systems in banking apps and services have ushered in a new era of customer-centricity and technological advancement. Through advanced algorithms and data analysis, banks have achieved increased productivity and cost reduction by automating processes and leveraging vast amounts of data for informed decision-making. Implementing intelligent algorithms has significantly enhanced fraud detection capabilities, enabling real-time identification and preventing fraudulent activities. In the financial market, AI-driven trading systems have optimized investment strategies by analyzing large-scale market data, driving efficiency and accuracy. The insurance industry has also benefited from artificial intelligence, as underwriting processes have been streamlined and claims management has become more efficient. Global AI Market by Vertical: BFSI (Banking, Financial Services, and Insurance) Source: Grand View Research
Artificial intelligence applications in retail and e-commerce have transformed the customer experience, supply chain operations, and business efficiency. AI-powered systems enable retailers to analyze vast amounts of customer data, unveiling valuable insights that help understand buying patterns and behaviors. It allows personalized shopping experiences and tailored product recommendations, enhancing customer satisfaction and loyalty. In e-commerce, artificial intelligence optimizes search functionalities, improves product suggestions, and streamlines the ordering and delivery process, leading to a seamless online shopping experience. The competitive nature of the retail and e-commerce sector makes differentiation crucial, and AI offers businesses a significant edge. By leveraging artificial intelligence, retailers and eCommerce platforms make faster, data-driven decisions, optimize operations, and improve profitability. Global AI Market by Vertical: Retail & Ecommerce Source: Grand View Research
AI is revolutionizing the automotive, transportation, and logistics industries by introducing advanced solutions to enhance efficiency, safety, and overall operations. In automotive manufacturing, AI-driven robotics and automation optimize production processes, ensuring higher quality and precision. Predictive maintenance powered by artificial intelligence minimizes vehicle downtime and maximizes performance. In transportation and logistics, artificial intelligence facilitates route optimization, fleet management, and demand forecasting. By analyzing vast data sets, artificial intelligence enables businesses to streamline operations, reduce fuel consumption, and improve supply chain efficiency. Autonomous vehicles benefit from artificial intelligence technologies, enhancing safety through advanced driver assistance systems and self-driving capabilities. Additionally, AI-driven data analytics and predictive modeling empower intelligent decision-making, risk assessment, and supply chain optimization. Global AI Market by Vertical: Automotive, Transportation & Logistics Source: Grand View Research
The government & defense sectors are capitalizing on AI-powered solutions to address complex challenges, enhance service delivery, facilitate decision-making processes, and strengthen national security. With the increasing adoption of artificial intelligence technologies, these sectors also focus on implementing AI-driven governance solutions. Governments worldwide are forming partnerships with major technology companies to reinforce ethics and governance practices. In the collaboration between the UAE government and Microsoft, efforts are focused on developing artificial intelligence programs and frameworks to ensure trustworthy AI deployment. Global AI Market by Vertical: Government & Defense Source: Grand View Research
Biggest concerns about AI… “I believe that the unknowns of AI’s future capabilities and its impact on humans, ranging from privacy, bias, and work to just daily life, is what the industry needs to address to avoid fear taking over and hurdles to innovation and progress.“ Approach to regulate AI… “There’s no one-off regulation or policy solution but a never- ending approach of constant small, nimble steps by regulators and policymakers to adjust to seemingly lightning- speed AI progress. Tech giants are part of the solutions and must be held accountable for developing and deploying AI ethically and responsibly. It will seem impossible for all participants, but it is the only way forward for AI and society." Spiros Margaris General Partner at Margaris Ventures No. 1 Global Finance & FinTech Influencer; Top AI Influencer & Thought Leader Venture Partner at SparkLabs AI
The healthcare & life sciences sector is experiencing a significant transformation with the integration of AI technologies. Artificial intelligence is revolutionizing healthcare by enhancing patient care, enabling advanced diagnostics, accelerating drug discovery, and improving operational efficiency. In healthcare, AI- powered systems analyze vast amounts of medical data to aid in early disease detection, assist in accurate diagnosis, and provide personalized treatment recommendations. In the life sciences domain, artificial intelligence revolutionizes drug discovery processes by analyzing complex biological data, predicting drug efficacy, and identifying potential targets. Global AI Market by Vertical: Healthcare & Lifesciences Source: Grand View Research
Artificial intelligence has the potential to revolutionize various aspects of telecom operations, from network management and customer experience to service provisioning and cybersecurity. Telecom companies leverage artificial intelligence to optimize network performance, predict and prevent network outages, and automate routine maintenance tasks. AI-powered virtual assistants and chatbots enhance customer interactions by providing personalized support and resolving queries in real-time. Additionally, AI algorithms are utilized to analyze massive amounts of data generated by telecom networks, enabling companies to gain valuable insights for business decision-making and service enhancements. Global AI Market by Vertical: Telecom Source: Grand View Research
Artificial intelligence applications in this industry range from smart grid management and energy forecasting to demand response and energy optimization. By leveraging AI algorithms, energy & utility companies analyze vast amounts of data, such as energy consumption patterns, weather conditions, and grid performance, to make intelligent decisions and optimize energy generation, distribution, and consumption. AI-powered solutions enable predictive maintenance of energy infrastructure, identify energy theft or anomalies, and enhance grid reliability and resilience. Global AI Market by Vertical: Energy & Utilities Source: Grand View Research
Artificial intelligence is revolutionizing manufacturing processes by enhancing productivity, efficiency, and quality while enabling businesses to remain competitive globally. Traditionally, manufacturing has relied on manual labor and conventional machinery, but with the advent of artificial intelligence, manufacturers are increasingly adopting intelligent systems to automate and optimize their operations. One of the primary drivers behind the adoption of artificial intelligence in manufacturing is the pursuit of operational excellence. Artificial intelligence technologies, such as machine learning, computer vision, and natural language processing, enable manufacturers to analyze large volumes of data in real- time, extracting valuable insights and making data-driven decisions. Global AI Market by Vertical: Manufacturing Source: Grand View Research
Artificial intelligence has emerged as a powerful tool for optimizing various aspects of agriculture, including crop cultivation, livestock management, supply chain logistics, and overall farm operations. By leveraging artificial intelligence technologies, the agriculture industry has the potential to enhance productivity, reduce costs, minimize waste, and mitigate environmental impact. Artificial intelligence in the agriculture industry refers to the utilization of intelligent algorithms and systems to analyze vast amounts of data collected from agricultural processes, enabling farmers and stakeholders to make data-driven decisions. Global AI Market by Vertical: Agriculture Source: Grand View Research
The information technology/information technology-enabled services (IT/ITeS) industry plays a pivotal role in driving innovation and digital transformation across various sectors. With the advent of AI, the industry has witnessed a paradigm shift, presenting significant opportunities for businesses to leverage AI technologies to enhance efficiency, improve customer experiences, and drive competitive advantage. The AI market within the IT/ITeS industry encompasses a wide range of technologies and services that enable machines to simulate human intelligence and perform tasks that typically require human intervention. This includes natural language processing, machine learning, computer vision, robotic process automation, and expert systems. Global AI Market by Vertical: IT/ITES Source: Grand View Research
AI is transforming the media industry by enhancing content creation, distribution, and personalized audience engagement. This integration of AI offers opportunities for businesses to improve operations and deliver engaging entertainment experiences. The media and entertainment AI market includes machine learning, natural language processing, and recommendation systems, which help analyze data, automate tasks, and provide insights for content innovation. AI-driven recommendation systems personalize content based on user data like viewing habits and interactions, enhancing consumer satisfaction. Global AI Market by Vertical: Media & Entertainment Source: Grand View Research
The advent of AI has revolutionized industries such as construction, education, travel, and hospitality, enhancing efficiency, productivity, and customer experiences. In construction, AI technologies automate project management, enhance safety, and optimize resource use, deploying AI-enabled drones and robotics for tasks like inspections and material transport while facilitating data-driven decision-making. In education, AI introduces personalized learning through intelligent tutoring systems, virtual and augmented reality for immersive experiences, and chatbots for round-the-clock student support, also aiding educators in administrative tasks. In the travel sector, AI-driven chatbots improve customer service by assisting with bookings and offering personalized recommendations based on data analysis. Similarly, in hospitality, AI enhances guest experiences through personalized services via chatbots, virtual assistants, and voice recognition technologies, streamlining operations and improving satisfaction. Global AI Market by Vertical: Others Source: Grand View Research
When will we achieve AGI? “Achieving artificial general intelligence (AGI) may happen sooner than many anticipate, but perhaps not as quickly as the most optimistic forecasts suggest. My journey into computer science began in the late 1970s, with a focus on artificial intelligence (AI). At that time, AI was a broad concept often associated with advanced, human-like intelligence as portrayed in science fiction, such as HAL from ‘2001: A Space Odyssey’. This era lacked the clear distinction we see today between AGI and generative AI. My initial work in AI felt more theoretical than practical, leading me to shift my focus to computer graphics. However, my interest in AI persisted, especially during my tenure at the Federal Aviation Administration, where I encountered AI's application in predicting hazardous weather conditions using LISP on Symbolics machines—a form of what is now known as machine learning. Over the years, I've observed AI's evolution from a vantage point of informed curiosity. A prevalent, though unspoken, belief among my peers was that a milestone towards achieving "true AI" or AGI would be the capability for information to self-learn and self-modify. The recent advancement in large language models (LLMs) and generative AI over the past 18 months represents this critical step, indicating that we are on the brink of an exponential surge in technology. Based on these developments, I project that AGI could emerge in some form within the next 5 to 8 years, marking a significant milestone in the journey of AI.” Rob DeMillo Co-founder & CEO at Sophia Space Former CTO at Skidmore, Owings & Merrill (SOM) Former CTO at Nimble Collective Former CTO at Discovery Digital Networks (formerly Revision3) Former VP of Engineering at VeriSign (formerly m-Qube) Venture Partner at SparkLabs Global
This map provides a comprehensive overview of the global AI ecosystem by categorizing major companies into functional tiers such as hardware, network, software, security, platforms, services, and cloud. It primarily highlights large, established players like NVIDIA, IBM, Oracle, Microsoft, and AWS, organizing them by their core role in the AI value chain. Global AI Market Map (by Market & Market)
Generative AI Market Map (by Sequoia Capital)
Generative AI Infrastructure Stack (by Sequoia Capital)
“While most people think AI is plug-and-play, industrial and physical AI tell a very different story. In sectors like advanced manufacturing or smart warehouses, production optimization and robotics control can’t be solved with off-the-shelf models — they require simulation, iteration, and precision. That’s why we built MetAI: to turn static blueprints into high-fidelity, simulation- ready 3D digital twin environments where AI agents can learn, optimize, and validate before ever touching the real world. Our infrastructure converts CAD and 2D drawings into SimReady OpenUSD environments, reducing deployment risk and unlocking scalable, vertical-specific AI systems. Backed by NVIDIA and born in Taiwan’s manufacturing heartland, we’re building globally scalable infrastructure for the next era of AI — where intelligent agents don’t just read, but move, act, and operate in the physical world. Just as LLMs need text, physical AI needs digital worlds — and that’s what we provide.” Daniel Yu Co-Founder & CEO at MetAI *first startup in Taiwan to receive an investment from Nvidia
AI Enterprise Market Map (by Lightspeed Capital)
Voice AI Market Map (by Bessemer Venture Partners)
The CB Insights AI 100 map offers a more granular, 2024-focused breakdown of both horizontal and vertical AI companies. Vertical AI refers to startups specializing in specific industries like healthcare, aerospace, and manufacturing, while horizontal AI companies provide general-purpose solutions such as computer vision, productivity tools, and DevOps. AI 100 Market Map (by CB Insights)
AI Market Impact
Rise of Agentic AI & AI Agents Agentic AI is the artificial intelligence systems that has the ability to independently make decisions, perceive the tasks and environment at hand, display iterative problem solving, and act in pursuit of a goal—often across multiple steps, environments, and tools—without requiring constant human intervention. Traditional AI is typically constrained to specific tasks and reactive while agentic AI operates with a degree of self-direction and dynamically interacts with humans and its surroundings to secure its outcomes. Agentic AI systems primarily differ from generative AI models, such as ChatGPT and Google's Gemini, since they are designed to make decisions versus simply making content. Additionally, agentic AI works towards established goals and tasks while GenAI models require human prompts. The product form or software entity of an agentic AI platform is an AI agent.
