Growth Trends in AI and Data Solutions

As artificial intelligence continues its meteoric rise, we often hear about breakthroughs and new capabilities. But what if the next big challenge isn’t just technical, but about something more fundamental — running out of data? A recent report highlights a looming bottleneck: by 2028, AI developers may exhaust the stock of public online text available for training large language models (LLMs). The rapid growth in model size and complexity is outpacing the slow expansion of usable Internet content, and tightening restrictions on data usage are only compounding the problem. What does this mean for the future of AI? Good piece in Nature outlining some of the key advances in the field. 1️⃣ Shift to Specialized Models: The era of “bigger is better” may give way to smaller, more focused models, tailored to specific tasks. 2️⃣ Synthetic Data: Companies like OpenAI are already leveraging AI-generated content to train AI — a fascinating, but potentially risky, feedback loop. 3️⃣ Exploring New Data Types: From sensory inputs to domain-specific datasets (like healthcare or environmental data), innovation in what counts as “data” is accelerating. 4️⃣ Rethinking Training Strategies: Re-reading existing data, enhancing reinforcement learning, and prioritizing efficiency over scale are paving the way for smarter models that think more deeply. This challenge isn’t just technical; it’s ethical, legal, and creative. Lawsuits from content creators highlight the delicate balance between innovation and intellectual property rights. Meanwhile, researchers are pushing the boundaries of what’s possible with less. Link to piece here: https://lnkd.in/gvRvxJZq

This year, the State of Data and AI Engineering report has been marked by consolidation, innovation and strategic shifts across the data infrastructure landscape. I identified 5 key trends that are defining a data engineering ecosystem that is increasingly AI-driven, performance-focused and strategically realigned. Here's a sneak peek at what the report covers: - The Diminishing MLOps Landscape: As the standalone MLOps space is rapidly consolidating, capabilities are being absorbed into broader platforms, signaling a shift toward unified, end-to-end AI systems. - LLM Accuracy, Monitoring & Performance is Blooming: Following 2024's shift toward LLM accuracy monitoring, ensuring the reliability of generative AI models has moved from "nice-to-have" to business-critical. - AWS Glue and Catalog Vendor Lock-in: While Snowflake just announced read/write support for federated Iceberg REST catalogs, finally loosening its catalog grip, AWS Glue is already offering full read/write federation, and is therefore the neutral catalog of choice for teams avoiding vendor lock-in. - Storage Providers Are Prioritizing Performance: in line with the growing demand for low-latency storage, we see a broader trend in which cloud providers are racing to meet the storage needs of AI and real-time analytics workloads. - BigQuery's Ascent in the Data Warehouse Wars: with 5x the number of customers of both Snowflake and Databricks combined, BigQuery is solidifying its role as a cornerstone of Google Cloud’s data and AI stack. These trends highlight how data engineering is evolving at an unprecedented pace to meet the demands of a rapidly changing technological landscape. Want to dive deeper into these critical insights and understand their implications for your data strategy? Read the full report here: https://lnkd.in/dPCYrgg6 #DataEngineering #AI #DataStrategy #TechTrends #DataInfrastructure #GenerativeAI #DataQuality #MLOps

