The hottest Enterprise Substack posts right now

And their main takeaways
Category
Top Technology Topics

Did OpenAI Just Kill Glean?

Enterprise AI Trends • 253 implied HN points • 26 Jun 25
🕹 Technology AI Software Data Enterprise Innovation
  1. ChatGPT can now perform 'Deep Research' using private documents from sources like Google Drive and Dropbox. This makes creating reports much easier for users.
  2. The ability to generate reports is significant because a lot of middle managers spend a lot of time on this task. It's a huge time-saver.
  3. This new feature could impact other apps that provide similar research functions, like Glean, making it a competitive landscape for AI applications.

Evals Startups Are Not Enterprise Ready

Enterprise AI Trends • 253 implied HN points • 08 Jun 25
🕹 Technology AI Software SaaS Open Source Enterprise
  1. Evals platforms may not be enterprise ready yet, as they often just repackage simpler tasks rather than offering real value. It's better to explore free open-source options first.
  2. The market for evals is crowded and competitive, leading to similar features across different products. This makes it a buyer's market, with pricing being more important than unique capabilities.
  3. Using open-source tools can provide most features needed without locking you into one vendor. Building internal capabilities first can help avoid future complications when needing to switch providers.

Enterprise AI is Moving Fast, and Other Takeaways From a Week With Amazon

Big Technology • 4503 implied HN points • 09 Dec 24
🕹 Technology AI Enterprise Software Innovation Business
  1. Generative AI is mainly used in businesses right now because they face unique problems. Companies are investing in it to process information and improve operations.
  2. Spending on generative AI is mostly for tools like ChatGPT and APIs for building custom solutions. This growth in enterprise spending may help develop AI technologies for consumers later on.
  3. OpenAI and Amazon are becoming competitors in the AI space. Their focus and innovations can change how AI is used in both business and personal applications.

How Adaptive AI Microcontainers Outmaneuver Modern Cybersecurity Threats in AI Workloads

Phoenix Substack • 42 implied HN points • 06 Feb 25
🕹 Technology Cybersecurity AI Microservices Software Enterprise
  1. AI workloads are crucial for businesses but can attract cyber threats. These threats target predictable systems and can steal data or disrupt operations.
  2. Static security methods, like firewalls, are not enough to protect AI workloads. New challenges like lateral movement and data theft highlight the need for better security.
  3. Adaptive AI Microcontainers create secure environments by changing and healing themselves automatically. This makes it hard for hackers to predict or exploit the system.

Fullstory's Enterprise Transformation

Good Better Best • 4 implied HN points • 13 Jun 25
💼 Business SaaS Enterprise Pricing Product Strategy Market Trends
  1. When a company wants to sell to bigger businesses, it needs to change its products to meet different needs. Just scaling existing features isn't enough.
  2. Adding professional services can help customers get more value from a product. These extra services can make a big difference in how well customers use the product.
  3. A company's pricing strategy should fit how larger organizations buy. Sometimes that means moving from easy self-service pricing to more custom and guided deal structures.
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Microsoft sees OpenAI as an existential threat

Enterprise AI Trends • 337 implied HN points • 23 Feb 25
🕹 Technology AI Software Enterprise Innovation Business
  1. Microsoft feels threatened by OpenAI because OpenAI is becoming powerful in the enterprise AI space. They worry that OpenAI's success could hurt Microsoft's own products.
  2. The 'AGI clause' gives OpenAI a strong advantage. It allows them to keep any advanced models from Microsoft, which could limit Microsoft's ability to compete effectively.
  3. Microsoft is trying to slow down AI adoption to regain control. They believe that if companies are hesitant to adopt AI quickly, it gives them time to improve their own offerings.

Why, When, and How to add Sales to your Product-Led Growth motion.

Elena's Growth Scoop • 786 implied HN points • 11 Jan 24
💼 Business Sales Product-led growth Enterprise Competitors Growth Strategies
  1. Product-Led Growth (PLG) allows products to scale endlessly and adapt quickly to market changes.
  2. Adding sales to PLG is necessary for B2B companies to capture enterprise market and drive growth.
  3. When considering adding sales, focus on complex products, higher AOVs, and the need for sales assistance.

