Ajai Karthikeyan

It has been a week since Amazon Web Services (AWS) announced our AWS Generative AI Accelerator and applications have been flying in 🚀🚀🚀 I can't share how many startups have applied, but it definitely has been the most active and exciting of our accelerator launches 🤩 What is awesome about this accelerator is that it's truly global, so startups from anywhere around the global can apply 🌐 As long as your startup is building a tech or tech-enabled product, has a tech lead, a working product, and is still early stage (not raised a Series A), then you are eligible for the program ✍️ Learn more about the program and apply using the link in the post below and remember to submit your application before 11:59 PM ET July 19 📌 #startups #AWS #GenerativeAI #accelerator

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Some additional charts on the aggregate hyperscaler data from Q1. The charts below show 1) The aggregate net new ARR added on q quarterly basis. Q1 is always a seasonal low 2) The YoY growth in net new ARR in Q1. Q1 '23 was the "bottom" with AWS actually adding negative net new ARR (ie they shrunk), so the YoY comp is the "easiest" for the aggregate bucket.

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JFrog reported its quarterly earnings on Aug 7th. ProntoNLP compared the JFrog earning call transcript to 3 other similar SAAS (DevOps) companies as well as to the average of the software GICS group. ProntoNLP's Aspect based LLM signal gave an early warning for JFrog on May 9th 2024 (their last earning call) when the Aspects score dropped from 0.52 to 0.4. ProntoNLP analyzed the earning call transcripts of these 4 SAAS (DevOps) companies JFrog, Atlassian, Dynatrace and Datadog. As can be seen in the image below the only company that had an increase in the LLM score compared with its score last quarter is Atlassian while JFrog had a much lower LLM score (-16%) compared with their last quarter (Color Legend is at the top part of the image below). The dotted navy blue line shows the average LLM score for each of the last 4 quarters for the whole software GICS group. All these 4 companies are SAAS companies and hence the customer related LLM categories are extremely important. The image below shows that Datadog and Atlassian have extremely high values for Customer Acquisition, Customer Behavior and Customer Retention. ProntoNLP's new LLM Aspects based signal for these companies shows the following trend: JFrog: a huge decline from 0.4 last quarter to 0.08 Atlassian: score stayed the same at 0.43 Dynatrace: slight decline from 0.45 last quarter to 0.42 Datadog : slight decline from 0.47 last quarter to 0.42 Based on the LLM scores in the Customer related categories and the change in the Aspects scores of the 4 software stocks it seems that Atlassian and Datadog are the top picks now and JFrog is at the bottom of the pack. The new comparison feature of ProntoNLP is extremely powerful and can support picking the winners and losers within any GICS group based on the latest #GenerativeAI technologies. ProntoNLP provides via SFTP real time quantitative signals on earning calls, filings, macro and news. Reach out to ProntoNLP to start using our #GenerativeAI based tools and Maintain an edge in alpha creation. #Quant #HedgeFunds #ArtificialIntelligence #StockMarket

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If you care about price this is the best analysis I’ve seen regarding the diff between using an emerging GPU cloud provider vs the big 3. The big 3 charge more per hour for the same H100 is simply because they can, they have the pricing power, and they offer technical experts and solutions architects who help customers build and architect their AI/ML solutions. Are their pricing power diminishes so will this pricing spread. If you agree or disagree, would love to hear your input!

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Key insight from Satya Nadella on the Bg2 Pod that's not getting enough attention: SaaS applications as we know them will collapse in the AI era. Why? The business logic layer is moving entirely to AI agents. The future architecture: • Agents will be multi-repo CRUD, not tied to single databases • Business logic shifts from apps to the AI tier • Backend systems become primarily data stores This isn't just a data transformation - it's a fundamental shift in how business logic and communication flows through enterprise systems. Think supply chains operating like real-time trading desks rather than quarterly review cycles. The architectural implications here are massive and under-appreciated. And to make these AI agents from different vendors collaborate with each other, we need agentic connectors - something we have been thinking about solving for a while. Worth a listen -->Agentic discussion starts around the 30 min mark: https://lnkd.in/gvCqyQau). The future is truly agentic.

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HashiCorp Reportedly Being Acquired By IBM [UPDATE]: According to the Wall Street Journal, a deal for IBM to acquire HashiCorp could materialize in the next few days. Shares of HashiCorp jumped almost 20% on the news. UPDATE 4/24/24: IBM has confirmed the deal valued at $6.4 billion. "IBM will pay $35 per share for HashiCorp, a 42.6% premium to Monday's closing price," reports Reuters. "The acquisition will be funded by cash on hand and will add to adjusted core profit within the first full year of closing, expected by the end of 2024." HashiCorp's shares continued to surge Tuesday on the news. CNBC reports: Developers use HashiCorp's software to set up and manage infrastructure in public clouds that companies such as Amazon and Microsoft operate. Organizations also pay HashiCorp for managing security credentials. Founded in 2012, HashiCorp went public on Nasdaq in 2021. The company generated a net loss of nearly $191 million on $583 million in revenue in the fiscal year ending Jan. 31, according to its annual report. In December, Mitchell Hashimoto, co-founder of HashiCorp, whose family name is reflected in the company name, announced that he was leaving. Revenue jumped almost 23% during that period, compared with 2% for IBM in 2023. IBM executives pointed to a difficult economic climate during a conference call with analysts in January. The hardware, software and consulting provider reports earnings on Wednesday. Cisco held $9 million in HashiCorp shares at the end of March, according to a regulatory filing. Cisco held early acquisition talks with HashiCorp, according to a 2019 report. Read more of this story at Slashdot.