Since 2012, the Machine Learning, AI & Data (MAD) ecosystem is captured by FirstMark's Landscape reports which show the rapidly evolving ecosystem of AI, data, and analytics. See for an interactive, reader-friendly, and accessible format of the 2024 MAD Landscape: https://mad.firstmark.com/ PDF (below): https://lnkd.in/gwFJfzSe * * * The Landscape's 2024 edition, published in March 2024, now features 2,011 companies, up from 1,416 in 2023 and just 139 in 2012. According to Matt Turck's blog post, providing an overview of the trends, growth is fueled by 2 massive cycles: - The "Data Infrastructure" wave - a decade-long cycle which emphasized data storage, processing, and analytics, from Big Data to the Modern Data Stack. Despite expectations for consolidation in this space, it hasn’t occurred yet, resulting in a large number of companies continuing to operate independently. - The second wave is the "ML/AI cycle", which gained momentum with the rise of Generative AI. Since this cycle is still in its early stages, the MAD Landscape included emerging startups. These 2 waves are deeply interconnected, with the MAD Landscape emphasizing the symbiotic relationship between data infrastructure, analytics/BI and ML/AI, and applications. * * * In the area of AI Governance, Security, and Risk, AI-specific startups and tools are on the rise: - “AI Observability” include startups that help test, evaluate and monitor LLM applications  - “AI Developer Platforms” is close to MLOps, but recognizes the wave of platforms that are wholly focused on AI application development, in particular around LLM training, deployment and inference - “AI Safety & Security” includes companies addressing concerns innate to LLMs, from hallucination to ethics, regulatory compliance, etc * * * 24 key themes shaping the industry are identified: - Distinct pipelines and tools for structured and unstructured data - Maturation and potential consolidation of the Modern Data Stack - Data Quality and Observability: Growing importance of tools that ensure data accuracy and reliability - Increasing focus on data governance frameworks and privacy regulations - Rise of technologies enabling real-time data analytics and decision-making - Data Integration and Interoperability - Data Democratization: Broader access to data and analytics tools - Recognizing the critical contributions of Data Engineers - Impact of Generative AI - Hybrid Future: Coexistence and integration of LLMs and SLMs - Relevance of traditional AI approaches in the era of GenAI - Strategies of orgs building on top of existing AI models vs. developing comprehensive solutions - AI Agents and Edge AI - AI Safety and Ethics - AI Regulation and Policy implications for businesses - Demand for AI Talent and Education - AI in Healthcare - AI in Finance - AI in Retail and E-commerce - AI in Manufacturing - AI in Education - AI in Entertainment and Media - AI and Climate Change - The Future of Work

Top 10 research trends from the State of AI 2024 report: ✨Convergence in Model Performance: The gap between leading frontier AI models, such as OpenAI's o1 and competitors like Claude 3.5 Sonnet, Gemini 1.5, and Grok 2, is closing. While models are becoming similarly capable, especially in coding and factual recall, subtle differences remain in reasoning and open-ended problem-solving. ✨Planning and Reasoning: LLMs are evolving to incorporate more advanced reasoning techniques, such as chain-of-thought reasoning. OpenAI's o1, for instance, uses RL to improve reasoning in complex tasks like multi-layered math, coding, and scientific problems, positioning it as a standout in logical tasks. ✨Multimodal Research: Foundation models are breaking out of the language-only realm to integrate with multimodal domains like biology, genomics, mathematics, and neuroscience. Models like Llama 3.2, equipped with multimodal capabilities, are able to handle increasingly complex tasks in various scientific fields. ✨Model Shrinking: Research shows that it's possible to prune large AI models (removing layers or neurons) without significant performance losses, enabling more efficient models for on-device deployment. This is crucial for edge AI applications on devices like smartphones. ✨Rise of Distilled Models: Distillation, a process where smaller models are trained to replicate the behavior of larger models, has become a key technique. Companies like Google have embraced this for their Gemini models, reducing computational requirements without sacrificing performance. ✨Synthetic Data Adoption: Synthetic data, previously met with skepticism, is now widely used for training large models, especially when real data is limited. It plays a crucial role in training smaller, on-device models and has proven effective in generating high-quality instruction datasets. ✨Benchmarking Challenges: A significant trend is the scrutiny and improvement of benchmarks used to evaluate AI models. Concerns about data contamination, particularly in well-used benchmarks like GSM8K, have led to re-evaluations and new, more robust testing methods. ✨RL and Open-Ended Learning: RL continues to gain traction, with applications in improving LLM-based agents. Models are increasingly being designed to exhibit open-ended learning, allowing them to evolve and adapt to new tasks and environments. ✨Chinese Competition: Despite US sanctions, Chinese AI labs are making significant strides in model development, showing strong results in areas like coding and math, gaining traction on international leaderboards. ✨Advances in Protein and Drug Design: AI models are being successfully applied to biological domains, particularly in protein folding and drug discovery. AlphaFold 3 and its competitors are pushing the boundaries of biological interaction modeling, helping researchers understand complex molecular structures and interactions. #StateofAIReport2024 #AITrends #AI

