‘Aizuddin Zakaria Adli’s Post

I read this blog a few years back and it resonated quite strongly with me as a founder of a #SaaS company providing AI services to our customers. Thought it was worth highlighting and sharing it again in post-GPT world. AI companies often struggle with lower gross margins of 50-60%, contrasting sharply with traditional SaaS businesses reaching 60-80%+, due to hefty cloud costs and ongoing human support needs. This underscores the reality for #AI startups! Overcoming these hurdles could redefine economic strategies in tech, prompting new approaches that blend software efficiency with service-oriented flexibility to build resilient AI enterprises.

This chart explains how C-level of enterprise companies should think now about how to become a GenAI/AI-first company before a startup takes over your business.

Woah, that didn’t take long 🤯🚀. Glean, the enterprise AI startup, just raised $260M in Series E funding and hit a $4.6B valuation. Yep, that’s right—DOUBLED in less than 7 months. 💥 Founded in 2019 by former Google engineers, Glean has been growing like crazy with over 500+ employees, more than 4x growth in the past 18 months! 💪 TL;DR 💸 $550M in cash on hand 📈 Tripled annual recurring revenue in the last 12 months 👩🔧 Employee count skyrocketed from 125 to 500+ 💼 Serving big names like Reddit, Inc., Instacart, Pinterest, Databricks, Samsara 📊 40% daily active user rate—more than double the typical enterprise software (10-20%). 👀 Where did the funding come from? 💰 This round was co-led by ALTIMETER CAPITAL MANAGEMENT LLC and DST Global, with new investors like Craft Ventures, Sapphire Ventures, and SoftBank Vision Fund 2 joining the fray. Returning investors include Sequoia Capital, General Catalyst, ICONIQ Growth, Coatue Management, Kleiner Perkins, and Lightspeed Venture Partners. 🏦 Why? Because Glean’s AI is not just another search tool—it’s Google for Work with multi-step prompts, Zendesk and Salesforce integrations, and features that make ChatGPT look like it’s still in beta. 💻⚙️ As CEO Arvind Jain said: "Businesses are in the midst of an AI transformation… one as big or bigger than the internet." 🌍📡 Want to know how Glean's reshaping the AI market? Check out the full scoop here 🔗 https://lnkd.in/gxRcTP5P Oh, and by the way, the Enterprise AI market? It’s set to reach $143 billion by 2027. AI’s Second Act is going to be BIG. 🎭💡 #AI #EnterpriseAI #TechNews #Glean #GenerativeAI #Growth

In the Gen AI Product race between Startups and Big Tech, Big Tech will capture substantially more market share. Big Tech possesses access to an unparalleled distribution network and limitless capital that only a handful of startups can hope to rival. Leveraging these assets, Big Tech is launching comprehensive Gen AI agent suites that integrate seamlessly within their cloud ecosystem. For Big Tech, offering suites is natural as there are high economies of scale, and technology stacks are reusable between different agents. For a user, Suites triumph over a collection of standalone products (even if the standalone products are better in quality than their suite counterpart) from different vendors, as it reduces their touchpoints substantially. Take the newly launched Amazon Q for example. It is an enterprise Gen AI assistant rivaling several startups at once. 1️⃣ Amazon Q Business is a tailor made assistant for businesses that learns from their organization’s data, and assists in answering queries, generating new content, and deriving insights. This seems to rival Glean’s Glean Chat by connecting with the business’s data sources across multiple vendors like Amazon, Google, Microsoft, Slack, Salesforce, etc. 2️⃣ Amazon Q Developer is an advanced coding Copilot that is able to code, test, diagnose errors, and optimize cloud resources. This will rival Github Copilot as well as startups like Magic 3️⃣ Amazon Q Quicksight is a business intelligence assistant that is able to build dashboards, extract business insights, and create data stories highlighting key insights, drivers, and trends, all in natural language. This is a powerful tool that can dramatically improve the productivity of Business Intelligence Engineers, and reduce turnaround time for generating business reports. This aims to rival several startups in the space like Findly.ai. 4️⃣ Amazon Q Connect is a contact center Copilot that learns from historical data, and recommends customer support responses in real time. This rivals Microsoft’s Contact Center Copilot, as well as startups like Shelf.io 5️⃣ Amazon Q alongside AWS supply chain will help Supply Chain Managers get quick insights on the health of their supply chain. Similar to Amazon Q, Microsoft Copilot has several agents within the same Bot, and integrates seamlessly within the MS Ecosystem. Any startup will find it tough to match the value that these agents sitting on top of entire ecosystems provide to their users. Where startups hold a majority of their influence is in catering to a market that prefers standalone products over an entire ecosystem, as an ecosystem is too expensive if you don’t require a majority of the products. However, this market is less likely to have high paying customers like enterprises, and therein lies the greatest scaling challenge for Gen AI Product Startups.