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Fully Local LLMs with Web interface on your device: If you have free time to learn AND you have a Windows Desktop with an NVIDIA card, then my recommendation is to run Ollama and Open-WebUI in docker containers on a WSL Ubuntu24.04 Distribution. How do you do that?

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Open source has been an amazing distribution strategy and part of a GTM for many companies. But, over the last few years, open source software has been in the spotlight for businesses changing licenses (Hashicorp Terraform, Redis, Akka/Lightbend, etc.) and single maintainers coming under pressure from big corporations for bug fixes or getting subverted by state-backed actors (XZ Utils). It feels like the industry is fed up with open source and moving away from it. How did we end up in the current state? These are some of my thoughts after working to build a commercial open source company (Bytewax) for the past 3 years and working at successfully open source-supported businesses for the 6 years prior to that. > Bytewax continues to be open source :) you can get it for free here - https://lnkd.in/gE2Npn_9 ;) 1. Open source was a way to give science and technology to the people. To prevent one organization from monopolizing and extortinging the public or as a way to bring the collective knowledge of the masses to innovation. 2. Open source became a competitive tactic either by way of scorching the earth or commoditizing something to make your service more valuable. Think Meta's Lama Models, Google's Kubernetes, Heroku and Rails, GitHub and Git. 2. Open source become generally accepted as part of GTM to distribute software with hundreds (ok probably thousands?) of companies leveraging the advantages around distributing free, open source software with the idea of commercializing it by hosting it or providing support to the enterprise. 3. Open source is deflationary, it naturally drives down prices and puts pressure on businesses/maintainers to provide equivalent or more value than the market leader to get more adoption. 4. As a result of 3, Commercial businesses are forced to bundle more value in the OS offering making it difficult to create a sustainable business. You need a minimum of two products with different value propositions between your open source offering to the paid offering. Some examples of companies that have successfully navigated this in recent years - Vercel platform vs. Next.js, Databricks vs. Spark/Delta Lake/MLFlow. Due to the four factors above, we've reached a point where open source is increasingly unsustainable. That is why we continue to see maintainer burnout, companies switching licenses, and companies pivoting away from open source. What can we expect in the future? - More source available licenses with less permissivity - Fewer commercial companies starting with open source - New software distribution methods Open source software runs the world, from governments to the biggest corporations, and I believe it will continue to be a strategic imperative as a result. How do we make open source more sustainable?

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Fascinating discussion on revenue ops and gen AI over at the CloudFix AWS Made Easy studio with Rahul Subramaniam, ♾️ sev geraskin, and M.H. Lines of Stack Moxie! Look for the full episode coming soon!

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🌟PerfectScale is now on the Datadog Marketplace🌟 ⛔ 50% of Kubernetes resources are wasted on average. With cloud costs skyrocketing, this inefficiency is alarming. 👥 As a savvy engineering leader running complex Kubernetes environments, you know that optimizing resource utilization is critical, yet extremely challenging. 🔨 PerfectScale's integration with Datadog finally makes it possible. This powerful combination gives you unparalleled visibility into your clusters and intelligent optimization to eliminate resource waste automatically. 💡 The result? You maximize application performance and availability while minimizing costs - effortlessly. Control your Kubernetes spending and stop throwing money down the drain. Optimize intelligently with PerfectScale and Datadog today! Want more information on the feature? 📓Read more about the update: https://hubs.la/Q02BHxnH0 📚Check our Documentation Portal: https://hubs.la/Q02BHyDp0 🖇️Visit the Datadog Integration page: https://hubs.la/Q02BHwHP0 #kubernetes #devops #k8s #sre #datadog

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Attention CIOs and IT Directors: Are you running MongoDB on x86? Is scalability an issue? Consider LinuxONE for data center consolidation, better performance and scalability! Direct message me for more information today. •Better Performance: Get 1.8x more throughput per core on an IBM LinuxONE Emperor 4 LPAR versus a compared x86 platform. •Better Scalability: LinuxONE, with its scale-up architecture and the ability to allocate resources, allows you to scale up a single MongoDB instance to 17 TB in a single system without sharding. • •MongoDB Consolidation: Run 1344 concurrent databases executing a total of 377 billion transactions per day on a single IBM LinuxONE Emperor III server. #mongodb #linuxONE #security #scalability

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2024 - 2025 will see a lot of firesales in tech - startups and VC firms Imagine raising $1bn+, making over $100m in revenue a year, being valued as much as $8.3bn recently and then selling for.. $200m dollars My advice to the VC-backed founders I speak to is to be honest with self early enough and so consider all options - cut costs, sell, etc VCs themselves are in trouble and are in no position to help any but the top, top portfolio companies

