Trends in Technology Companies Within the AI Landscape

🤔 As we are nearing end of 2024, it is that time when everyone looks for comparing “what really happened with enterprise AI adoption”. I read through this fascinating report from Menlo Ventures that validates many trends. The numbers are staggering - enterprise AI spending surged to $13.8B in 2024, a 6x jump from 2023! But what really caught my attention is a validation that how we have moved from experimentation to execution. Three trends particularly stand out to me: 1. The rise of AI agents is real - while most current implementations focus on augmenting human workflows, seeing early examples of autonomous AI systems managing complex end-to-end processes. - bottomline, this isn't just automation - it's transformation. 2. Technical departments still lead adoption (49% of spend), but what is exciting is seeing AI budgets flowing to every department - from Sales to HR to Legal. - this widespread adoption signals AI's transition from a tech tool to a fundamental business capability. 3. The multi-model approach is winning- organizations typically deploy 3+ foundation models in their AI stacks, choosing different models for different use cases. - interestingly, while OpenAI's share has decreased to 34%, Anthropic doubled its presence to 24% in the enterprise space. 4. RAG (retrieval-augmented generation) is dominating at 51% adoption, up from 31% last year. - but here's a surprise - only 9% of production models are fine-tuned. Real-world implementation looks different from the hype. 5. Implementation costs are the hidden gotcha- while only 1% worry about purchase price, implementation costs derailed 26% of failed pilots. 6. The incumbent advantage is cracking- while ~60% still prefer established vendors, 40% question if current solutions truly meet their needs. - that's a massive opportunity for innovative startups. 7. Vertical AI is having its moment- no surprise, this provides maximum value for highly regulated industries - healthcare is leading, followed by Financial Services. - I advocate for AI solutions tackling industry-specific workflows in regulated industries rather than just generic use cases. So, what fascinates me most? The pragmatism, really, - companies aren't fixated on price (only 1% cited it as a concern!) - they're focused on ROI and industry-specific customization. This is not just tech evolution, it is business-centric and high time for incumbents to hone in on domain strengths in solving for AI-powered transformation - get reading for 2025 🚀 And, well to me, that is a clear sign of a maturing market. 🔍 Source: "2024: The State of Generative AI in the Enterprise" by Menlo Ventures (November 2024) - https://lnkd.in/g6j-nPVp What trends are you seeing in enterprise AI adoption? Would love to hear your perspectives! #artificialintelligence #innovation #technology #reflectingonAIin2024

🤔 Just ran the numbers, and I'm seeing a fascinating shift coming in the #AI and #Cloud landscape... The conventional wisdom that agentic AI would naturally gravitate to hyperscaler platforms is proving to be more myth than reality. Here's what's really happening: Processor Evolution • Most agentic AI systems are leveraging commodity processors • The dependency on specialized GPUs is diminishing • Simple CPU clusters are handling many AI workloads effectively Cost Reality Check • Hyperscaler margins (40-60%) are becoming harder to justify • Private clouds delivering 50-70% cost savings for AI workloads • MSPs and colos offering more flexible, cost-effective solutions Market Adaptation • Sovereign clouds gaining traction with regionalized AI solutions • Enterprise IT becoming more sophisticated about true TCO • Multi-cloud strategies focusing on cost optimization over brand names 🎯 The Reality: By end of 2025, we'll see that AWS, Azure, and GCP missed their AI growth targets significantly. The market is speaking - agentic AI doesn't need hyperscaler infrastructure to thrive. 💡 My Prediction: Watch for a massive shift toward hybrid architectures, with agentic AI workloads running primarily on optimized private infrastructure and smaller, specialized providers. #CloudComputing #ArtificialIntelligence #TechTrends #CloudStrategy #Enterprise #Innovation Thoughts? Would love to hear your perspectives on this shift.

The landscape of global innovation is shifting at an unprecedented pace, and recent trends at Tekedia Capital highlight a critical divergence. Our latest cohort of 18 companies, invested in just three months ago, is demonstrating the fastest revenue growth we've ever observed, surpassing even the strong performance of our October 2024 class. This earlier class included a remarkable company that achieved a $10 million Annual Recurring Revenue (ARR) within four months of its inception and is now on track for $100 million within its first year, having recently secured $36 million in funding. This data leads to a stark conclusion: the innovation gap between developing and developed nations is not merely present, but rapidly widening to an asymmetrical degree. The defining characteristic of leading companies today is "AI-nativity." These are not just firms using artificial intelligence, but rather businesses fundamentally built upon AI, regardless of their industry. For instance, the most successful insurance companies of tomorrow will likely be AI companies that happen to offer insurance products, not traditional insurance firms merely integrating AI. Similarly, a premier online tutor like ChatGPT isn't an edtech company that uses AI; it's an AI company providing educational services. When we contrast these emerging, AI-native enterprises with many African startups, a troubling pattern emerges. The gap in the pace of innovation and the value delivered to users is expanding. There's a tangible risk that many African Software-as-a-Service (SaaS) companies could face overnight disintermediation. This observation is not an alarmist prediction, but a conclusion drawn from reviewing the financials of dozens of companies across five continents in Tekedia Capital. In this rapidly accelerating era of AI, many African startups are struggling to keep pace, and a significant portion of our SaaS companies could potentially disappear by 2027. Urgent action is required. We must fundamentally rethink our approach to building businesses to ensure Africa can compete and win in this new global landscape. I have called the landscape the accelerated society era and we must find our level there productively https://lnkd.in/extqvMQz

