American Leadership’s Post

Corporate GenAI spending soars Business investment in genAI increased by a staggering 500% this year, growing from $2.3 billion in 2023 to $13.8 billion, according to a Menlo Ventures report. I read this story at CNBC and the study, which surveyed 600 IT decision-makers at companies with 50 or more employees, revealed notable shifts in enterprise AI market dynamics. OpenAI’s market share fell from 50% to 34%, while Anthropic doubled its share from 12% to 24%. Menlo Ventures, an investor in Anthropic, attributed this shift to the success of Claude 3.5 and the trend of companies utilizing multiple large AI models. Other players saw varied changes: Meta’s market share held steady at 16%, Cohere remained at 3%, Google increased from 7% to 12%, and Mistral dropped one percentage point to 5% in 2024. Foundation models, including OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude, dominated enterprise spending. Large language models alone received $6.5 billion in investment, highlighting their central role in generative AI adoption. The report highlighted AI agents—advanced tools capable of performing complex, multistep tasks—as a major area of interest and investment for 2024. Companies such as Google, Microsoft, Amazon, OpenAI, and Anthropic are actively developing this technology. Unlike traditional chatbots, AI agents can generate their own to-do lists and handle tasks with minimal user input. Among GenAI use cases, code generation emerged as the most prominent, with over 50% of respondents naming it as a primary application. Other uses included support chatbots (31%), enterprise search and retrieval, data extraction and transformation, and meeting summarization. #genai #ai #corporate

AI is incredibly exciting - but it must be adopted strategically. As we build future-focused new AI products here at Firstsource and support our clients as strategic advisors with selection and adoption, one of the things we emphasise is that its about delivering tailored, sustainable value for the implementing organisation, not just the next shiny new thing. A key question to ask if you are looking at a significant transformation grounded in AI: what is our strategy to ensure that our AI use and governance linked to this project will mature as our business does? #AI #businesstransformation #AIadoption #relAI

Feeling overwhelmed by all the AI chatbot options out there in the market? Well its a common feeling and our latest article breaks down the pros and cons of the top AI contenders like ChatGPT, Microsoft Copilot, Gemini, and Perplexity in detail to help you find your perfect AI buddy. In this post, you'll discover the following: -Key features of each of these AI chat tools. -What tasks they excel at and things they currently lack. -How to choose the best out of them for your needs. Find out which one works best for your everyday tasks! Read the full post here: https://lnkd.in/gvHAdkc5 And we'd love to see your comments, if any :). #AI #artificialintelligence #machinelearning #productivity #creativity #research #communication #technology

Really interesting insights; Best read this week. Generative AI is transforming enterprises, moving beyond prototypes to deliver measurable ROI. Good insights from 2024 Menlo Ventures report that show domain-specific models are gaining traction over generalized ones, while infrastructure challenges like scalability, cost, and latency remain critical. Success depends on aligning AI use cases with business goals, ensuring robust governance, and human in the loop is inevitable at this point. Two key questions that remain open at this point. What strategies will effectively address latency and scalability challenges at scale? Will this result in more controlled financial operations for Gen AI applications especially in the absence of a proven ROI framework ? How do we ensure ethical, unbiased outputs as AI systems become increasingly autonomous? #EnterpriseAI #GenAI #ResponsibleAI