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Cloud Giants Update: AWS (Amazon): $100B run rate growing 17% YoY (last Q grew 13%) Azure (Microsoft): ~$76B run rate (estimate) growing 31% YoY (last Q grew 28%) Google Cloud (includes GSuite): $38B run rate growing 28% YoY (last Q grew 26%, neither are cc)

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Cloud Giants Update: AWS (Amazon): $110B run rate growing 19% YoY (last Q grew 19%) Azure (Microsoft): ~$66B run rate (estimate) growing 34% YoY (last Q grew 33% cc) Google Cloud (includes GSuite): $45B run rate growing 35% YoY (last Q grew 29%, neither are cc)

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And who better to help Google Cloud clients’ bring #nextlevelintelligence to address their ongoing Digital Initiatives than HCLTech HCLTech Ecosystems HCLTech – Cloud enabled by Modern Cloud Platform Services through our Intelligent Automation Cloud powered by HCLSoftware. No other firm can address the three aspects of Modern Cloud by addressing cloud as a business model, op model, tech model as well as #cloudsmart approach by HCLTech. Think differently, think HCLTech CloudSMART

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How Startups Grow 👇 1. How Linktree got from 0 to $37M ARR and 12M users with the simplest product ever: https://lnkd.in/dET-g-Gd 2. How Rubrik's CEO hacked growth to achieve +$500M ARR in 10 years: https://lnkd.in/djKajRDj 3. How Perplexity AI got to +$1B valuation with $20M ARR under Google's Monopoly: https://lnkd.in/dzRXWYw3 4. $100M ARR in 18 months: how Wiz became the fastest-growing software company ever: https://lnkd.in/dyHk2Pda

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Enabling use cases like factory floor optimization, assisted workforce, and asset tracking can be expensive and time consuming. Building, deploying, and maintaining configuration can add to the complexity. In this webinar, you will learn from the experts at Google Cloud and ClearObject how to leverage the very latest in AI, modern infrastructure, and edge computing to enable modern manufacturing outcomes in your center today.

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Most software engineers will not be replaced by LLMs anytime soon. Writing code literally and figuratively is writing in a specific language (or dialect if you prefer). I view LLMs as translators between my plain English and my coding language/dialect. If you have no idea what you want to say or if your thoughts are not clear, no translator in the world can give you an elegant and insightful conversation, regardless of whether you speak your counterparty's language or not. However, if your thinking is clear, a translator can enable a very rich conversation, even if you don't speak their language at all. So it is with coding. Clear thinking comes from knowledge which is hard to come by and has a naturally high barrier to entry. Better software engineers have clearer thinking which will result in better code. Period. This will not change until AI has the ability to create its own knowledge via conjecture and criticism without any human guidance. (Edit/hint: none of the big AI companies/researchers seem close to that yet).

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🎉 2025 AI Reality Check: What Works in Enterprise AI 🎉 After leading several enterprise AI implementations last year, I've noticed something surprising: companies with the highest ROI aren't using the most advanced AI models. They're winning through a ruthless focus on fundamentals. Here's what works: 1️⃣ Focus on Measurable Outcomes AI adoption often stalls when the focus shifts from solving real problems to chasing the latest trends. The key? Anchor your initiatives in clear, business-driven goals that resonate with stakeholders. 💡 Example: I worked with a logistics company to address inventory challenges. By deploying predictive models trained on supply chain data, they were able to streamline operations and improve availability. 🔑 Takeaway: Start with the business problem, set clear goals, and let AI follow—not the other way around. 2️⃣ Build a Data-Driven Advantage Your AI model is only as good as the data it’s trained on. This year, focus on: Ensuring data quality: Invest in clean, structured, and labelled datasets. Eliminating bias: Build pipelines that account for diversity and inclusivity. 💡 Example: A retail client I worked with faced challenges in optimizing pricing due to skewed predictions. They achieved more accurate and impactful results by adjusting the training data to include broader demographic diversity. 🔑 Pro Tip: Data ethics and governance aren’t just checkboxes—they’re essential for trust and long-term success. 3️⃣ Empower People to Drive AI Success AI is not a replacement for human decision-making—it’s a force multiplier. The best outcomes come from human-machine collaboration. 💡 Example: For a financial services company, embedding domain experts into the model development process improved efficiency and adoption. Collaborative workflows ensured the AI aligned with real-world needs. 🔑 Takeaway: Ensure teams across business, data science, and operations collaborate early and often. AI adoption isn’t just about the tech—it’s about aligning people and processes. 🛠️ Emerging Trends to Watch in 2025 This year, AI innovation will be driven by: ✅ Specialized large language models (LLMs): Custom-tuned LLMs are unlocking hyper-specific use cases, from personalized healthcare to financial forecasting. ✅ Real-time applications: Advances in reinforcement learning and edge AI will enable faster, smarter decisions at scale. ✅ AI ethics and explainability: Transparency will move from a nice-to-have to a business necessity. #AI #EnterpriseAI #GenAI

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