The 2025 AI Index Report from Stanford Institute for Human-Centered Artificial Intelligence (HAI) is out. The report shows AI’s rapid growth and deepening impact globally. Key highlights: AI Adoption and Investment: Corporate investment rebounded significantly, especially in generative AI, with U.S. investment ($109.1B) vastly surpassing China ($9.3B) and the UK ($4.5B). AI usage is widespread, with 78% of organizations now using AI. Technical Advances: AI models continue to improve rapidly on complex benchmarks, notably in multimodal understanding and software development, narrowing the performance gap between global competitors (particularly the U.S. and China). Notably, AI inference costs have drastically decreased (280-fold cheaper in less than two years). Responsible AI and Trust: Despite advancements, significant concerns remain around data privacy, bias, fairness, and misinformation. Incidents related to AI have risen sharply (56.4% increase from the previous year), prompting intensified global regulatory efforts. Education and Workforce: Interest and enrollment in AI education are increasing, yet many educators still feel unequipped to teach AI effectively. AI continues to demonstrate a positive impact on workforce productivity and is helping to close skill gaps. Science and Healthcare: AI breakthroughs are driving scientific discoveries and dramatically improving healthcare, outperforming humans in some diagnostic tasks. FDA-approved AI-enabled medical devices rose sharply from six in 2015 to 223 in 2023. Geopolitical Dynamics: The U.S. leads in AI model development, but China dominates in AI patents and publications. Global AI optimism is increasing but remains uneven geographically, with higher optimism in Asia compared to the West. Environmental Impact: Energy efficiency in AI hardware improved substantially, but carbon emissions from AI training remain high, raising sustainability concerns. #ai #genai #stanford

As we step into 2024, I want to extend my warmest wishes to everyone in The Ravit Show Data & AI Community. As we toast to a new beginning, let’s look at 8 Key Trends in Data & AI -- 1. Semantic Layer: This year marks a significant leap in how machines interpret data. We're moving towards a semantic approach where data is not just numbers and text, but meaningful information that machines can understand contextually, and how we interact with AI systems. 2. Data Products: The concept of 'data as a product' is gaining momentum. It’s not just about collecting data anymore; it’s about refining it into a product that delivers real value - turning raw data into a strategic asset for better decision-making and customer insights. 3. Data Platforms: 2024 is seeing the evolution of data platforms into more sophisticated, integrated systems. These platforms are becoming the linchpin of our digital ecosystem, offering seamless access, processing, and analysis of data across various domains. 4. Multimodal Large Language Models (LLMs): LLMs are now going beyond text to understand and interpret multimedia content. This evolution opens up new avenues for AI applications in areas like content creation, media analysis, and interactive entertainment. 5. New Revenue Streams for Cloud Providers in Generative AI: Cloud computing is getting a major boost from generative AI. This symbiosis is creating novel revenue opportunities and transforming how we think about cloud services and AI capabilities. 6. Rise of Prompt Engineering: As AI becomes more prevalent, the art of prompt engineering is becoming critical. It's about effectively communicating with AI to generate precise and relevant outputs, a skill that's rapidly becoming essential in the tech workforce. 7. Data Privacy, Security, and Responsible AI Practices: With great power comes great responsibility. In 2024, there's an intensified focus on ethical AI, prioritizing data privacy and security. It's about building AI systems that are not only powerful but also trustworthy and responsible. 8. Metadata Management: 2024 is witnessing a surge in the importance of metadata in Data & AI. As we deal with ever-increasing volumes of data, managing metadata – the data about data – is becoming crucial. It’s not just about storing and accessing data anymore; it's about understanding its context, quality, and lineage. Effective metadata management leads to better data governance, quality, and usability, making it a pivotal aspect of data strategy in organizations. These trends are not just predictions; they are the pathways leading us to a more innovative and efficient future in Data & AI. What would you like to add? #data #datascience #datapredictions2024 #theravitshow