Who’s going to lead AI in 2025? Google, Grok, LLaMA, or ChatGPT? Case for OpenAI OpenAI has a first-to-market advantage, pioneering the modern AI chatbot landscape with significant headway in technology, brand recognition, and adoption. Its tools, like ChatGPT, reach millions, cementing OpenAI as a top choice for individuals and businesses. A partnership with Microsoft embeds its technology into enterprise ecosystems like Azure and Office 365, ensuring widespread reach and seamless integration. However, OpenAI faces challenges like talent attrition due to internal tensions, which could hinder innovation. Its ambitious AGI mission might shift focus from practical, market-dominant applications, while rivals like Grok and LLaMA press hard with open-source models and unique ecosystems. Case for LLaMA by Meta Meta’s LLaMA stands out with its open-source approach, encouraging global AI adoption and fostering innovation among developers. By lowering barriers to entry, it challenges proprietary platforms, promoting transparency, fairness, and pricing improvements. LLaMA plays a key role in democratizing AI for industries and regions excluded by high-cost, closed systems. However, its strategic role remains undefined, with uncertainty about its long-term purpose in the competitive AI landscape. Additionally, Meta’s reputation may affect market trust and adoption of the platform. Case for Google Google’s comeback story is highlighted by rapid advancements after a slow start, such as Veo 2 in text-to-video generation and breakthroughs like the Willow quantum computing chip. Google Workspace integration allows deep AI-powered productivity, while YouTube and search expertise provide unmatched data assets for training generative AI. Challenges include overcoming early missteps and maintaining relentless innovation amidst fierce competition from OpenAI and Meta. Case for Grok With Elon Musk at the helm, Grok boasts unmatched speed and vision. Leveraging X (formerly Twitter) for real-time data, Tesla’s Dojo supercomputer, and synergies across Musk’s ventures, Grok has unparalleled resources. Musk’s track record of disruption sets Grok apart as a bold, fast-moving contender in the AI race. Special mention to Anthropic’s Claude—useful for specific tasks like developing artifacts, but not my primary AI choice. I got Grok. You?

🔍 The Curious Case of ChatGPT Crashing: A Lesson in LLM Complexity If you’ve been following recent AI developments, you might have heard about an unusual glitch in ChatGPT: it crashed whenever the name David Meyer was mentioned. Surprisingly, this wasn’t an isolated issue—other names triggered similar behavior. While OpenAI resolved the problem within a few days, it left many scratching their heads about the deeper implications. What’s behind this anomaly? It’s a stark reminder of the sheer complexity of Large Language Models (LLMs). As per the recent deep series, at their core, these models rely on Reinforcement Learning from Human Feedback (RLHF) to refine responses and align them with user expectations. However, even with billions of parameters and rigorous training, unexpected issues can arise—especially when handling edge cases, biases, or the nuances of real-world data. Why does this matter for businesses and AI adopters? 1️⃣ Unintended Consequences: Even seemingly minor tweaks in an LLM’s training or deployment can have far-reaching impacts. A name triggering crashes might seem trivial, but it reflects the unpredictable nature of how these systems interpret and respond to inputs. 2️⃣ Trust and Reliability: For organizations deploying LLMs at scale, trust is everything. Imagine if your AI-powered service faced downtime because of a glitch like this. It’s a reminder that robust testing and monitoring are critical. 3️⃣ Ethical Considerations: RLHF helps shape models to reflect societal values, but it also introduces potential blind spots. Biases or errors in feedback loops can inadvertently create vulnerabilities. Reflection for the Future As we continue to deploy LLMs, we must also approach their adoption with caution. Whether you’re a business leader, a developer, or a user, here are three takeaways: ✅ Plan for edge cases: Anticipate and test for the unexpected, especially when scaling AI solutions. Test with your own benchmark, designed for your industry specific data and use cases. ✅ Invest in transparency: Understand how your models work and where they might fail. ✅ Prioritise responsible AI: Balance innovation with safeguards to ensure reliability and fairness. AI is incredibly powerful, but glitches like these remind us that we’re still navigating uncharted territory. What’s your take? How should companies approach the challenge of deploying LLMs responsibly while staying innovative? Let’s discuss! 👇 I'm particularly excited to share these insights as they complement the themes in my book "Grow Your Business with AI". https://bit.ly/4b31PEG Tomorrow's enterprises won't just use AI; they'll be orchestrated by Agentic AI. Own your agents, own your future. #AI #LLM #ChatGPT #ResponsibleAI #BusinessInnovation