Digital Health in Health Assessment & Medical Diagnostics Global Startups Landscape 2.5Q 2024 is evolving rapidly, driven by AI-powered innovations across various medical fields. AI-Driven Diagnostics: AI is at the forefront of medical diagnostics. Startups like Aiberry (mental health) and Aidoc (cardiovascular health) are using AI to analyze data in real-time, improving early diagnosis and decision-making. These technologies offer non-invasive, faster, and more accurate assessments than traditional methods. Medical Imaging and Radiology: AI-powered imaging is a key area, with startups like Aidence (lung cancer screening) and Paige (digital pathology) leading the way in enhancing radiological diagnostics. These companies are pushing the boundaries of precision medicine, improving early detection and workflow efficiencies for radiologists and pathologists alike. Portable and Wearable Devices: Portable and wearable diagnostic tools are gaining prominence, exemplified by Butterfly Network, Inc. (handheld ultrasound) and Hyperfine, Inc. (portable MRI). These startups are making high-quality medical imaging more accessible, especially in underserved regions. Predictive and Personalized Medicine: Companies like Cardiosense (cardiovascular health) and Freenome (cancer detection) are leveraging multi-sensor devices and AI to predict disease onset, providing personalized treatment recommendations. This shift toward predictive healthcare is reshaping patient care, enabling more proactive intervention strategies. Voice and Speech Biomarkers: In mental health, companies like Sonde Health, Inc. and Kintsugi are innovating by using voice technology to detect signs of depression and anxiety, proving the versatility of AI in mental health diagnostics and offering real-time mental health assessments. Women’s Health: LEVY Health (endocrine disorders and fertility), Sonio (prenatal ultrasound), and Nevia bio (early disease detection) are advancing women’s health diagnostics, focusing on reproductive and prenatal health through AI-powered decision support platforms. Cross-Specialty Diagnostics: Startups such as Viz.ai and PathAI provide cross-specialty diagnostic tools, focusing on synchronizing care in fields like neurology and pathology. Viz.ai facilitates faster stroke care with its AI-driven platform, whereas PathAI uses AI to enhance diagnostic accuracy in pathology, especially in cancer diagnostics. Global startups in this space are attracting significant investments, with companies like Aidoc raising substantial funds to expand their platforms to more conditions and regions. Achieving CE marking and FDA clearances, as seen with companies like Ultromics, is essential for global expansion and validation. #DigitalHealth #Healthcare #Assessment #Medical #Diagnostics #AIinHealthcare 

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

One of our most anticipated reports each year is out—a comprehensive look at the most significant tech trends unfolding today, from agentic AI to the future of mobility to bioengineering. It provides CEOs with insights on how to embrace frontier technology that has the potential to transform industries and create new opportunities for growth.   Here’s my top-line take: —Equity investments rose in 10 out of 13 tech trends in 2024, with 7 of those trends recovering from declines in the previous year. This rebound signals growing confidence in emerging technologies. —We're witnessing a significant shift in autonomous systems going from pilots to practical applications. Systems like robots and digital agents, are not only executing tasks but also learning and adapting. Agentic AI saw a $1.1 billion equity investment in 2024 alone. —The interface between humans and machines is becoming more natural and intuitive. Advances in immersive training environments, haptic robotics, voice-driven copilots, and sensor-enabled wearables are making technology more responsive to human needs. —And, of course, the AI effect stands out as both a powerful trend in its own right and a foundational amplifier of others. AI is accelerating robotics training, advancing bioengineering discoveries, optimizing energy systems, and more. The sheer scale of investment in AI is staggering, with $124.3 billion in equity investment in 2024 alone.   Let's discuss: Which of these trends do you think will have the most significant impact on your industry? Share your thoughts in the comments below!   Big thanks to my colleagues Lareina Yee, Michael Chui, Roger Roberts, and Sven Smit.   #TechTrends #AI #Innovation #FutureOfWork #EmergingTech http://mck.co/techtrends