Example Architecture of an Agentic AI System
Agentic AI Market Map (by Market & Market)
“Why is vertical-specific AI important? Big models are powerful generalists, but you don’t need a Ferrari to deliver pizza. Every time you use an expensive foundation model to sort data transactions or tag tickets, you’re paying a high price for output that’s correct only a fraction of the time, because it’s not trained in the nuances of your domain. Levro’s AI reinforcement learning platform helps companies train a custom model tailored to their exact workflow (financial assessments, product recommendations, support triage, etc.) that can run at a fraction of the cost and with higher accuracy. No AI research team required.” Cathy Han Co-Founder & CEO at LevroAI (YC S21) Co-Founder & former CEO at 42 Technologies (YC W14)
AI Agent Growth “AI agents are gaining traction quickly across an array of business applications—and the market for AI agents is expected to grow at a 45% CAGR over the next five years. As AI agents become commonplace—and they will— humans will work closely with them as teammates. AI agents will be onboarded, just like human workers, to learn roles and responsibilities, access relevant company data and business context, integrate into workflows, and support the humans’ responsibilities.” - Boston Consulting Group
AI Agent Growth
AI Agent Growth AI agents are not just products but are evolving into strategic platforms where developer and user loyalty will create the next billion user hubs or extend the leadership of the trillion dollar tech giants. Google, Amazon, and Microsoft are focused on this strategy as shown by the launch of Azure AI Agent, Vertex AI Agent Builder and Amazon Bedrock Agents. Creating developer lock-in is now a mad, mad race. Apple’s delay into agent development tooling is concerning. Maybe their closed culture is not the approach for this new AI reality?
AI Agent Market Map (by CB Insights) “While AI copilots have already made inroads across industries, the next evolution — autonomous agents with greater decision-making scope — is arriving quickly. AI agent startups raised $3.8B in 2024 (nearly tripling 2023’s total), and every big tech player is already developing AI agents or offering the tooling for them. Implications for enterprises will be far-reaching, from altering workforce composition (with new hybrid teams of humans and AI agents) to maximizing operational efficiency through full automation of routine tasks.” - CB Insights
“Every major technology shift adds a new layer of abstraction. In the past, requesting annual leave meant walking around the office collecting signatures from your manager and HR. Then ERP systems arrived, and you could submit your request through software. The system took care of the workflow, and you no longer had to think about the process. Today, the problem is complexity. Large enterprises run hundreds of applications. A typical Fortune 500 company uses around 370. Each comes with its own interface, logic, and rules. This leads to two issues: growing dependence on system experts and rising operational costs. AI represents the next step in our evolution. With an agentic platform, your intent becomes action. You no longer need to know which system is behind the scenes. You ask, and it gets done. Whether it's accessing information, starting a workflow, or managing permissions, everything happens through one unified interface. This is what we are building with HUMAIN ONE. One interface to control your entire enterprise. The future is closer than most people think, and it will redefine how work gets done.” Saejong Lee VP and Head of Product for HUMAIN OS Former Director of Product at Aramco Digital Venture Partner at SparkLabs Saudi Arabia
AI agent is the product of an agentic AI platform. It is software that perceives its environment, independently makes decisions and executes on its goals and stated tasks. Over the next 3 years, AI agents will have a transformative impact on industries where workflows are repetitive, information-rich, and ripe for automation or augmentation. Here are the top ten industries that will be impacted the most: Industries Impacted the Most within 3 Years 1. Enterprise Software & B2B SaaS AI agents can automate internal workflows, sales ops, customer success, recruiting, and other enterprise functions (e.g., CRM updates, meeting summaries) 2. Customer Service & Contact Centers AI agents can reduce costs by replacing or augmenting Tier 1 and Tier 2 support, and operate 24/7 with high consistency
Industries Impacted the Most within 3 Years 3. Healthcare Diagnostic assistants, documentation agents, and scribe agents improve back office efficiency and reduce physician burnout. Hopefully reduce the overall cost of our inefficient healthcare system. 4. Finance & Insurance AI agents can analyze documents, manage risks, detect fraud, and create numerous efficiencies across banking and insurance platforms. The cost savings some financial and insurance companies experienced using blockchain will seem miniscule to the impact of AI agents. 5. Education Learning agents and personalized AI tutors can scale teaching while better adapt to learning styles of each student. Additionally this can better support underserved populations.
Industries Impacted the Most within 3 Years 6. Retail & E-Commerce AI agents will optimize logistics, better personalize shopping experiences, provide instant Q&A, dynamically manage pricing and other tasks. Will this level the playing field for small and medium players or further empower largest companies? 7. Cybersecurity AI agents will continuously monitor threats and attacks, patch, and simulate attacks for improvements. This will allow threat detection and response to scale better 8. Legal & Compliance AI agents can generate legal drafts, execute on mundane legal writing, analyze contracts, and help monitor regulatory changes
Industries Impacted the Most within 3 Years 9. Media, Marketing & Content Creation Agents can generate social media posts, assist in advanced content creation, create campaign strategies, make content calendars, analyze audience responses in real-time and other tasks to boost the efficiencies of marketing firms and professionals. 10. Software Development AI agents can map out products and applications, code and build, test applications and deploy software autonomously.
“Research in new training and inference improvements continue to quickly leap from research to production, driven by a demand for better models from existing data, and from upper bounds on available computing power. The highest quality frontier models might continue to need hyperscalar-level investments, but general-purpose models that are ‘good enough’, and high quality domain-specific models, are rising fast. Privacy-sensitive and regulation-heavy verticals like legal and heathcare need LLM solutions they can put on-prem or on the cloud of their choice, and smart startups are addressing that need. Open weights models, and even truly open source models and software and open data, are driving much of that innovation and commoditization, rhyming with the impact open source has long had on the IT industry. Better structured open data sets, and greater legal and regulatory clarity around training data (you can use it, but you need to obtain it legally), are where corporations, non-profits, and even governments are realizing they can turbocharge model development through collective action.” Brian Behlendorf Former Chief AI Strategist at The Linux Foundation Primary Developer of the Apache Web Server Board Member at Mozilla Foundation, Electronic Frontier Foundation & Filecoin Foundation Former Managing Director at Mithril Capital Advisor at SparkLabs Global Ventures & Venture Partner at SparkLabs AI
Generative AI Enterprise Healthcare Finance Cybersecurity Transportation Agriculture General Market Impact
Generative AI As LLMs become more and more powerful, Generative AI developments will continue to advance rapidly. We expect to see: Multimodal models: going forward, models are expected to process text, audio, video and images simultaneously Explosive growth in use cases around generative audio and video, generative design, education, healthcare, just to name a few Robust government regulations that aim to balance innovation and technology advancement with privacy, security and well- being of citizens
“Enterprise AI is entering a critical phase, where precision, trust, and contextual intelligence are essential. The real opportunity lies in building AI systems that deeply understand language, intent, and interaction patterns at scale, especially for high- impact business functions like Customer Experience (CX). At Lucidya, we’ve spent years developing enterprise-grade AI Agents trained on billions of real-world conversations and behavioral signals across sectors and channels. This foundation allows us to deliver AI that is not only multilingual and culturally aware, but also tuned to the complexities of CX in enterprise environments. Delivering this level of intelligence comes with a high bar: compliance, privacy, and trust are mandatory. That’s why we’ve built our platform with security and regulatory alignment at its core, because enterprise AI can’t scale unless it respects data, has safeguards and real business outcomes. I believe the future of enterprise AI belongs to those who can deliver domain-specific intelligence that is scalable, compliant, and trusted.” Abdullah Asiri Founder & CEO at Lucidya *recently closed their US$30 million Series B round to become the largest AI-backed startup in MENA
Enterprise The top three areas over the next 2 years: Data Analytics and Business Intelligence: AI-powered data analytics and business intelligence tools are crucial for enterprises aiming to harness the vast amounts of data they generate and collect. Automation and Robotic Process Automation (RPA): Automation, including RPA, is a significant investment area, focusing on automating routine, repetitive tasks to improve efficiency, reduce errors, and free up human employees for more strategic work. Customer Experience and Engagement: Enhancing customer experience and engagement through AI technologies is a key investment area. This includes chatbots and virtual assistants for customer service, personalized marketing, and recommendation systems.
Healthcare The top three areas over the next 2 years: Diagnostic and Imaging Technology: AI plays a crucial role in improving the accuracy and efficiency of medical diagnostics. By analyzing images from MRIs, CT scans, X-rays, and other imaging technologies, AI algorithms can help detect diseases such as cancer, neurological disorders, and cardiovascular conditions earlier Personalized Medicine and Treatment Optimization: AI is being used to tailor treatment plans to individual patients, taking into account their genetic makeup, lifestyle, and other factors. This includes the development of precision medicine, where AI algorithms analyze vast datasets to identify which treatments are likely to be most effective for specific patient profiles. Operational Efficiency and Patient Care Management: AI investments are also targeted towards improving the operational aspects of healthcare provision. This includes the use of AI for managing patient flow, reducing wait times, and improving resource allocation.
Finance Top impact areas: Algorithmic Trading: AI is increasingly being used in algorithmic trading to analyze large datasets and execute trades at speeds and volumes that are beyond human capability. By leveraging AI, financial institutions can identify market trends, predict price movements, and make trading decisions that optimize returns on investments. Robotic Process Automation (RPA) and Back-Office Automation: Financial institutions are investing in AI to automate routine and repetitive tasks in areas such as account processing, compliance checks, and customer service inquiries. This not only improves operational efficiency and reduces costs but also allows human employees to focus on more strategic and customer- focused activities.
Cybersecurity Top impact areas: Threat Detection and Analysis: AI algorithms are being employed to analyze network traffic, identify unusual patterns, and detect potential threats at an early stage. This includes the use of machine learning models to recognize signs of malware, phishing attempts, and other sophisticated cyber attacks that traditional security measures might overlook. Behavioral Analytics and Anomaly Detection: AI-driven behavioral analytics focus on understanding the normal behavior of users and network devices to identify deviations that could indicate a security threat. This area of investment helps in detecting insider threats, compromised credentials, and advanced persistent threats (APTs) that do not match known malware signatures but exhibit anomalous behavior.
Transportation The top three areas that we will explore are: Autonomous Vehicles: AI is the backbone of autonomous vehicle technology, encompassing self-driving cars, drones, and unmanned aerial vehicles. These AI systems process data from various sensors and cameras in real-time, enabling vehicles to navigate complex environments safely. The development and refinement of autonomous vehicles promise to reduce accidents caused by human error, improve traffic flow, and decrease carbon emissions. Traffic Management and Optimization: AI-driven traffic management systems are being deployed to analyze traffic patterns, optimize traffic lights, and reduce congestion. By leveraging real-time data, these systems can predict and manage traffic flows more efficiently, thereby enhancing urban mobility and reducing travel times. This includes the use of AI in public transportation to optimize routes and schedules based on passenger demand. Predictive Maintenance: In the transportation industry, AI is revolutionizing the approach to maintenance. Predictive maintenance uses AI algorithms to analyze data from vehicle sensors and predict when parts might fail or require maintenance. This proactive approach can significantly reduce downtime, extend vehicle life, and lower maintenance costs by addressing issues before they lead to failures.