What would a $2,000-a-month ChatGPT look like?

Enterprise AI Trends • 168 implied HN points • 19 Feb 25
🕹 Technology AI Software Pricing Enterprise Markets
  1. The future of AI will see two main pricing categories: low-end for general users and high-end for specialized, enterprise-focused users. There's not much room in the middle.
  2. High-end AI products will need to be built on strong industry knowledge and proprietary data to be successful. This means startups might struggle to compete.
  3. AI companies can charge a lot because their products provide immense value in competitive fields, where even a small advantage can lead to big profits.

OpenAI's Enterprise AI Strategy in a Nutshell

Enterprise AI Trends • 189 implied HN points • 10 Feb 25
🕹 Technology AI Enterprise Software Innovation Sales
  1. OpenAI is shifting its focus to a stronger enterprise strategy, moving beyond just APIs and consumer-focused ChatGPT plans.
  2. They plan to develop and deliver custom AI models specifically for businesses, separate from what regular users get.
  3. OpenAI wants to launch AI agents for companies, hinting at a significant change in how they compete in the market.

Former Facebook Research Scientists Raise $20 Million Seed Round for Enterprise Foundation Model Startup

Newcomer • 982 implied HN points • 07 Jun 23
💼 Business Technology Startups Venture Capital Competition Enterprise
  1. Former Facebook research scientists raise $20 million for a foundation model startup called Contextual AI
  2. Contextual AI's foundation model for enterprises aims to address existing model challenges like hallucination and data privacy
  3. Competition in the foundation model space is intense, with companies like Cohere and Vectara already in the game

The Rise of Indian Llamas

Sector 6 | The Newsletter of AIM • 399 implied HN points • 25 Dec 23
🕹 Technology AI Language Models Open Source Innovation Enterprise
  1. Llama 2 is a popular open-source language model with many downloads worldwide. In India, people are using it to create models that work well for local languages.
  2. A new Hindi language model called OpenHathi has been released, which is based on Llama 2. It offers good performance for Hindi, similar to well-known models like GPT-3.5.
  3. There is a growing interest in using these language models for business in India, indicating that the trend of 'Local Llamas' is just starting to take off.

The Rise of the Joint-Mission Startup

Pratap’s Substack • 277 implied HN points • 22 Feb 24
💼 Business Startups AI Enterprise Innovation Consulting
  1. AI can do much more than just make companies more efficient. It can actually change how we work and team up with machines.
  2. Working together as partners is key for big companies when using AI, not just buying software. They need deep collaboration to succeed in a new AI world.
  3. Startups have a big chance to tackle larger problems by creating complete solutions instead of just quick fixes. This approach can reshape how businesses operate.

The bull case for AI startups

The AI Frontier • 59 implied HN points • 13 Jun 24
🕹 Technology AI Startups Innovation Product Development Market Trends Enterprise
  1. AI startups have a lot of room for innovation, even with big companies investing heavily in AI. There are still many opportunities for new ideas and products.
  2. Startups can take more risks and try out unusual ideas that bigger companies might avoid due to reputation concerns. This freedom can lead to exciting new products.
  3. While big companies have access to a lot of data and resources, startups can be more flexible and connect data from various sources. This can give them an advantage in creating better solutions for customers.

Enterprise Generative AI Unfolded

Gradient Flow • 439 implied HN points • 27 Jul 23
🕹 Technology AI Development Tools Enterprise Applications
  1. Mastering Model Development & Optimization is crucial for building efficient and powerful Generative AI and Large Language Models. Scaling to large datasets, applying model compression strategies, and efficient model training are key aspects.
  2. Customizability & Fine-tuning are essential to adapt pre-existing LLMs to specific business needs. Techniques like fine-tuning and in-context learning help tailor LLMs for unique use cases, such as adjusting speech synthesis models for customized experiences.
  3. Investing in Operational Tooling & Infrastructure, including robust model hosting, orchestration, and maintenance tools, is vital for efficient and real-time deployment of AI systems in enterprises. Tools for logging, tracking, and enhancing LLM outputs ensure quality control and ongoing improvements.