The McKinsey Technology Trends report highlights critical shifts in AI and Generative AI (GenAI) job growth, with substantial implications for CPG and MarTech companies. 📍There is no surprise here: AI and GenAI are at the forefront of growth. GenAI, in particular, shows strong growth in adoption, innovation, and investment. While some sectors, like next-gen software development, saw a decline in job postings, GenAI is rising by 341%. Investing in AI-driven solutions will enhance operational efficiency, supply chain management, and consumer insights. 📍I also see that GenAI is transitioning into large-scale adoption. It is becoming essential in creating hyper-personalized marketing strategies and product innovation. For CPG brands, GenAI already revolutionized content creation, delivering tailored ads and dynamic consumer experiences in real-time. MarTech companies currently leverage GenAI for advanced customer engagement and automated campaign management. 📍Despite a slight drop in job postings, cloud and edge computing are essential for deploying AI solutions at scale, especially for real-time processing and decision-making. The demand for Industrialized Machine Learning specialists is growing, as companies require infrastructure to support AI scaling across operations. Predictive analytics powered by machine learning will optimize supply chains and customer journey mapping, enabling more efficient marketing spend. 📍As AI talent becomes scarce, CPG companies must focus on upskilling their workforce and forming strategic partnerships to build a sustainable AI talent pipeline. Developing in-house AI expertise and cross-disciplinary teams that understand both AI and CPG will be critical for maintaining competitive advantage. We'll see more Chief AI Officers (CAIO) at the helm of those teams. 𝗧𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗹 𝗼����𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀, 𝗳𝗼𝗹𝗹𝗼𝘄 ecommert® 𝗮𝗻𝗱 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝘁𝗼 𝗼𝘂𝗿 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 👇 #ArtificialIntelligence #AI #GenAI #technology #technologytrends #retailmedia #digitalshelf #CPG #FMCG #Brands #growth #strategy

Trending AI Categories I've Been Exploring Lately I'm seeing a lot of #venture activity across these 9 #AI categories— with huge traction and emerging winners, even though many of these companies are still very young. And while it's always risky to enter categories where leaders are already emerging, these startups are operating in massive, untapped greenfield markets. That’s why there’s also a big wave of smaller players, often just a year behind and with significantly lower valuations, trying to catch up or focus on narrower slices within the categories below that require more customization. In many cases, the playbook is becoming increasingly transparent. 1. AI Voice Agents Enabling Businesses – Lower latency, more natural voices, better handling of interruptions, emotions, reduced costs, improved memory. Use cases span BPOs, insurance, clinics, airlines, recruiting in staffing agencies—any industry with heavy phone use. All with a similar tech stack: Pipecat + LLMs + Sesame, Cartesia, or ElevenLabs. 2. AI D2C Doctor on Top of a Health System of Record – A new B2C playbook is emerging, very focused on continuous testing (blood, MRIs, urine, wearables) and monitoring. Startups target specific geos or niches (e.g., athlete wannabes, millionaires) and often include celebrities for top-of-funnel and trusted doctors on the cap table (Prenuvo, Ezra, Biograph, Nekko, FunctionHealth, Eternal.co). 3. Web3 & AI Decentralization – Solving for data labeling, aggregating available computing resources, and agent verification, all with decentralized solutions (KlusterAI, Fraction, GensynAI, 0g). Getting rid of AI bottlenecks using decentralization. 4. Next-Gen AI Chips – RISC-V, data center networking, silicon photonics, and edge computing innovations optimized for AI (Xscape, iPronics, Etched, Taalas, Enfabrica, Celestial, Lightmatter, d-matrix, Encharge, Tenstorrent, Rivos, Hailo, SiMa, Retym). 5. Agent Platforms – AI Everywhere – AI agents across enterprise, customer support, and workflow automation. 6. Low-Code & No-Code AI – Democratizing AI development, enabling faster adoption across industries, either building AI assistants (Stackai, nxn.io) or building high quality software without writing code (Replit, Lovable, Glide) 7. Saturation in AI medical Scribes – High adoption but also high competition in AI-powered medical scribes (Abridge, Nabla, Ambience, Voize, Freed). 8. AI in Clinical Trials – AI optimizing recruitment, protocol design, data analysis, and regulatory compliance (Grove trials). 9. AI for legal workflows - whether it is drafting, contract review and negotiation, risk detection, legal search, due diligence, summarization (Harvey, Eudia, Robin AI, Wordsmith)