“Farmed fish is the fastest growing sector of food production, helping to meet the world's demand for healthy protein, and accounting for over half of the fish produced today. Aquaculture is also one of the most sustainable forms of protein production and helps reduce our dependence on overfishing. To grow, the industry needs a way to monitor and improve the health of fish and its local waters. Aquabyte builds incredibly complex AI models based on underwater cameras, computer vision, and ML models based on fish biology, allowing fish farms to monitor and optimize fish growth and health. This data helps fish farmers make data-driven decisions to produce healthier fish faster at lower cost, higher efficiency, and better sustainability.” Bryton Shang Senior Advisor to the NOAA Administrator (National Oceanic & Atmospheric Administration) Founder, former CEO & Executive Chairman of Aquabyte, a pioneering aquaculture technology company leveraging computer vision and AI
Agricultural Precision Farming: AI technologies enable precision farming, which involves the precise and controlled application of water, fertilizers, and pesticides to crops. By analyzing data from soil sensors, drones, and satellites, AI algorithms can optimize these inputs to enhance crop yields, reduce waste, and minimize environmental impact. This approach supports sustainable agriculture practices by ensuring resources are used efficiently. Crop Health Monitoring and Disease Detection: AI-powered imaging and diagnostics tools are being used to monitor crop health and detect diseases early. By analyzing images from drones or satellites, AI can identify signs of stress, pest infestations, or disease in crops, enabling farmers to take targeted action to mitigate losses. This technology is crucial for ensuring food security and maximizing agricultural productivity. Supply Chain Optimization: AI is streamlining agricultural supply chains, making them more efficient and responsive. By analyzing data on weather, crop yields, and market demand, AI algorithms can predict supply chain disruptions and optimize logistics. This includes everything from the timing of harvests to the storage and transportation of produce, ensuring that agricultural products are delivered to markets in the most efficient manner possible. The top three areas impact areas:
AI Adoption & Investment
As artificial intelligence (AI) continues to evolve, its adoption across industries is accelerating at an unprecedented pace. No longer a niche technology, AI is now embedded into core business functions—from predictive analytics in finance to supply chain optimization and personalized marketing. Recent developments have also introduced a powerful new player in this space: Generative AI (GenAI). While both AI and GenAI stem from the same foundational technologies, their applications and implications diverge significantly. Organizations are increasingly leveraging traditional AI to automate decision-making, analyze large datasets, detect fraud, and improve operational efficiency. At the same time, Generative AI is revolutionizing how businesses approach creativity, customer interaction, and content development. From chatbots that compose full emails to models that generate synthetic medical images, GenAI is enabling machines to create original, human-like outputs based on training data. AI Adoption
AI Adoption
AI Adoption
“The big AI companies are focused on solving intelligence. We’re solving presence. If you want people to fall in love with AI personas, they need to be more than smart. They need to be something you can feel, connect with, and grow alongside, not just a voice in your head or words on a screen. That’s where games come in and why this Unity partnership is a major milestone. As AI companions become part of everyone's day-to-day, people will want a deeper experience where they can do things with them, not just talk to them. By bringing Genies' technology to the world’s largest game development community, this collaboration empowers creators to build a new generation of AI-driven experiences, marking the beginning of a new era for AI companions in interactive entertainment.” Akash Nigam Co-founder & CEO at Genies *Genies has raised over $200 million from Silver Lake, Bond, NEA, Breyer Capital, Thomas Tull, Bob Iger and others.
AI Adoption
AI Adoption
AI Adoption
“Until two years ago, many enterprise customers questioned the real-world benefits of using AI in production environments. Some were convinced of its value, while others were skeptical. However, starting last year, there has been a dramatic shift. Now, everyone clearly understands the productivity boost AI can provide. Every enterprise customer is experiencing a fear of missing out (FOMO) if they don't integrate AI into their workflows immediately. Meanwhile, AI technology is advancing at a dizzying pace, transforming from simple automation tools for mundane tasks into sophisticated assistants capable of handling complex work alongside humans.” Changsu Lee Founder & CEO at Allganize
AI patents are an indicator of innovation and technological leadership. They reflect not only a country’s or company's ability to develop AI technologies, but also their intent to protect and commercialize intellectual property. Tracking AI patent activity helps us understand where the most intensive R&D efforts are taking place and which regions are likely to shape the future of AI. AI Patents
This graphic highlights global innovation in artificial intelligence by showing the number of granted AI patents per 100,000 inhabitants in 2022. South Korea and Luxembourg lead significantly, indicating a high concentration of AI research and development activity relative to population size. The United States, Japan, and China also rank prominently, which reflect their roles as global AI leaders. This metric highlights not just technological advancement but also national investment in AI innovation. AI Patents
“Companies of all sizes must embrace AI not as a luxury, but as a necessity for survival and growth. Large Language Models (LLMs) like ChatGPT offer a broad foundation, enabling organizations to automate knowledge work, enhance decision-making, and unlock new efficiencies. However, the true competitive edge lies in specialized, fine- tuned models tailored to an industry’s unique data, terminology, and workflows. A healthcare provider, a logistics firm, or a financial institution each faces distinct challenges that a generalized model cannot fully address without customization. Those who fail to integrate both general and domain- specific AI into their operations risk falling behind (or dying off completely) competitors who are accelerating innovation, reducing costs, increasing revenues, and delivering smarter, more personalized experiences.” Scott Sorochak Venture Partner at SparkLabs Group Former CRO at Brandwatch Former SVP Global Sales at Livefyre (acquired by Adobe) Former EIR at Foundation Capital
AI Private Investments
AI Private Investments Source: Writerbuddy
Private investment plays a pivotal role in accelerating artificial intelligence (AI) innovation by funding research, startups, and applications across industries. This shows the United States leading global AI private investment with over $67 billion, surpassing China’s $7.76 billion. The large gap reflects the U.S.’s dominance in AI innovation, driven by strong capital markets, a mature venture capital system, infrastructure, and startups. This disparity highlights the strategic role of private funding in shaping global AI leadership. AI Private Investments
This graphic highlights a significant shift in AI investment priorities. In 2023, private funding surged in AI infrastructure, research, and governance, emphasizing a strong focus on building scalable, secure, and responsible AI foundations. While areas like healthcare remained important, the overall trend reflects investor confidence in core AI technologies that support long-term innovation and trustworthiness of models and applications. AI Private Investments
Thank You *Written by Bernard Moon, William Chu, Bobby Fan, and Selina Zhong www.sparklabsgroup.com

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SparkLabs Primer on Artificial Intelligence 2025

  • 2.
    SparkLabs Group Since 2013, SparkLabshas invested in over 600 startups with a $56 billion in total post money valuation. The Korea Association of Health partnered with SparkLabs to operate MediOpenLab in 2024 and beyond. SparkBioLab and its portfolio companies will have access to MediCheck service's data from over 2 million health check-ups every year. Google chose SparkLabs Korea to jointly operate a Green Tech Accelerator in Busan, South Korea from 2021 to 2022. Facebook selected SparkLabs Korea to run their Innovation Lab Korea from 2018 to 2019. SparkLabs Group was the only venture capital firm (or any investment company) in the world to be recognized in Newsweek’s 2019 Top 100 Smart City Partners Awards
  • 3.
    SparkLabs Group SparkLabs has been investingin AI since 2016. We are investors in OpenAI, Vectara, Kneron, Anthropic, Lucidya, xAI and others. One of nine investment firms invited to participate in OpenAI’s recent fundraising round that closed in January 2024. SparkLabs Group has a partnership with UMG, the world’s leading music company with presence in more than 60 territories, to scout and develop the next wave of digital music startups in Asia. As of Dec 2024, we have invested in 323 startups through its accelerator programs across the globe. 280 have survived or were successful exits, which is a survival rate of 86.7% since 2013. This is testament to the team’s dedication to our startup founders.
  • 4.
    Alison Gopnik Cognitive Scientist “Oneof the fascinating things about the search for AI is that it’s been so hard to predict which parts would be easy or hard. At first, we thought that the quintessential preoccupations of the officially smart few, like playing chess or proving theorems—the corridas of nerd machismo—would prove to be hardest for computers. In fact, they turn out to be easy. Things every dummy can do, like recognizing objects or picking them up, are much harder. And it turns out to be much easier to simulate the reasoning of a highly trained adult expert than to mimic the ordinary learning of every baby.”
  • 5.
  • 6.
    Artificial Intelligence (AI)technology represents one of the most transformative innovations of the 21st century, reshaping the way we interact with machines, process information, and solve complex problems. At its core, AI involves the creation of algorithms and systems capable of performing tasks that typically require human intelligence, such as learning, reasoning, problem-solving, and understanding natural language. From self-driving cars to personalized recommendations, AI's applications are vast and varied, influencing industries as diverse as healthcare, finance, manufacturing, and entertainment. It will eventually touch every aspect of our lives in the coming decade.
  • 7.
    The rapid advancementsin AI technology are driven by breakthroughs in machine learning, deep learning, natural language processing, and computer vision. These advancements enable machines to not only process vast amounts of data but also to adapt and improve their performance over time. As AI continues to evolve, it raises profound questions about the future of work, ethics, and societal impacts. Understanding the foundations, current capabilities, and potential future developments of AI is essential for anyone seeking to navigate the opportunities and challenges posed by this cutting-edge technology.
  • 8.
    “AI models areadvancing at an extraordinary pace, but turning that raw capability into real economic value is only just beginning. There’s a vast and complex gap between what these models can theoretically do and what it takes to actually deliver production-grade outcomes. Bridging that gap is where the opportunity lies, and it’s where we’re seeing some of the most impactful innovation today. Whether it’s working with companies like Ramp, Webflow, or HubSpot, the lesson is clear: applying AI effectively requires deep integration, rigorous calibration, and thoughtful design. At AirOps, we’re focused on helping enterprise content teams drive visibility in AI-powered search experiences like ChatGPT and Google AI by managing content at scale, because the future of discovery is conversational, and those who show up well in those interfaces will win.” Alex Halliday Founder & CEO at AirOps Former VP of Product at Bungalow Former Head of Product at MasterClass Venture Partner at SparkLabs Global Ventures
  • 9.
    Timeline of MajorAI Milestones From Foundational Ideas to Frontier Models (1950-2025) by SparkLabs Group
  • 10.
    The building blocksof Artificial Intelligence (AI) are the foundational components and technologies that enable AI systems to function effectively. These include: DATA Data is the fuel for AI systems. Large volumes of structured and unstructured data are required to train AI models. This data can include text, images, audio, video, and sensor data, among others. Importance: The quality, quantity, and diversity of data directly impact the performance and accuracy of AI systems.
  • 11.
    Algorithms Algorithms are themathematical procedures or sets of rules that AI systems use to process data and make decisions. They include various types of models, such as machine learning algorithms, deep learning models, and reinforcement learning frameworks. Importance: Algorithms determine how AI systems learn from data and how they generalize to new, unseen data.
  • 12.