Just analyzed $12B worth of enterprise tech spend data from Q1—and the trends are crystal clear: AI-native products are eating the world, but with a crucial caveat. The real winners aren't just slapping "AI" on their pitch decks. They're the ones who deeply understand which specific problems AI can uniquely solve for their customers. Mid-market Acquisition Leaders: 1. Notion (nearly 3x customer growth in 2 years!) 2. Databricks 3. Qualified 4. Assembled 5. Wiz 6. Deel (making waves, but for different reasons) 7. Goldcast SMB Acquisition Leaders: 1. SEMrush 2. dbt Cloud 3. UserTesting 4. Jellyfish 5. Ethena The pattern? Clear value propositions in spaces prime for AI disruption. Plus critical infrastructure plays (Databricks, dbt Cloud, Wiz) that power the AI revolution. On the Decline: Mid-market decliners include DocuSign, PagerDuty, Browserstack, Checkr, Apollo.io, Clearbit, and Expensify. In SMB, we're seeing drops for LinkedIn Recruiter, ZoomInfo, Lattice, Sage Intacct, and Salesloft. Many are former high-flyers now struggling to differentiate from newer, cheaper alternatives solving identical pain points. The lesson is clear: many companies treat AI as a hammer looking for a nail. The companies winning aren't just using the AI hammer better—they're "nailing" the pain points and solving real problems better than anyone else. What tech stack changes are you seeing? Which tools are delivering outsized value in your organization?

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

Agentic AI trends that are a reality already (or someone's working on it 😄): 1. AI Agents won’t just save time — they’ll make money. AI agents will shift from boosting productivity to generating revenue directly. ⏩️ Example: An agent closes outbound deals, writes term sheets, or wins new clients autonomously. 2. Agents will help phase out legacy systems. Instead of replacing old CRMs or ERPs overnight, agents will quietly absorb and replace them from the outside in. ⏩️ Example: An agent learns your workflow, automates key actions, makes the system obsolete over time, and codes them. 3. Agents can mimic human behavior. New AI agents simulate real personalities and groups — unlocking a new kind of behavioral A/B testing. ⏩️ Example: Test how 1,000 investors might react to your pitch deck before ever sending it. Take a look at the research from Stanford University. Link in the comments. 4. Agents will pay each other. Financially autonomous agents can now manage wallets, pay for APIs, or contract other agents. ⏩️ Example: One agent pays another to complete a task, like gathering market data or translating a deck. Project: Payman Ai 5. AI-native fraud is coming fast. Fake voices, documents, and faces will flood markets — especially in finance, identity, and compliance. ⏩️ Example: A deepfaked CEO voice authorizes a $1M transaction. Detection tools must keep up. 6. AI-native institutions are next. AI VCs already exist - AI banks, PE firms, and hedge funds are on the horizon. ⏩️ Example: An AI agent allocates capital, writes IC memos, and reports to LPs without human input. We are building something fascinating here. But also check out one of the Y Combinator startups I left in the comments. 7. New multimodal AI like GPT-4o changes the game. Agents can now see, hear, and speak - making them more useful in real-world tasks. ⏩️ Example: An agent reads a contract PDF, checks for risks, explains it on a call, and sends a summary. 8. AI agents will be insured. As agents make critical decisions, enterprises will insure them like human employees, but we still need to minimize hallucinations. ⏩️ Example: A credit agent makes a false investment call → insurance covers the loss. ARE WE IN THE FUTURE? #AI

AI Agents Are Moving From Hype to Everyday Tools Forbes just released its AI 50 2025 list - and it’s one of the clearest looks yet at how the AI ecosystem is maturing. The companies are organized into two big layers: → Apps: what we use to interact with AI → Infrastructure: what powers those tools behind the scenes What’s especially interesting this year is the rise of #AIagents - tools that can take action, not just generate content. A few examples that stood out: → Sales & Customer Tools - Startups like  Clay  and  Sierra  are helping teams personalize outreach, automate follow-ups, and keep customer conversations going with minimal manual effort. → Developer Productivity Tools like  Codeium and Cursor  are making it easier for engineers to write, debug, and ship code faster - imagine a coding assistant that learns your workflow. → Creative AI Platforms like Runway , Pika , ElevenLabs are showing up in video editing, design, and voice - helping individuals and teams produce high-quality content in less time. → Legal and Health AI Agents like Harvey (law) and Abridge (medicine) are being trained on industry-specific workflows. These aren’t general-purpose chatbots,  they’re becoming collaborators in highly specialized fields. On the infrastructure side, companies like  LangChain ,  Fireworks AI, and   Together AI are helping these apps go beyond chat - enabling reasoning, memory, and multi-step decision-making. 👉 The key shift: We’re moving from “AI that talks” to AI that helps you get stuff done. If you’ve been wondering where the real use cases are emerging, this list is a great place to start. Which of these AI companies are you already using  or curious to try? Drop them in the comments! #AI  #ForbesAI50  #ArtificialIntelligence #TechTrends #FutureOfWork  #VC  #Startups ~~~ Enjoy this? ♻️ Repost it to your network and follow Valerie Bertele 🚀 for more news on #AI, #Investing and  #Innovation 🧠