Sequoia: AI apps beating corporate IT “pretty doable" Some key points from the VC’s latest: Brief: Sequoia, the prominent Silicon Valley venture capital firm, discussed in its new article 'Generative AI’s Act o1' how startups “beating corporate IT and global systems integrators” by focusing on GenAI at the application layer is "pretty doable". Breakdown: 1\ The cloud transition brought software-as-a-service. The AI transition is about building applications that automate and amplify knowledge work, 'service-as-software', a much larger market worth trillions. 2\ Competition in the foundation layer (infrastructure and models) is stabilizing, led by Microsoft/OpenAI, AWS/Anthropic, Meta, and Google/DeepMind, due to high capital requirements. 3\ GenAI is evolving from "thinking fast" (pre-trained responses) to "thinking slow" (OpenAI o1's reasoning at inference), unlocking the potential for disruptive AI/agentic "killer apps" in consumer and enterprise markets. 4\ Two years ago, many called application layer companies "just a wrapper on GPT". Today, those "wrappers" have become one of the best ways to build lasting value. 5\ What started as wrappers has evolved into "cognitive architectures": multiple models, routing mechanisms, vector/graph databases for RAG, compliance guardrails, and application logic that mirrors human reasoning in workflows. 6\ Emerging examples: Harvey AI lawyer, Glean AI work assistant, Factory AI software engineer, Abridge AI medical scribe, XBOW AI pentester, Sierra AI customer support agent. 7\ However, transforming GenAI capabilities into reliable end-to-end enterprise applications still requires significant business expertise and engineering effort. 8\ Established enterprises must evolve or risk being outpaced by new businesses that master the intricacies of knowledge work and manage to reach customers. 9\ Similar to the emergence of 20 $1 billion+ companies in cloud and mobile applications, Sequoia expects a comparable opportunity with AI. Why it’s important: There's vast potential for enterprise reinvention and disruption. Innovation at the application layer will play a key role in defining the next generation of industry leaders. The choice is clear: disrupt or be disrupted. P.S. Link to Sequoia’s full article in comments. --- ♻️ Repost this to help your network! 📌 Want to level up with GenAI? ➲ Get on the list: https://lnkd.in/eJ6QQeru

The future belongs to AI startups...

AI start-ups generate money faster than past tech companies🚀 According to Financial Times: “The AI start-ups in the cohort took a median 11 months to hit $1mn in annualised revenue after their first sales on Stripe, compared with 15 months for the previous generation of SaaS companies, the data showed.” “AI start-ups that have scaled to more than $30mn in annualised revenue achieved the milestone in 20 months — five times faster than past SaaS companies.” “Goldman Sachs this month has raised concerns about the profitability of AI businesses given ‘the AI winners of today are no longer capital-light businesses’, referencing the significant costs required for computing infrastructure to run and train AI models.” Interesting data. Would love to see the full sample of AI companies though.. Join 28k+ thought leaders from VCs like a16z, Accel, Index, Sequoia, and more to get free data-driven insights like this via https://lnkd.in/dV62qJ5y

Sequoia’s perspective on the rise of Generative AI applications highlights a turning point in the AI market. We’re moving from simply enhancing tasks to fully transforming how businesses operate. AI applications are becoming more sophisticated, integrating multiple models, advanced data processing, and compliance checks, which means there’s enormous potential for AI-driven disruption across industries. For AI research institutions and vendors this is a defining moment. As AI technology evolves, their role in guiding businesses - from small startups to large enterprises - is becoming more critical. AI research institutions and specialized vendors working together can provide the expertise and resources needed to ensure AI applications are not only cutting-edge but also secure, ethical, and scalable. The future of the AI market will see significant ecosystem growth with AI research institutions partnering with AI vendors, and big businesses to develop solutions that align with real-world needs, helping companies leverage AI responsibly. Whether it’s improving efficiency, developing new products, or ensuring compliance, these AI communities are essential in shaping the next generation of AI applications. The AI market is growing fast and those that foster ecosystems and collaborate with all key industry participants will lead the way. #ai #ecosystem #aicommunity #airesearch #institution #aivendors

How can AI startups win? As you build your business, you need to be thinking about how you can stake a defensible place in AI, something that is particularly challenging as the big players continually carve out huge chunks of business ideas. Seseri points out that in the cloud era, we had a foundation layer where the infrastructure players staked their claim; a middle layer where the platform players lived; and at the top we have the application layer where SaaS lived. With the cloud, a few players like Amazon, Microsoft and Google emerged to control infrastructure. The foundation layer in AI is where the large language models live, and a few players like OpenAI and Anthropic have emerged. While you could argue these are startups, they aren’t in the true sense because they are being financed by those same big players who dominate the infrastructure market. “If you’re going to compete for a new foundation layer, or you know, LLM play, it’s going to be very tough with multibillion dollar capital requirements, and at the end of the day, chances are it will end up being a commodity,” she said. At the top of the stack is the application layer where thousands of SaaS companies were able to take advantage of in the cloud era. She said that the big players like Amazon, Google and Microsoft were not able to take all of the application layer business and there was room for startups to develop and grow into large, successful businesses. There is also a middle layer where the plumbing gets done. She points to companies like Snowflake that have succeeded in building successful businesses in the middle layer by providing a place for the application players to put their data. So where is she investing when it comes to AI? “I put my dollars in the application layer and very selectively in the middle layer. Because I think there is a moat around algorithms, whether it’s algorithms that are proprietary to you, or open source — and data. You don’t need to own the data. But if I have to pick, I’d like to have unique data access and unique algorithms. If I am forced to pick one, I will go after data,” she said. Building an AI startup surely isn’t easy, perhaps even more challenging than a SaaS startup. But it’s where the future is, and companies that are going to try it have to know what they’re up against and build accordingly.

Google Cloud CEO: 60% of AI Startups Use Our Tech "...Google Cloud’s CEO says his company has several paths to monetizing artificial intelligence (AI). “We have seen 10-times growth year over year of people using our clusters,” Thomas Kurian said Tuesday (Sept. 10) at a Goldman Sachs tech conference. “Sixty percent of all AI startups use our clusters for training and inference,” added the executive, whose comments were reported by Seeking Alpha..." #AItech Google #startups #growth #training #inference PYMNTS _________________________________ If you want to immediately see new content that I curate 👉🏻 Ring my bell (🔔) and follow #informationbanker or Informationbanker and if you want to get the posts that got noticed in my weekly What’d I miss newsletter, subscribe here: "https://lnkd.in/d5_KvWwu"