    Machine Learning Machine Learning(ML) is a subset of AI that focuses on developing algorithms that enable computers to learn from data and improve their performance over time without being explicitly programmed. Importance: ML is the backbone of modern AI, allowing systems to identify patterns, make predictions, and adapt to new information. Neural Networks Neural networks are a key component of deep learning, a subset of machine learning. Inspired by the human brain, neural networks consist of interconnected layers of nodes (neurons) that process data in complex ways to recognize patterns and make decisions. Importance: Neural networks are essential for tasks such as image and speech recognition, natural language processing, and more complex decision-making processes.
  • 13.
    Natural Language Processing (NLP) NLPis a field of AI that focuses on the interaction between computers and human language. It involves the development of algorithms that allow machines to understand, interpret, and generate human language. Importance: NLP is crucial for applications such as chatbots, language translation, sentiment analysis, and voice-activated assistants.
  • 14.
    Computer Vision Computer visionenables machines to interpret and make decisions based on visual data, such as images and videos. It involves techniques for image recognition, object detection, and image segmentation. Importance: Computer vision is vital for applications like autonomous vehicles, facial recognition, and medical imaging.
  • 15.
    Ethical and RegulatoryFrameworks These frameworks guide the responsible development and deployment of AI, addressing issues such as bias, fairness, transparency, and accountability. Importance: As AI becomes more integrated into society, ensuring that it is developed and used ethically is critical for gaining public trust and preventing harm.
  • 16.
    On regulation… “I believewe should be very slow to regulate AI and very mindful of the 2nd and 3rd order effects that could come from any regulations we enact. Many of the potential harms of AI are already addressable under existing laws. We shouldn't just start regulating everything without knowing why we're doing it. It will become very clear over time what new problems arise that actually need new regulations.” Advances for AI over the next 3 years… “Major improvements in LLM memory will occur, and they will have access to your files. I expect we'll have a version of ChatGPT that has access to all your files and remembers everything you've said before, making AI an incredibly powerful personal assistant.” Nathan Lands Founder & CEO at Lore Co-Creator & Host at The Next Wave Podcast Venture Partner at SparkLabs AI
  • 17.
    Computing Power Advanced AIsystems require significant computational resources to process large datasets and perform complex calculations. This is provided by powerful processors, GPUs (Graphics Processing Units), and distributed computing systems. Importance: The availability of high-performance computing power enables the training and operation of sophisticated AI models. Together, these building blocks form the foundation of AI technology, enabling the creation of systems that can learn, reason, perceive, and interact in ways that were once the exclusive domain of humans.
  • 18.
    “Getting computers tounderstand and find human knowledge has always been a challenge. Legacy keyword search systems are inflexible and require rich knowledge graphs + language rules to work well, which makes them very expensive to build / maintain. The new generation of Gen AI algorithms allow us to understand and find information semantically across languages, which leads to much more powerful systems at significantly lower costs. At Vectara we specialize in making RAG work smoothly with high quality results and reduced hallucinations. We also address a number of other regulated industry requirements like access control, explainability, data privacy, and many others.” Amr Awadallah Co-Founder & CEO at Vectara. Co-Founder & former Chief Technology Officer at Cloudera (multi-billion dollar IPO) Advisor at SparkLabs Saudi Arabia & SparkLabs AI
  • 19.
    The Three Typesof AI From Narrow to Superintelligence Al that specializes in a single task (e.g., language translation, facial recognition). ARTIFICIAL NARROW INTELLIGENCE (ANI) Al with the ability to understand, learn, and apply knowledge across a wide range of tasks like a human. ARTIFICIAL GENERAL INTELLIGENCE (AGI) Al that surpasses human intelligence in all aspects, including creativity, problem- solving, and emotions. ARTIFICIAL SUPERINTELLIGENCE (ASI)
  • 20.
    Artificial General Intelligence(AGI) is generally believed to be 5 or more years away from becoming a reality. But this is often a moving target when asking various “experts” such as Elon Musk who recently said it will be achieve by the end of 2026. Three Types of Artificial Intelligence Estimates to Achieving Artificial Superintelligence (ASI) Estimated Time of Arrival Ray Kurzweil (futurist) Approximately by 2045 (Singularity prediction) Elon Musk ASI could come within 5–10 years, if unchecked Demis Hassabis (DeepMind) AGI is achievable within a decade, ASI afterwards OpenAI researchers (2023 survey) 10–50 years, median estimate around 30 years Kree Supreme Intelligence (Marvel Comics)
  • 21.
    AI Lifecycle The AIlifecycle is a structured, iterative process guiding the development, deployment, and maintenance of AI systems. It begins with the Design Phase, where the problem is clearly defined, relevant stakeholders are engaged, and data is collected, explored, and prepared. This foundational stage ensures alignment with business objectives, feasibility, and ethical considerations. Next is the Develop Phase, where AI models are built and evaluated. This includes choosing the right algorithm, designing model architecture, training with high-quality data, and assessing performance using appropriate metrics. Techniques such as regularization, hyperparameter tuning, and cross-validation are applied to optimize results. The final stage is Deploy Phase, where the trained model is integrated into production environments. Here, the model is exposed to real-world data and continuously monitored to detect drift, performance degradation, or anomalies. This lifecycle ensures AI solutions remain effective, secure, and aligned with organizational goals, while enabling retraining and updates as needed through feedback loops.
  • 22.
    Basic Computing ofArtificial Intelligence WHAT IS A TENSOR? A tensor is a mathematical object that can be viewed as an array of data. The rank of a tensor simply refers to the number of dimensions in the array. In the context of data science, tensors are multi-dimensional arrays of numbers that represent complex data. They are the fundamental data structures used in machine learning and deep learning frameworks. DEFINITION OF A TENSOR In a strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector space. However, in machine learning, the term "tensor" informally refers to two different concepts that organize and represent data. Data may be organized in a multidimensional array (M-way array) that is informally referred to as a "data tensor". This data can be analyzed using artificial neural networks or tensor methods. APPLICATIONS OF TENSORS Tensors are particularly useful for handling complex data such as images, audio, and text. For example, a color image can be represented as a 3D tensor with dimensions corresponding to height, width, and color channels (red, green, and blue). Similarly, a video can be represented as a 4D tensor with time being the additional dimension.
  • 23.
    Let’s create anexample of the above content through natural language processing. Hi King Hi Queen Hi Jack When there are words, the word is expressed in vector by one-hot encoding as follows Since the word is expressed as a vector, it seems that the sentence can be expressed as a matrix based on the vector of the word. Example The sentence is expressed as a matrix as above. However, we usually input a corpus as input to the neural network, and the corpus can be expressed as follows: hi king hi queen hi jack [ [[1,0,0,0], [0,1,0,0] ], [[1,0,0,0], [0,0,1,0]] , [[1,0,0,0], [0,0,1,0]] ] The words in the above corpus are composed of 4 dimensions. Each sentence is composed of 2 words. Since there are a total of 3 sentences, this model can be considered as a 3-dimensional tensor of size (3, 2, 4) Basic Computing of Artificial Intelligence
  • 24.
    When extracting objectssuch as people, animals, and objects from an image, a huge number of convolutions (overlapping integrals) occur. The convolution operation itself consists of a very large number of multiplications. Convolution A key operation required for Al inference or learning is tensor convolution (overlapping integral) operation. This convolution operation allows us to roughly determine the shape of the border in the image along the direction specified by the filter. In deep learning, this task is called feature extraction (a method of distinguishing objects using a pattern recognition algorithm). The learning process of a deep learning model involves modifying the weight of each filter to make the feature map (resulting value resulting from the calculation) through multiple convolution operations as similar to the correct answer as possible.
  • 25.
    “Game AI hasbeen stuck in the past for decades. Traditional systems rely on rigid scripts and predetermined responses, but modern AI unlocks something fundamentally different: characters that think, adapt, and form genuine connections with players. At Glade, we're building the infrastructure to make this possible at scale. Our flagship product, GladeCore, is a lightweight, on-device AI system that eliminates cloud dependency, delivering ultra-low latency AI characters with zero variable costs and no privacy concerns for developers. It's battle-tested in our award-winning game Cursed Crown, and we're now helping other studios create the next generation of truly intelligent games.” Daniel Fang Founder & CEO at Glade (YC S23)
  • 26.
    CPU vs GPUvs NPU The fundamental difference between CPU, GPU and TPU is the way these circuits are engineered and the way they process the instructions. The below diagram explains how these three chips do arithmetic calculations. CPU which is designed to handle scalar calculations can do 1x1 data unit. On the other hand, GPUs are specifically designed to do vector calculations that is 1xN data unit. NPU does tensor calculations which can go up to NxN calculations. When we look from the no. of operations these chips can handle, CPU can handle tens of operation per cycle while GPU can handle tens of thousands of operation per cycle and NPU can handle up to 128000 operations per cycle. CPU: Parallelized scalar multiplication GPU: Parallelized vector multiplication NPU: Parallelized tensor multiplication
  • 27.
    The Advantages ofUsing NPUs NPUs are faster and more cost-effective than GPUs The tensor processing capabilities of NPUs are specifically designed for efficient execution of tensor operations, leveraging specialized tensor cores to accelerate the computation of deep learning models. While CPUs and GPUs can also perform these operations, NPUs complete them much faster and with significantly lower energy costs Enhanced Memory, Reduced Power Use, and Smaller Size The NPU is specifically designed for artificial intelligence and machine learning tasks. This memory is integrated within the NPU chip itself, which helps reduce data transfer times and lower power consumption compared to systems that use external memory. Additionally, the efficient use of memory space allows for smaller chip designs. These features enable faster and more efficient AI processing through NPUs. The compact form factor also makes NPUs particularly well-suited for use in on-device AI applications, such as smartphones and AR/VR devices. SAPEON’s NPU outperformed GPUs in the MLPerf AI Datacenter Benchmark Test Source: SAPEON’s X330 AI NPU Inference Chips are Now More Important Than Learning Chips In particular, NPUs are becoming increasingly important in the inference market. At the point of launching AI services, what matters most for service companies is no longer chips designed for learning; instead, the focus has shifted to chips for inference. The applications being developed are diverse, including image generation, disease detection (such as cancer and tumors), and assessments of loan repayment capabilities. The use of specialized inference chips tailored to each application inevitably improves the quality of the services provided.
  • 28.
    Data Without data, you'rejust another person with an opinion. Data is the foundation of modern artificial intelligence, enabling machines to learn, adapt, and make informed decisions. AI systems require vast amounts of structured and unstructured data, including text, images, audio, and sensor readings, to function effectively. High-quality data enhances model accuracy, while insufficient or biased data can lead to poor performance. From training large language models to enabling real-time decision-making, data serves as the fuel that powers innovation, personalization, and automation in AI. Synthetic Data: A Solution to Data Limitations AI systems rely heavily on vast amounts of high-quality data to perform effectively. However, real-world data is often constrained by privacy regulations, high acquisition costs, and limited availability—particularly in sensitive sectors like healthcare and finance. To overcome these challenges, synthetic data has emerged as a promising alternative. Generated through algorithms rather than collected from real-world events, synthetic data can closely mimic the statistical properties of real data without compromising individual privacy. It is increasingly used to augment training in fields like autonomous driving, natural language processing, and medical imaging, especially where data is scarce or sensitive. Synthetic data enables customizable, diverse datasets and allows simulation of rare or risky scenarios. However, its utility depends on quality, realism, and variability. Poorly generated data may reduce model performance, and validation against real data remains essential. As tools advance, synthetic data will play a growing role in scalable and ethical AI development.
  • 29.
  • 30.
    The global artificialintelligence (AI) market has seen unprecedented growth over the past decade, driven by rapid technological advancements and increasing investment. AI is transforming industries by enhancing efficiency, automating processes, and enabling data-driven decision-making. As businesses and governments continue to adopt AI solutions, the market is expected to expand further, shaping the future of innovation and economic development. Global AI market
  • 31.