When software stops caring

Sunday Letters • 99 implied HN points • 21 Apr 24
🕹 Technology Software User Experience Enterprise Consumer Productivity
  1. Enterprise software focuses more on the buyer than the user, making user experience less important. It just needs to be usable enough to avoid complaints.
  2. Consumer software prioritizes a great user experience because users can easily switch. This keeps companies on their toes to fix bugs and improve features quickly.
  3. Emerging apps from big tech are stuck in the middle. They need basic functionality but often don’t get the attention they need, leading to worse user experiences over time.

📌 You're invited to GenAI Productionize 2024

TheSequence • 371 implied HN points • 01 Mar 24
🕹 Technology AI Enterprise Generative
  1. GenAI Productionize 2024 is an industry-first summit focused on productionizing enterprise generative AI.
  2. Participants will learn from leading companies like LinkedIn, Google, and more on how they get their GenAI apps into production.
  3. The event will cover practical strategies for governance, evaluation, and monitoring of enterprise GenAI applications.

Consumer and Enterprise in the AI era

next big thing • 67 implied HN points • 20 Nov 24
🕹 Technology AI Consumer Enterprise Investing Startups
  1. Companies today are often serving both consumers and enterprises, breaking old boundaries. This means the most exciting businesses aren't just one or the other; they cater to both.
  2. The rise of AI, especially with tools like ChatGPT, is happening faster than any tech before. Many AI companies are seeing rapid user growth from both casual consumers and big businesses.
  3. For entrepreneurs, it's important to choose the right focus but also to have a vision that spans both markets. If your product gains popularity with both types of users, it could lead to great success.

🏞️ Knowledge Capital unleashed: Maximizing Enterprise Potential with AI Copilots

Work3 - The Future of Work • 157 implied HN points • 02 Aug 23
🕹 Technology AI Enterprise Data processing Digital Tools Privacy Concerns
  1. Enterprise Copilots are becoming a norm with AI assistants being built by various players to maximize company potential.
  2. Information is vital in organizations and tools like AI assistants can help capture, organize, and use it effectively.
  3. The evolution of Enterprise AI Assistants is expected to progress from basic tasks to executing actions, and companies like Microsoft are leading the way in developing these tools.

What does it mean to be "Enterprise Ready"

Brick by Brick • 18 implied HN points • 18 Nov 24
💼 Business Startups Enterprise Sales Product Development Compliance
  1. Startups need to adapt their processes to meet strict enterprise requirements, especially in compliance and security. This means being ready for audits and ensuring data protection.
  2. Creating a product that fits into the enterprise ecosystem is key. This includes having integrations, customization options, and strong reporting tools that enterprises expect.
  3. When selling to enterprises, startups must change their approach, focusing on value rather than just features. They should also prepare for complex pricing and long contracting processes.

OpenAI Just Killed Thousands of Startups

Sector 6 | The Newsletter of AIM • 39 implied HN points • 29 Aug 23
🕹 Technology AI Startups Enterprise Data Business Model
  1. OpenAI has created a new version of ChatGPT that only certain businesses can use, which means many startups that relied on this technology are now struggling.
  2. Startups that sold products based on OpenAI's original technology are in danger as they no longer have a competitive edge.
  3. These companies need to find new ways to stand out or they risk failing in the market.

Back from the Brink

Sector 6 | The Newsletter of AIM • 39 implied HN points • 30 Aug 23
🕹 Technology AI Enterprise Security Revenue Innovation
  1. OpenAI was struggling financially but is now expecting to earn about $1 billion in the next year. This is a huge increase from the earlier projection of $200 million.
  2. The company's new product, ChatGPT Enterprise, is designed for businesses and offers better security, faster access, and more customization options.
  3. These changes are helping OpenAI feel more confident about its future revenue and success in the AI market.

Clouded Judgement 1.3.25 - Domain Specific Models

Clouded Judgement • 7 implied HN points • 03 Jan 25
🕹 Technology AI Software Data Industry Enterprise
  1. In 2025, we will see a lot of special AI models that focus on specific areas of knowledge, like health or engineering. These models will learn from specialized and private data to perform better than general AI models.
  2. These domain-specific models will help industries that need deep understanding and accuracy, solving complex problems that generalized AI can struggle with. This means they can deliver the right answers when it matters most.
  3. As businesses create their own tailored AI models, the enterprise AI market will grow significantly. This will change how companies operate and improve efficiency in many fields.