Recommended report: World Intellectual Property Organization (2024). #Generative #Artificial #Intelligence. Patent Landscape Report. Geneva. 💡 This report provides an analysis of patenting activity and scientific publications in the field of Generative Artificial Intelligence (#GenAI). It aims to shed light on the current technology development, key players, and potential applications of GenAI technologies. 👍🏼 #Goals: - Examine the global development and trends in GenAI patenting and research - Analyze patent trends for different GenAI models, modes (data types), and application areas >> The number of GenAI patent families has grown from just 733 in 2014 to over 14,000 in 2023, an increase of over 800% since the introduction of the transformer architecture for large language models in 2017. >> The growth in scientific publications has been even more dramatic, increasing from only 116 publications in 2014 to more than 34,000 in 2023. >> Over 25% of all GenAI patents and over 45% of all GenAI scientific publications were published in 2023 alone. 🧠 The remarkable surge in GenAI patents and #publications, especially in recent years, underscores the disruptive potential of these technologies and the intense race among companies and research institutions to secure intellectual property rights and drive innovation in this rapidly evolving domain. 👁️ 5 Key Ideas: 1. GenAI patent families and scientific publications have increased significantly since 2017, driven by advancements in deep learning, availability of large datasets, and improved AI algorithms. 2. Tencent, Ping An Insurance Group, and Baidu are the top patent owners in GenAI, with Chinese organizations dominating the top rankings. 3. China, the United States, and South Korea are the leading locations for GenAI invention based on patent data. 4. Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and decoder-based Large Language Models (#LLMs) are the main GenAI models with the most patents. 5. Key application areas for GenAI patents include software, life sciences, document management, business solutions, industry and manufacturing, transportation, security, and telecommunications. 🎯 3 Conclusions: 1. The release of OpenAI's ChatGPT in 2022 has been a pivotal moment for GenAI, driving public enthusiasm and further research and development efforts. 2. While Chinese organizations lead in terms of GenAI #patenting, companies like Alphabet/Google, IBM, and Microsoft are also major players, particularly in scientific publications and impactful research. 3. GenAI is expected to have a significant impact across various industries, enabling applications such as drug development, content creation, customer service, product design, and autonomous driving. Source: https://lnkd.in/eAZy-Pvc

4 Game-Changing Trends in Data & AI for 2025 The next year will be a turning point for organizations leveraging AI. The difference between leaders and laggards will be defined by their ability to navigate these emerging realities: 1. GenAI will shift from broad applications to targeted impact. The days of “GenAI for everything” are numbered. The real value lies in contextual use cases—AI solutions designed to address specific business problems with precision. Broad, one-size-fits-all approaches may dazzle, but they rarely deliver sustained value. 2. The AI talent shortage will intensify. The market will continue to be flooded with resumes, but identifying individuals who can genuinely drive impact will be harder than ever. Organizations that succeed will prioritize strategic hiring frameworks that distinguish technical skill from real-world execution. 3. Organizational design will take center stage. Great data alone isn’t enough. Companies will likely begin to focus on restructuring workflows, eliminating silos, and fostering collaboration to unlock the full potential of AI initiatives. Alignment between teams will be as critical as alignment between datasets. 4. Businesses will invest in AI with greater precision. The exuberance surrounding AI isn’t fading, but it’s becoming more intentional. Leaders will evaluate initiatives based on their ability to generate measurable ROI. AI investments will shift from exploratory to outcome-driven. 💡 Organizations that embrace these trends will position themselves for sustainable growth. Those that don’t risk being left behind in an increasingly competitive landscape. Which of these trends resonates most with your current challenges? I’d love to hear your thoughts. #AI #DataAnalytics #Leadership #GenerativeAI #BusinessInnovation