  • 32.
    Introduction to theGlobal AI Market Artificial Intelligence (AI) has experienced exponential growth, driven by rapid advancements in machine learning, robotics, and automation. As industries increasingly integrate AI-driven solutions, the market is expanding at an acc elerating pace, with key sectors like machine learning, natural language processing, and autonomous systems lea ding the way. The adoption of AI is no longer confined to tech companies—industries such as healthcare, finance, manufacturing, and transportation are leveraging AI to enhance efficiency and innovation. With continuous invest ment and breakthroughs in AI hardware and software, the global AI market is expected to sustain its upward traject ory, reshaping economies and industries in the years to come.
  • 33.
    Global AI market Drivers •The market is experiencing growth due to an increasing demand for faster and more reliable hardware, prompted by the vast quantities of data generated daily. • The necessity for highly parallel architectures and specialized hardware to manage high-performance computations essential for neural network operations is fostering the adoption of accelerators in the market. • The extensive integration of artificial intelligence software in various industries, which enhances business processes, improves decision-making efficiency, and drives innovation, is contributing significantly to market expansion. • The escalating requirement for expert guidance and comprehensive support in deploying and utilizing artificial intelligence solutions is boosting the demand for professional services within the market. • The importance of support and maintenance services is growing, driven by their role in ensuring the long-term success of artificial intelligence initiatives across various businesses.
  • 34.
    Global AI market Theartificial intelligence (AI) market is categorized by three primary offerings: hardware, software, and services. Each segment addresses different facets of AI needs and contributes to the overall expansion of the artificial intelligence market. AI Market Segmentation Hardware: This segment includes accelerators, processors, memory, and network components. Processors are further divided into microprocessor units, graphics processing units, field-programmable gate arrays, and other types of processors. The demand for advanced hardware that mimics the intelligence and efficiency of the human brain is a significant driver for growth in this segment. Software: The software sector of the AI market is segmented by type and deployment method. Software types include pre-trained models, customizable AI solutions, edge AI, and AI marketplaces. Deployment methods are categorized into cloud-based and on-premises solutions. Services: This offering is split into professional and managed services. Professional services encompass consulting, deployment and integration, as well as support and maintenance services.
  • 35.
    Market Size Global AImarket Source: Grand View Research
  • 36.
    and architectural designsto meet the growing processing requirements of artificial intelligence systems. This competition in the market has led to continuous product launches and advancements in hardware development and software platforms for machine learning algorithms and programs. The artificial intelligence hardware market is projected to experience significant growth, driven by the increasing need for high- performance hardware platforms capable of running complex artificial intelligence software. The presence of major artificial intelligence companies in North America has positioned the region as a key market for AI-related hardware. Global AI market The artificial intelligence market has been segmented, by hardware, into accelerators, processors, memory, and network. The demand for faster and more reliable hardware in the field of AI is increasing due to the massive amount of data being generated daily. Established companies such as Intel, IBM, NVIDIA, and Samsung, as well as startups, are actively developing new hardware Hardware Source: Grand View Research
  • 37.
    ”Material handling applicationsare increasingly adopting automation to address global labor shortages. AI is pivotal in unlocking solutions to previously unsolved automation challenges across the supply chain—from factories and large regional distribution centers to smaller warehouses. One of the key untapped problems, the loading and unloading of truck trailers, can now be addressed thanks to recent AI advancements. Gideon revolutionizes the loading and unloading bay with autonomous forklifts, leveraging AI for human-like perception and real-world execution.” Josip Cesic Co-founder & CEO at Gideon *Gideon raised $48 million from Toyota Industries, Prologis Ventures, Koch Disruptive Technologies, DB Schenker, Taavet Hinrikus (Co-founder of TransferWise) and others.
  • 38.
    The artificial intelligencemarket for hardware has been segmented, by processor, into graphics processing units (GPUs), microprocessing units (MPUs), field programmable gate arrays (FPGAs), and other processors. The adoption of processors in the field of AI has witnessed significant growth as AI applications continue to advance. GPUs, known for their parallel processing Global AI market capabilities, have become a popular choice for accelerating AI workloads. They offer high performance and efficient execution of complex algorithms. MPUs and FPGAs are also being widely adopted in AI, providing specific advantages such as flexibility and power efficiency. The increasing demand for AI- driven solutions and the need for faster processing speeds have driven the adoption of these processors. As AI continues to evolve, processors play a vital role in enabling the development and deployment of advanced AI models and applications. Processors Source: Grand View Research
  • 39.
    SEMICONDUCTOR COMPANIES MARKETREVENUE WORLDWIDE FROM 2009 TO 2023
  • 40.
    INTEGRATED CIRCUIT (IC)DESIGN COMPANIES REVENUE WORLDWIDE FROM 2017 TO 2023, BY QUARTER
  • 41.
    NVIDIA SPECIALIZED MARKETREVENUE WORLDWIDE FROM FISCAL YEAR 2019 TO 2024, BY QUARTER
  • 42.
    AMD SEGMENT REVENUEWORLDWIDE FROM 2021 TO 2023, BY QUARTER
  • 43.
    INTEL QUARTERLY REVENUE FROM2021 TO 2023, BY SEGMENT
  • 44.
    “With the explosivegrowth of AIGC, global demand for cloud AI computing has surged, placing immense pressure on energy infrastructure and carbon neutrality. As multimodal AIGC expands, challenges like latency, bandwidth, and privacy will increasingly limit the use of cloud AIGC. The shift of AI compute from the cloud to the edge is an inevitable trend. Kneron focuses on creating efficient edge AI inference chips that use less power and cost less. We have successfully launched a range of edge AI products across key industries including security, autonomous driving, and consumer electronics, leading in several specialized markets. Our next milestone is to launch next-generation chips that run complex AIGC models locally—enabling faster, more secure, and energy- efficient AI experiences.” Albert Liu Co-Founder & CEO at Kneron *Kneron has raised over $200 million from Sequoia China, Qualcomm, Horizon Ventures, Foxconn and others.
  • 45.
    Global AI market Microchips Nvidia’sDominance in the GPU Market The GPU (Graphics Processing Unit) market is a rapidly evolving segment of the semiconductor industry, playing the leading role in artificial intelligence (AI). NVIDIA’s dominance in the GPU market is the result of a multi-decade strategy that combined its early bets on AI, technical foresight, and an integrated software ecosystem. Source: IoT Analytics
  • 46.
    Astera Labs &AI Connectivity Astera Labs specializes in high-speed connectivity solutions for AI cloud data centers, ensuring seamless AI infrastructure performance. Their Intelligent Connectivity Platform accelerates data transfer speeds, enhancing AI chip efficiency. Advantages By improving AI chip-to-chip connectivity, Astera Labs optimizes data movement across AI infrastructures. This reduces bottlenecks in AI workloads, ensuring efficient scaling for next- generation AI applications. Market Impact Astera Labs' IPO in 2024 saw shares surge by over 70%, reflecting strong investor confidence in AI semiconductor infrastructure. With a valuation exceeding $16 billion, the company is positioned as a key player in AI hardware despite not yet reaching profitability. Global AI market Microchips
  • 47.
    SK Hynix’s HBM:Powering AI's Future High Bandwidth Memory (HBM) is essential for AI acceleration, offering significantly higher bandwidth and lower power consumption compared to traditional DRAM. It is designed to handle the intensive computational demands of modern AI models. NVIDIA integrates SK Hynix’s HBM to enhance AI model training and inference, reducing latency and increasing efficiency. Advantages HBM supports massive parallel processing, enabling faster computations crucial for deep learning. It allows quicker access to large datasets, improving AI workload efficiency. Additionally, its lower energy consumption makes AI chips more sustainable for large-scale applications. Impact on AI Hardware The integration of HBM in AI chips ensures that AI infrastructures operate at peak efficiency. It enhances GPU performance, allowing AI systems to process complex models with greater speed and scalability. Global AI market Microchips
  • 48.
    Groq LPUs (LanguageProcessing Units) Groq’s Language Processing Unit (LPU) introduces a new approach to AI hardware, providing an alternative to GPUs. Unlike GPUs designed for general parallel computing, LPUs focus on low-latency, high-speed AI inference, making them highly efficient for real-time AI applications. Advantages LPUs achieve up to 13 times faster inference speeds than Microsoft AI chips, improving responsiveness for AI-driven workloads. They optimize token processing speeds, enhancing AI assistants and other machine learning applications. LPUs reduce computational bottlenecks, making them ideal for large language models such as Llama 2 and GPT models. Future Potential As AI applications continue to evolve, Groq’s LPU technology is likely to play a significant role in shaping the future of AI hardware. By challenging the dominance of GPUs and TPUs in inference workloads, LPUs present a new class of AI accelerators focused on efficiency and speed. Global AI market Microchips
  • 49.
    Global AI market Microchips AIComputing Moving to the Edge The current energy consumption of cloud AI is unsustainable unless the architecture and power availability changes, which is why some analysts believe more of AI will be moved to the edge via microchips. There is not enough energy in the current grid to meet AI’s projected needs.
  • 50.
  • 51.
    “In the ageof vibe coding, ephemeral software is becoming a competitive edge. These are tools built fast, used intensely, and thrown away without ceremony. At Setset, we build and discard internal AI tools weekly to solve product image challenges in e-commerce—delivering high-quality photography and video at scale, tailored to each brand’s aesthetic and technical setup. With faster timelines and lower costs, these bespoke systems outperform one-size- fits-all platforms. Software is no longer something you buy and consume, but something you create on the fly to meet the need of the moment.” Nils Westerlund Founder at SetSet.ai Former Growth Team Lead & Product Lead at Runway AI Venture Partner at SparkLabs AI
  • 52.
    Artificial intelligence softwareutilizes advanced techniques like machine learning, deep learning, and natural language processing to mimic human intelligence. These programs are adept at analyzing complex data, recognizing patterns, and making predictions. They support a variety of applications, from chatbots and virtual assistants to autonomous systems, leveraging complex networks Global AI market to process data, learn, and enhance performance over time. Continuous improvements in AI software have led to innovations in fields like image recognition and speech synthesis, improving human- computer interactions and enabling personalized experiences. This software is transforming industries by streamlining decision-making and fostering innovation in sectors such as healthcare, finance, and manufacturing. Software Source: Grand View Research
  • 53.
    Pre-trained models Pre-trained modelshave meticulously crafted models that have undergone rigorous training on vast datasets to excel at specific tasks, such as natural language processing, image recognition, or speech recognition. These models are endowed with learned features and patterns derived from the training data, negating the necessity to train models from scratch. To attain remarkable accuracy, pre-trained models are meticulously trained on extensive, high-quality datasets utilizing state-of-the- art techniques. Leveraging pre-trained models empowers developers and researchers to save substantial time and resources that would otherwise be required for training models from the ground up. This accessibility allows organizations and individuals with limited resources to achieve exceptional performance levels without extensive data or expensive computational infrastructure. Customizable AI Customizable AI is a specialized software type in the artificial intelligence market that allows users to customize and adapt AI models and algorithms to meet specific requirements. Unlike pre-packaged solutions, it offers flexibility and control over the underlying algorithms, data processing pipelines, and model architectures. With customizable AI, businesses and developers fine-tune models to their unique datasets, problem domains, and objectives. It tailor the training process, incorporate domain-specific knowledge, and adapt models to changing circumstances. Customizable AI empowers organizations to develop highly specialized and domain-specific artificial intelligence applications, improving accuracy and performance. It finds applications in healthcare, finance, manufacturing, and customer service, where customized AI solutions are vital for optimal results. It also provides a powerful tool for organizations to leverage AI's potential while addressing their specific needs and challenges. Global AI market Software
  • 54.