Market Map and Analysis: Gen AI Business Productivity Companies

The Strategy Deck • 19 implied HN points • 27 Jun 23
🕹 Technology AI Enterprise Machine Learning
  1. Generative AI is transforming enterprise productivity by automating tasks and workflows.
  2. Key segments in this field include AI Meeting Assistants, Business Knowledge Base Platforms, and Application Building Tools.
  3. Companies are developing tools like AI assistants for meetings, knowledge base platforms, and app building tools to enhance business productivity.

Clouded Judgement 10.18.24

Clouded Judgement • 7 implied HN points • 18 Oct 24
🕹 Technology Software AI Enterprise Data Cloud
  1. Enterprise software has always relied on systems that store data, but the real value comes from how people use that data in workflows. It's not just about the data, but how it's managed and processed.
  2. AI is set to change this by taking over the data entry tasks that humans typically do. This means less focus on user interfaces and more on how efficiently AI can handle and process data automatically.
  3. With this shift to AI-driven systems, we will see new ways of building applications that prioritize smart databases. This could make traditional systems less important and create a need for new tools to manage complex workflows.

How do you compete with the everything company?

Entry Level Investing • 16 implied HN points • 09 Nov 23
🕹 Technology AI Startups Data Cloud Computing Enterprise
  1. OpenAI is a strong competitor in AI, but they are not infallible across all business lines.
  2. To compete with OpenAI, focus on deep vertical specialization, proprietary data moats, and cloud agnostic solutions.
  3. Consider prioritizing enterprise needs, leveraging open source communities, and recognizing the challenges of being an 'everything company.'

Databricks’ AI Strategy in a Nutshell

Enterprise AI Trends • 4 HN points • 25 Jun 24
🕹 Technology AI Data Governance Enterprise Cloud
  1. Databricks is growing in enterprise AI by focusing on data and AI governance with its Unity Catalog. This tool helps businesses manage how they use and share data and AI apps.
  2. Data governance is a big challenge for companies using AI. Without proper management, there can be serious security issues, especially with sensitive customer data.
  3. Unity Catalog makes it easier for Databricks to sell other services. Once companies start using it, they find it helps with many areas, leading to more business opportunities for Databricks.

Mobile DevOps for Enterprise that Just Works

Jacob’s Tech Tavern • 3 HN points • 15 Jan 24
🕹 Technology Development DevOps Enterprise Automation Mobile
  1. Mobile DevOps for Enterprise can be challenging due to the unique requirements and constraints of mobile development.
  2. Appcircle offers a more streamlined and user-friendly approach to setting up CI/CD pipelines, especially for mobile projects.
  3. Appcircle provides advantages such as simplified infrastructure management, faster build speeds, comprehensive permissions management, and features like tester management and enterprise app store.

Product security: barking up the wrong tree

lcamtuf’s thing • 2 HN points • 13 Mar 24
🕹 Technology Security Software Enterprise Coding Vulnerability
  1. The focus on product security often overshadows the more critical aspect of enterprise security.
  2. Enterprise security faces challenges like employee actions that can bypass security measures, demonstrating the need for a paradigm shift.
  3. Successful security programs accept the inevitability of compromise and prioritize detection, response, and containment over aiming for perfect defenses.

A Transatlantic Perspective on the Cybersecurity Industry

Cody's Version • 0 implied HN points • 14 Mar 23
🕹 Technology Cybersecurity Intelligence Enterprise IT Networks
  1. Key differences exist between the operational and strategic approaches to business in the US and Europe in terms of cybersecurity industry.
  2. European cybersecurity leaders often focus on tactical issues, while US counterparts tend to integrate intelligence and security operations into top-level strategic business operations.
  3. European cybersecurity vendors prioritize technical solutions, while US companies offer consolidated platforms with strategic intelligence and rapid-release products.