Big news! ChatGPT is set to float on the Nasdaq;…… 1. This isnt happening anytime soon but watch the scramble when it’s announced. 2. Copilot and Gemini can already be invested via Microsoft and Alphabet. Ignore AI at your peril. AI is not just a technological advancement; it's a transformative force reshaping industries and redefining the future of work. By leveraging AI, businesses can unlock unprecedented efficiencies, uncover deep insights from vast amounts of data, and create personalised experiences at scale. Whether it's through predictive analytics, intelligent automation, or enhanced decision-making, AI empowers organisations to innovate and compete in ways previously unimaginable. Embracing AI is no longer optional—it's a strategic imperative for those who aim to lead in the digital age. Let's drive this change together and harness the power of AI to build a smarter, more connected world. #Innovation #FutureOfWork #DigitalTransformation #Sustainability, #Leadership, #AI #ExpatLifeInvest #ExpatCommunity https://lnkd.in/dEThDmAM

Great insights into Gen AI adoption and trends, especially this: “ 1/ Generative AI has found screaming product-market fit in its first few breakout use cases: 🥇 Code copilots (51% adoption) - e.g., All Hands AI, Codeium, Harness 🥈 Support chatbots (31%) - e.g., Aisera, Decagon, Sierra 🥉 Enterprise search (28%) - e.g., Glean, Sana 2/ The foundation model landscape is shifting: …Anthropic doubled its enterprise share from 12% to 24% while OpenAI slipped from 50% to 34%. Closed-source models remained dominant vs open-source models (e.g., Llama) with 81% market share. “ #genaitrends

Menlo Ventures ‘ analysis of generative AI trends is a fantastic read, capturing the pulse of this rapidly evolving space. As a builder in the AI domain, I want to add a few observations from my experience, which align with and build upon these trends: 1. Data Readiness as a Core Pillar: Data readiness is a recurring theme across enterprises. Platforms like unstructured.io and Vectara are leading efforts to prepare unstructured and structured data for seamless AI integration. These tools are addressing foundational challenges, such as real-time data preparation and cataloging, which are critical for large-scale AI deployments. 2. Connector Strategies for Data Integration: The success of startups like UnifyApps and Glean which are implementing 100+ connector strategies, highlights the growing need for scalable data integration solutions. These connectors allow enterprises to bridge silos, enabling seamless access to and utilization of data from diverse systems. 3. Agentic Automation Changing the Game: The rise of agentic AI is transforming how enterprises approach automation. Players like Agentforce, Zapier automation and Taskade are enabling departments to automate workflows independently, creating a paradigm shift in RPA and IPA landscapes. Established companies like UiPath are pivoting hard to remain competitive in this new ecosystem. 4. Vertical AI and the Challenge of AI Fatigue: Departments across enterprises are rapidly adopting vertical AI solutions, tailored to their unique needs, but this has also led to "AI fatigue." For example, sales teams are overwhelmed by multiple copilots and AI tools. There’s a growing demand for platforms that address these challenges holistically, such as Salesforce At the same time, startups focusing on specific pain points for individual personas have a real opportunity to stand out and drive adoption. 5. User Loyalty and Startup Engagement: Enterprise users often show strong loyalty to the first solution that meets their needs, even resisting internal innovations or alternative platforms. Startups that position themselves as trusted partners and involve enterprise personas as design partners can create lasting relationships. However, timing is critical—startups that delay or attempt to solve broadly for an entire department risk missing the adoption window altogether. Key Takeaway: The importance of focus, speed, and user-centricity is key. Startups need to align closely with enterprise pain points, iterate rapidly, and engage key stakeholders early to win in this competitive market. cc Derek Xiao Tim Tully Joff Redfern #GenAI #VentureInvesting #MenloVentures

Thanks for the interesting insight Derek Xiao Financial services is a top 4 industry for AI adoption. And spend on Vertical AI is the fast growing space. Covecta is here to help you participate.