    Edge AI Edge AIsoftware encompasses a diverse range of machine learning algorithms executed on local hardware devices. Its purpose is to enable the deployment of AI algorithms directly on a device or machine. With Edge AI software, users access real-time data without the need for external systems or internet connectivity, as the processing occurs locally on the device itself. This decentralized approach allows faster and more efficient data processing, eliminating the dependency on network connections or cloud-based servers. By leveraging Edge AI software, organizations achieve real-time insights and perform AI tasks locally, enhancing responsiveness and privacy and reducing reliance on external connections. AI Marketplaces AI marketplaces are digital platforms within the AI market that facilitate the discovery, acquisition, and exchange of AI models, algorithms, and services. These marketplaces serve as centralized hubs for developers, data scientists, and businesses to access and leverage a wide range of AI resources. In AI marketplaces, users explore and obtain pre-built AI models and algorithms, accelerating the development process and reducing the need for extensive customization. Additionally, these platforms enable developers to showcase and monetize their AI creations, promoting collaboration and innovation. AI marketplaces also offer a suite of AI-related services, including data labeling, model training, and consulting, to support organizations in their AI journey. It also provides tools and frameworks for the seamless integration of AI capabilities into applications, empowering businesses to harness the power of artificial intelligence. By fostering knowledge sharing, resource accessibility, and commercialization of artificial intelligence innovations, AI marketplaces contribute to the widespread adoption and democratization of artificial intelligence technologies across industries. It plays a vital role in driving the advancement and application of AI, shaping the future of intelligent systems. Global AI market Software
  • 55.
    AI software isdeployed through various methods, including on-premises and cloud-based deployments. The choice depends on factors such as data security, available infrastructure, and workload complexity. On-premises deployment offers direct control and customization within the organization's infrastructure, suitable for strict security requirements. Cloud-based deployment utilizes remote servers, providing scalability, accessibility, and managed infrastructure. It is ideal for organizations seeking Global AI market Software flexibility and resource efficiency. The deployment method is chosen based on the organization's needs, ensuring optimal control, scalability, and resource utilization in AI software deployment. Source: Grand View Research
  • 56.
    The services segmentin the AI market encompasses both professional and managed services, playing a crucial role in the entire AI life cycle, including product upgrades, maintenance, training, and consulting. In the digital economy era, enterprises are actively seeking innovative solutions to enhance their business operations and optimize resources. Artificial Intelligence vendors offer diverse services, including support and maintenance, deployment and integration, and consulting, to effectively execute and manage the life cycle of artificial intelligence solutions. The growth of the services segment is primarily driven by the increasing complexity of artificial intelligence operations and the widespread adoption of artificial intelligence technologies across industries. Businesses increasingly rely on services that streamline operations, optimize resource utilization, and drive growth and profitability. Global AI market Software Source: Grand View Research
  • 57.
    “’Text-to-software’ platforms arecompressing the idea-to- MVP timeline from months to days. Builders such as Lovable and Bolt.new already let both technical and non-technical users chat their way to functional sites and apps, leading to a rapid recent increase in overall throughput for new software and products. At the same time, tools like Cursor are amplifying individual engineers and letting leaner teams ship more with fewer hires. People are building more and faster than ever. Cosmic takes the next logical step: our agent not only writes you a beautiful front-end but also handles your entire backend, like auth, database, payment processing, and more, so even non-technical users can launch, monetize, and iterate in a single weekend.” Sam Park Co-Founder & CEO at Cosmic (YC F24)
  • 58.
    This section coversthe segmentation of the artificial intelligence market based on vertical. The segments studied include BFSI, retail & eCommerce, automotive, transportation and logistics, government & defense, healthcare & life sciences, telecom, energy & utilities, manufacturing, IT/ITeS, media & entertainment, and other verticals (education, travel and hospitality, and construction). Global AI Market by Vertical Source: Grand View Research
  • 59.
    While analyzing AImarket offerings provides insights into the types of solutions available—such as software platforms, hardware components, and professional services—examining the market by vertical reveals how these solutions are being applied across different industries. This perspective highlights the growing demand for industry-specific AI applications, with sectors like healthcare, finance, and manufacturing increasingly adopting tailored AI tools to address their unique challenges and opportunities. Global AI Market by Vertical Source: Grand View Research
  • 60.
    AI-powered systems inbanking apps and services have ushered in a new era of customer-centricity and technological advancement. Through advanced algorithms and data analysis, banks have achieved increased productivity and cost reduction by automating processes and leveraging vast amounts of data for informed decision-making. Implementing intelligent algorithms has significantly enhanced fraud detection capabilities, enabling real-time identification and preventing fraudulent activities. In the financial market, AI-driven trading systems have optimized investment strategies by analyzing large-scale market data, driving efficiency and accuracy. The insurance industry has also benefited from artificial intelligence, as underwriting processes have been streamlined and claims management has become more efficient. Global AI Market by Vertical: BFSI (Banking, Financial Services, and Insurance) Source: Grand View Research
  • 61.
    Artificial intelligence applicationsin retail and e-commerce have transformed the customer experience, supply chain operations, and business efficiency. AI-powered systems enable retailers to analyze vast amounts of customer data, unveiling valuable insights that help understand buying patterns and behaviors. It allows personalized shopping experiences and tailored product recommendations, enhancing customer satisfaction and loyalty. In e-commerce, artificial intelligence optimizes search functionalities, improves product suggestions, and streamlines the ordering and delivery process, leading to a seamless online shopping experience. The competitive nature of the retail and e-commerce sector makes differentiation crucial, and AI offers businesses a significant edge. By leveraging artificial intelligence, retailers and eCommerce platforms make faster, data-driven decisions, optimize operations, and improve profitability. Global AI Market by Vertical: Retail & Ecommerce Source: Grand View Research
  • 62.
    AI is revolutionizingthe automotive, transportation, and logistics industries by introducing advanced solutions to enhance efficiency, safety, and overall operations. In automotive manufacturing, AI-driven robotics and automation optimize production processes, ensuring higher quality and precision. Predictive maintenance powered by artificial intelligence minimizes vehicle downtime and maximizes performance. In transportation and logistics, artificial intelligence facilitates route optimization, fleet management, and demand forecasting. By analyzing vast data sets, artificial intelligence enables businesses to streamline operations, reduce fuel consumption, and improve supply chain efficiency. Autonomous vehicles benefit from artificial intelligence technologies, enhancing safety through advanced driver assistance systems and self-driving capabilities. Additionally, AI-driven data analytics and predictive modeling empower intelligent decision-making, risk assessment, and supply chain optimization. Global AI Market by Vertical: Automotive, Transportation & Logistics Source: Grand View Research
  • 63.
    The government &defense sectors are capitalizing on AI-powered solutions to address complex challenges, enhance service delivery, facilitate decision-making processes, and strengthen national security. With the increasing adoption of artificial intelligence technologies, these sectors also focus on implementing AI-driven governance solutions. Governments worldwide are forming partnerships with major technology companies to reinforce ethics and governance practices. In the collaboration between the UAE government and Microsoft, efforts are focused on developing artificial intelligence programs and frameworks to ensure trustworthy AI deployment. Global AI Market by Vertical: Government & Defense Source: Grand View Research
  • 64.
    Biggest concerns aboutAI… “I believe that the unknowns of AI’s future capabilities and its impact on humans, ranging from privacy, bias, and work to just daily life, is what the industry needs to address to avoid fear taking over and hurdles to innovation and progress.“ Approach to regulate AI… “There’s no one-off regulation or policy solution but a never- ending approach of constant small, nimble steps by regulators and policymakers to adjust to seemingly lightning- speed AI progress. Tech giants are part of the solutions and must be held accountable for developing and deploying AI ethically and responsibly. It will seem impossible for all participants, but it is the only way forward for AI and society." Spiros Margaris General Partner at Margaris Ventures No. 1 Global Finance & FinTech Influencer; Top AI Influencer & Thought Leader Venture Partner at SparkLabs AI
  • 65.
    The healthcare &life sciences sector is experiencing a significant transformation with the integration of AI technologies. Artificial intelligence is revolutionizing healthcare by enhancing patient care, enabling advanced diagnostics, accelerating drug discovery, and improving operational efficiency. In healthcare, AI- powered systems analyze vast amounts of medical data to aid in early disease detection, assist in accurate diagnosis, and provide personalized treatment recommendations. In the life sciences domain, artificial intelligence revolutionizes drug discovery processes by analyzing complex biological data, predicting drug efficacy, and identifying potential targets. Global AI Market by Vertical: Healthcare & Lifesciences Source: Grand View Research
  • 66.
    Artificial intelligence hasthe potential to revolutionize various aspects of telecom operations, from network management and customer experience to service provisioning and cybersecurity. Telecom companies leverage artificial intelligence to optimize network performance, predict and prevent network outages, and automate routine maintenance tasks. AI-powered virtual assistants and chatbots enhance customer interactions by providing personalized support and resolving queries in real-time. Additionally, AI algorithms are utilized to analyze massive amounts of data generated by telecom networks, enabling companies to gain valuable insights for business decision-making and service enhancements. Global AI Market by Vertical: Telecom Source: Grand View Research
  • 67.
    Artificial intelligence applicationsin this industry range from smart grid management and energy forecasting to demand response and energy optimization. By leveraging AI algorithms, energy & utility companies analyze vast amounts of data, such as energy consumption patterns, weather conditions, and grid performance, to make intelligent decisions and optimize energy generation, distribution, and consumption. AI-powered solutions enable predictive maintenance of energy infrastructure, identify energy theft or anomalies, and enhance grid reliability and resilience. Global AI Market by Vertical: Energy & Utilities Source: Grand View Research
  • 68.
    Artificial intelligence isrevolutionizing manufacturing processes by enhancing productivity, efficiency, and quality while enabling businesses to remain competitive globally. Traditionally, manufacturing has relied on manual labor and conventional machinery, but with the advent of artificial intelligence, manufacturers are increasingly adopting intelligent systems to automate and optimize their operations. One of the primary drivers behind the adoption of artificial intelligence in manufacturing is the pursuit of operational excellence. Artificial intelligence technologies, such as machine learning, computer vision, and natural language processing, enable manufacturers to analyze large volumes of data in real- time, extracting valuable insights and making data-driven decisions. Global AI Market by Vertical: Manufacturing Source: Grand View Research
  • 69.
    Artificial intelligence hasemerged as a powerful tool for optimizing various aspects of agriculture, including crop cultivation, livestock management, supply chain logistics, and overall farm operations. By leveraging artificial intelligence technologies, the agriculture industry has the potential to enhance productivity, reduce costs, minimize waste, and mitigate environmental impact. Artificial intelligence in the agriculture industry refers to the utilization of intelligent algorithms and systems to analyze vast amounts of data collected from agricultural processes, enabling farmers and stakeholders to make data-driven decisions. Global AI Market by Vertical: Agriculture Source: Grand View Research
  • 70.
    The information technology/informationtechnology-enabled services (IT/ITeS) industry plays a pivotal role in driving innovation and digital transformation across various sectors. With the advent of AI, the industry has witnessed a paradigm shift, presenting significant opportunities for businesses to leverage AI technologies to enhance efficiency, improve customer experiences, and drive competitive advantage. The AI market within the IT/ITeS industry encompasses a wide range of technologies and services that enable machines to simulate human intelligence and perform tasks that typically require human intervention. This includes natural language processing, machine learning, computer vision, robotic process automation, and expert systems. Global AI Market by Vertical: IT/ITES Source: Grand View Research
  • 71.
    AI is transformingthe media industry by enhancing content creation, distribution, and personalized audience engagement. This integration of AI offers opportunities for businesses to improve operations and deliver engaging entertainment experiences. The media and entertainment AI market includes machine learning, natural language processing, and recommendation systems, which help analyze data, automate tasks, and provide insights for content innovation. AI-driven recommendation systems personalize content based on user data like viewing habits and interactions, enhancing consumer satisfaction. Global AI Market by Vertical: Media & Entertainment Source: Grand View Research
  • 72.
    The advent ofAI has revolutionized industries such as construction, education, travel, and hospitality, enhancing efficiency, productivity, and customer experiences. In construction, AI technologies automate project management, enhance safety, and optimize resource use, deploying AI-enabled drones and robotics for tasks like inspections and material transport while facilitating data-driven decision-making. In education, AI introduces personalized learning through intelligent tutoring systems, virtual and augmented reality for immersive experiences, and chatbots for round-the-clock student support, also aiding educators in administrative tasks. In the travel sector, AI-driven chatbots improve customer service by assisting with bookings and offering personalized recommendations based on data analysis. Similarly, in hospitality, AI enhances guest experiences through personalized services via chatbots, virtual assistants, and voice recognition technologies, streamlining operations and improving satisfaction. Global AI Market by Vertical: Others Source: Grand View Research
  • 73.
    When will weachieve AGI? “Achieving artificial general intelligence (AGI) may happen sooner than many anticipate, but perhaps not as quickly as the most optimistic forecasts suggest. My journey into computer science began in the late 1970s, with a focus on artificial intelligence (AI). At that time, AI was a broad concept often associated with advanced, human-like intelligence as portrayed in science fiction, such as HAL from ‘2001: A Space Odyssey’. This era lacked the clear distinction we see today between AGI and generative AI. My initial work in AI felt more theoretical than practical, leading me to shift my focus to computer graphics. However, my interest in AI persisted, especially during my tenure at the Federal Aviation Administration, where I encountered AI's application in predicting hazardous weather conditions using LISP on Symbolics machines—a form of what is now known as machine learning. Over the years, I've observed AI's evolution from a vantage point of informed curiosity. A prevalent, though unspoken, belief among my peers was that a milestone towards achieving "true AI" or AGI would be the capability for information to self-learn and self-modify. The recent advancement in large language models (LLMs) and generative AI over the past 18 months represents this critical step, indicating that we are on the brink of an exponential surge in technology. Based on these developments, I project that AGI could emerge in some form within the next 5 to 8 years, marking a significant milestone in the journey of AI.” Rob DeMillo Co-founder & CEO at Sophia Space Former CTO at Skidmore, Owings & Merrill (SOM) Former CTO at Nimble Collective Former CTO at Discovery Digital Networks (formerly Revision3) Former VP of Engineering at VeriSign (formerly m-Qube) Venture Partner at SparkLabs Global
  • 74.
    This map providesa comprehensive overview of the global AI ecosystem by categorizing major companies into functional tiers such as hardware, network, software, security, platforms, services, and cloud. It primarily highlights large, established players like NVIDIA, IBM, Oracle, Microsoft, and AWS, organizing them by their core role in the AI value chain. Global AI Market Map (by Market & Market)
  • 75.
    Generative AI MarketMap (by Sequoia Capital)
  • 76.
    Generative AI InfrastructureStack (by Sequoia Capital)
  • 77.
    “While most peoplethink AI is plug-and-play, industrial and physical AI tell a very different story. In sectors like advanced manufacturing or smart warehouses, production optimization and robotics control can’t be solved with off-the-shelf models — they require simulation, iteration, and precision. That’s why we built MetAI: to turn static blueprints into high-fidelity, simulation- ready 3D digital twin environments where AI agents can learn, optimize, and validate before ever touching the real world. Our infrastructure converts CAD and 2D drawings into SimReady OpenUSD environments, reducing deployment risk and unlocking scalable, vertical-specific AI systems. Backed by NVIDIA and born in Taiwan’s manufacturing heartland, we’re building globally scalable infrastructure for the next era of AI — where intelligent agents don’t just read, but move, act, and operate in the physical world. Just as LLMs need text, physical AI needs digital worlds — and that’s what we provide.” Daniel Yu Co-Founder & CEO at MetAI *first startup in Taiwan to receive an investment from Nvidia
  • 78.
    AI Enterprise MarketMap (by Lightspeed Capital)
  • 79.
    Voice AI MarketMap (by Bessemer Venture Partners)
  • 80.
    The CB InsightsAI 100 map offers a more granular, 2024-focused breakdown of both horizontal and vertical AI companies. Vertical AI refers to startups specializing in specific industries like healthcare, aerospace, and manufacturing, while horizontal AI companies provide general-purpose solutions such as computer vision, productivity tools, and DevOps. AI 100 Market Map (by CB Insights)
  • 81.
  • 82.
    Rise of AgenticAI & AI Agents Agentic AI is the artificial intelligence systems that has the ability to independently make decisions, perceive the tasks and environment at hand, display iterative problem solving, and act in pursuit of a goal—often across multiple steps, environments, and tools—without requiring constant human intervention. Traditional AI is typically constrained to specific tasks and reactive while agentic AI operates with a degree of self-direction and dynamically interacts with humans and its surroundings to secure its outcomes. Agentic AI systems primarily differ from generative AI models, such as ChatGPT and Google's Gemini, since they are designed to make decisions versus simply making content. Additionally, agentic AI works towards established goals and tasks while GenAI models require human prompts. The product form or software entity of an agentic AI platform is an AI agent.
  • 83.
    Example Architecture ofan Agentic AI System
  • 84.
    Agentic AI MarketMap (by Market & Market)
  • 85.
    “Why is vertical-specificAI important? Big models are powerful generalists, but you don’t need a Ferrari to deliver pizza. Every time you use an expensive foundation model to sort data transactions or tag tickets, you’re paying a high price for output that’s correct only a fraction of the time, because it’s not trained in the nuances of your domain. Levro’s AI reinforcement learning platform helps companies train a custom model tailored to their exact workflow (financial assessments, product recommendations, support triage, etc.) that can run at a fraction of the cost and with higher accuracy. No AI research team required.” Cathy Han Co-Founder & CEO at LevroAI (YC S21) Co-Founder & former CEO at 42 Technologies (YC W14)
  • 86.
    AI Agent Growth “AIagents are gaining traction quickly across an array of business applications—and the market for AI agents is expected to grow at a 45% CAGR over the next five years. As AI agents become commonplace—and they will— humans will work closely with them as teammates. AI agents will be onboarded, just like human workers, to learn roles and responsibilities, access relevant company data and business context, integrate into workflows, and support the humans’ responsibilities.” - Boston Consulting Group
  • 87.
  • 88.
    AI Agent Growth AIagents are not just products but are evolving into strategic platforms where developer and user loyalty will create the next billion user hubs or extend the leadership of the trillion dollar tech giants. Google, Amazon, and Microsoft are focused on this strategy as shown by the launch of Azure AI Agent, Vertex AI Agent Builder and Amazon Bedrock Agents. Creating developer lock-in is now a mad, mad race. Apple’s delay into agent development tooling is concerning. Maybe their closed culture is not the approach for this new AI reality?
  • 89.
    AI Agent MarketMap (by CB Insights) “While AI copilots have already made inroads across industries, the next evolution — autonomous agents with greater decision-making scope — is arriving quickly. AI agent startups raised $3.8B in 2024 (nearly tripling 2023’s total), and every big tech player is already developing AI agents or offering the tooling for them. Implications for enterprises will be far-reaching, from altering workforce composition (with new hybrid teams of humans and AI agents) to maximizing operational efficiency through full automation of routine tasks.” - CB Insights
  • 90.
    “Every major technologyshift adds a new layer of abstraction. In the past, requesting annual leave meant walking around the office collecting signatures from your manager and HR. Then ERP systems arrived, and you could submit your request through software. The system took care of the workflow, and you no longer had to think about the process. Today, the problem is complexity. Large enterprises run hundreds of applications. A typical Fortune 500 company uses around 370. Each comes with its own interface, logic, and rules. This leads to two issues: growing dependence on system experts and rising operational costs. AI represents the next step in our evolution. With an agentic platform, your intent becomes action. You no longer need to know which system is behind the scenes. You ask, and it gets done. Whether it's accessing information, starting a workflow, or managing permissions, everything happens through one unified interface. This is what we are building with HUMAIN ONE. One interface to control your entire enterprise. The future is closer than most people think, and it will redefine how work gets done.” Saejong Lee VP and Head of Product for HUMAIN OS Former Director of Product at Aramco Digital Venture Partner at SparkLabs Saudi Arabia
  • 91.
    AI agent isthe product of an agentic AI platform. It is software that perceives its environment, independently makes decisions and executes on its goals and stated tasks. Over the next 3 years, AI agents will have a transformative impact on industries where workflows are repetitive, information-rich, and ripe for automation or augmentation. Here are the top ten industries that will be impacted the most: Industries Impacted the Most within 3 Years 1. Enterprise Software & B2B SaaS AI agents can automate internal workflows, sales ops, customer success, recruiting, and other enterprise functions (e.g., CRM updates, meeting summaries) 2. Customer Service & Contact Centers AI agents can reduce costs by replacing or augmenting Tier 1 and Tier 2 support, and operate 24/7 with high consistency
  • 92.
    Industries Impacted theMost within 3 Years 3. Healthcare Diagnostic assistants, documentation agents, and scribe agents improve back office efficiency and reduce physician burnout. Hopefully reduce the overall cost of our inefficient healthcare system. 4. Finance & Insurance AI agents can analyze documents, manage risks, detect fraud, and create numerous efficiencies across banking and insurance platforms. The cost savings some financial and insurance companies experienced using blockchain will seem miniscule to the impact of AI agents. 5. Education Learning agents and personalized AI tutors can scale teaching while better adapt to learning styles of each student. Additionally this can better support underserved populations.
  • 93.
    Industries Impacted theMost within 3 Years 6. Retail & E-Commerce AI agents will optimize logistics, better personalize shopping experiences, provide instant Q&A, dynamically manage pricing and other tasks. Will this level the playing field for small and medium players or further empower largest companies? 7. Cybersecurity AI agents will continuously monitor threats and attacks, patch, and simulate attacks for improvements. This will allow threat detection and response to scale better 8. Legal & Compliance AI agents can generate legal drafts, execute on mundane legal writing, analyze contracts, and help monitor regulatory changes
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    Industries Impacted theMost within 3 Years 9. Media, Marketing & Content Creation Agents can generate social media posts, assist in advanced content creation, create campaign strategies, make content calendars, analyze audience responses in real-time and other tasks to boost the efficiencies of marketing firms and professionals. 10. Software Development AI agents can map out products and applications, code and build, test applications and deploy software autonomously.
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    “Research in newtraining and inference improvements continue to quickly leap from research to production, driven by a demand for better models from existing data, and from upper bounds on available computing power. The highest quality frontier models might continue to need hyperscalar-level investments, but general-purpose models that are ‘good enough’, and high quality domain-specific models, are rising fast. Privacy-sensitive and regulation-heavy verticals like legal and heathcare need LLM solutions they can put on-prem or on the cloud of their choice, and smart startups are addressing that need. Open weights models, and even truly open source models and software and open data, are driving much of that innovation and commoditization, rhyming with the impact open source has long had on the IT industry. Better structured open data sets, and greater legal and regulatory clarity around training data (you can use it, but you need to obtain it legally), are where corporations, non-profits, and even governments are realizing they can turbocharge model development through collective action.” Brian Behlendorf Former Chief AI Strategist at The Linux Foundation Primary Developer of the Apache Web Server Board Member at Mozilla Foundation, Electronic Frontier Foundation & Filecoin Foundation Former Managing Director at Mithril Capital Advisor at SparkLabs Global Ventures & Venture Partner at SparkLabs AI
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    Generative AI As LLMsbecome more and more powerful, Generative AI developments will continue to advance rapidly. We expect to see: Multimodal models: going forward, models are expected to process text, audio, video and images simultaneously Explosive growth in use cases around generative audio and video, generative design, education, healthcare, just to name a few Robust government regulations that aim to balance innovation and technology advancement with privacy, security and well- being of citizens
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    “Enterprise AI isentering a critical phase, where precision, trust, and contextual intelligence are essential. The real opportunity lies in building AI systems that deeply understand language, intent, and interaction patterns at scale, especially for high- impact business functions like Customer Experience (CX). At Lucidya, we’ve spent years developing enterprise-grade AI Agents trained on billions of real-world conversations and behavioral signals across sectors and channels. This foundation allows us to deliver AI that is not only multilingual and culturally aware, but also tuned to the complexities of CX in enterprise environments. Delivering this level of intelligence comes with a high bar: compliance, privacy, and trust are mandatory. That’s why we’ve built our platform with security and regulatory alignment at its core, because enterprise AI can’t scale unless it respects data, has safeguards and real business outcomes. I believe the future of enterprise AI belongs to those who can deliver domain-specific intelligence that is scalable, compliant, and trusted.” Abdullah Asiri Founder & CEO at Lucidya *recently closed their US$30 million Series B round to become the largest AI-backed startup in MENA
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    Enterprise The top threeareas over the next 2 years: Data Analytics and Business Intelligence: AI-powered data analytics and business intelligence tools are crucial for enterprises aiming to harness the vast amounts of data they generate and collect. Automation and Robotic Process Automation (RPA): Automation, including RPA, is a significant investment area, focusing on automating routine, repetitive tasks to improve efficiency, reduce errors, and free up human employees for more strategic work. Customer Experience and Engagement: Enhancing customer experience and engagement through AI technologies is a key investment area. This includes chatbots and virtual assistants for customer service, personalized marketing, and recommendation systems.
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    Healthcare The top threeareas over the next 2 years: Diagnostic and Imaging Technology: AI plays a crucial role in improving the accuracy and efficiency of medical diagnostics. By analyzing images from MRIs, CT scans, X-rays, and other imaging technologies, AI algorithms can help detect diseases such as cancer, neurological disorders, and cardiovascular conditions earlier Personalized Medicine and Treatment Optimization: AI is being used to tailor treatment plans to individual patients, taking into account their genetic makeup, lifestyle, and other factors. This includes the development of precision medicine, where AI algorithms analyze vast datasets to identify which treatments are likely to be most effective for specific patient profiles. Operational Efficiency and Patient Care Management: AI investments are also targeted towards improving the operational aspects of healthcare provision. This includes the use of AI for managing patient flow, reducing wait times, and improving resource allocation.
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    Finance Top impact areas: AlgorithmicTrading: AI is increasingly being used in algorithmic trading to analyze large datasets and execute trades at speeds and volumes that are beyond human capability. By leveraging AI, financial institutions can identify market trends, predict price movements, and make trading decisions that optimize returns on investments. Robotic Process Automation (RPA) and Back-Office Automation: Financial institutions are investing in AI to automate routine and repetitive tasks in areas such as account processing, compliance checks, and customer service inquiries. This not only improves operational efficiency and reduces costs but also allows human employees to focus on more strategic and customer- focused activities.
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    Cybersecurity Top impact areas: ThreatDetection and Analysis: AI algorithms are being employed to analyze network traffic, identify unusual patterns, and detect potential threats at an early stage. This includes the use of machine learning models to recognize signs of malware, phishing attempts, and other sophisticated cyber attacks that traditional security measures might overlook. Behavioral Analytics and Anomaly Detection: AI-driven behavioral analytics focus on understanding the normal behavior of users and network devices to identify deviations that could indicate a security threat. This area of investment helps in detecting insider threats, compromised credentials, and advanced persistent threats (APTs) that do not match known malware signatures but exhibit anomalous behavior.
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    Transportation The top threeareas that we will explore are: Autonomous Vehicles: AI is the backbone of autonomous vehicle technology, encompassing self-driving cars, drones, and unmanned aerial vehicles. These AI systems process data from various sensors and cameras in real-time, enabling vehicles to navigate complex environments safely. The development and refinement of autonomous vehicles promise to reduce accidents caused by human error, improve traffic flow, and decrease carbon emissions. Traffic Management and Optimization: AI-driven traffic management systems are being deployed to analyze traffic patterns, optimize traffic lights, and reduce congestion. By leveraging real-time data, these systems can predict and manage traffic flows more efficiently, thereby enhancing urban mobility and reducing travel times. This includes the use of AI in public transportation to optimize routes and schedules based on passenger demand. Predictive Maintenance: In the transportation industry, AI is revolutionizing the approach to maintenance. Predictive maintenance uses AI algorithms to analyze data from vehicle sensors and predict when parts might fail or require maintenance. This proactive approach can significantly reduce downtime, extend vehicle life, and lower maintenance costs by addressing issues before they lead to failures.
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    “Farmed fish isthe fastest growing sector of food production, helping to meet the world's demand for healthy protein, and accounting for over half of the fish produced today. Aquaculture is also one of the most sustainable forms of protein production and helps reduce our dependence on overfishing. To grow, the industry needs a way to monitor and improve the health of fish and its local waters. Aquabyte builds incredibly complex AI models based on underwater cameras, computer vision, and ML models based on fish biology, allowing fish farms to monitor and optimize fish growth and health. This data helps fish farmers make data-driven decisions to produce healthier fish faster at lower cost, higher efficiency, and better sustainability.” Bryton Shang Senior Advisor to the NOAA Administrator (National Oceanic & Atmospheric Administration) Founder, former CEO & Executive Chairman of Aquabyte, a pioneering aquaculture technology company leveraging computer vision and AI
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    Agricultural Precision Farming: AItechnologies enable precision farming, which involves the precise and controlled application of water, fertilizers, and pesticides to crops. By analyzing data from soil sensors, drones, and satellites, AI algorithms can optimize these inputs to enhance crop yields, reduce waste, and minimize environmental impact. This approach supports sustainable agriculture practices by ensuring resources are used efficiently. Crop Health Monitoring and Disease Detection: AI-powered imaging and diagnostics tools are being used to monitor crop health and detect diseases early. By analyzing images from drones or satellites, AI can identify signs of stress, pest infestations, or disease in crops, enabling farmers to take targeted action to mitigate losses. This technology is crucial for ensuring food security and maximizing agricultural productivity. Supply Chain Optimization: AI is streamlining agricultural supply chains, making them more efficient and responsive. By analyzing data on weather, crop yields, and market demand, AI algorithms can predict supply chain disruptions and optimize logistics. This includes everything from the timing of harvests to the storage and transportation of produce, ensuring that agricultural products are delivered to markets in the most efficient manner possible. The top three areas impact areas:
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    As artificial intelligence(AI) continues to evolve, its adoption across industries is accelerating at an unprecedented pace. No longer a niche technology, AI is now embedded into core business functions—from predictive analytics in finance to supply chain optimization and personalized marketing. Recent developments have also introduced a powerful new player in this space: Generative AI (GenAI). While both AI and GenAI stem from the same foundational technologies, their applications and implications diverge significantly. Organizations are increasingly leveraging traditional AI to automate decision-making, analyze large datasets, detect fraud, and improve operational efficiency. At the same time, Generative AI is revolutionizing how businesses approach creativity, customer interaction, and content development. From chatbots that compose full emails to models that generate synthetic medical images, GenAI is enabling machines to create original, human-like outputs based on training data. AI Adoption
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    “The big AIcompanies are focused on solving intelligence. We’re solving presence. If you want people to fall in love with AI personas, they need to be more than smart. They need to be something you can feel, connect with, and grow alongside, not just a voice in your head or words on a screen. That’s where games come in and why this Unity partnership is a major milestone. As AI companions become part of everyone's day-to-day, people will want a deeper experience where they can do things with them, not just talk to them. By bringing Genies' technology to the world’s largest game development community, this collaboration empowers creators to build a new generation of AI-driven experiences, marking the beginning of a new era for AI companions in interactive entertainment.” Akash Nigam Co-founder & CEO at Genies *Genies has raised over $200 million from Silver Lake, Bond, NEA, Breyer Capital, Thomas Tull, Bob Iger and others.
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    “Until two yearsago, many enterprise customers questioned the real-world benefits of using AI in production environments. Some were convinced of its value, while others were skeptical. However, starting last year, there has been a dramatic shift. Now, everyone clearly understands the productivity boost AI can provide. Every enterprise customer is experiencing a fear of missing out (FOMO) if they don't integrate AI into their workflows immediately. Meanwhile, AI technology is advancing at a dizzying pace, transforming from simple automation tools for mundane tasks into sophisticated assistants capable of handling complex work alongside humans.” Changsu Lee Founder & CEO at Allganize
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    AI patents arean indicator of innovation and technological leadership. They reflect not only a country’s or company's ability to develop AI technologies, but also their intent to protect and commercialize intellectual property. Tracking AI patent activity helps us understand where the most intensive R&D efforts are taking place and which regions are likely to shape the future of AI. AI Patents
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    This graphic highlightsglobal innovation in artificial intelligence by showing the number of granted AI patents per 100,000 inhabitants in 2022. South Korea and Luxembourg lead significantly, indicating a high concentration of AI research and development activity relative to population size. The United States, Japan, and China also rank prominently, which reflect their roles as global AI leaders. This metric highlights not just technological advancement but also national investment in AI innovation. AI Patents
  • 117.
    “Companies of allsizes must embrace AI not as a luxury, but as a necessity for survival and growth. Large Language Models (LLMs) like ChatGPT offer a broad foundation, enabling organizations to automate knowledge work, enhance decision-making, and unlock new efficiencies. However, the true competitive edge lies in specialized, fine- tuned models tailored to an industry’s unique data, terminology, and workflows. A healthcare provider, a logistics firm, or a financial institution each faces distinct challenges that a generalized model cannot fully address without customization. Those who fail to integrate both general and domain- specific AI into their operations risk falling behind (or dying off completely) competitors who are accelerating innovation, reducing costs, increasing revenues, and delivering smarter, more personalized experiences.” Scott Sorochak Venture Partner at SparkLabs Group Former CRO at Brandwatch Former SVP Global Sales at Livefyre (acquired by Adobe) Former EIR at Foundation Capital
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    Private investment playsa pivotal role in accelerating artificial intelligence (AI) innovation by funding research, startups, and applications across industries. This shows the United States leading global AI private investment with over $67 billion, surpassing China’s $7.76 billion. The large gap reflects the U.S.’s dominance in AI innovation, driven by strong capital markets, a mature venture capital system, infrastructure, and startups. This disparity highlights the strategic role of private funding in shaping global AI leadership. AI Private Investments
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    This graphic highlightsa significant shift in AI investment priorities. In 2023, private funding surged in AI infrastructure, research, and governance, emphasizing a strong focus on building scalable, secure, and responsible AI foundations. While areas like healthcare remained important, the overall trend reflects investor confidence in core AI technologies that support long-term innovation and trustworthiness of models and applications. AI Private Investments
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    Thank You *Written by BernardMoon, William Chu, Bobby Fan, and Selina Zhong www.sparklabsgroup.com