The hottest Enterprise Substack posts right now

And their main takeaways
Category
Top Technology Topics

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.

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.

📌 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.

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.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

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.

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.

🏞️ 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.

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.

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.

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.

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.'

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.

Anthropic just made the agentic AI race interesting

Alex's Personal Blog • 0 implied HN points • 23 Oct 24
🕹 Technology AI Software Innovation Enterprise Performance
  1. Anthropic introduced a new AI feature that allows its model, Claude, to interact with computers like a human does. This means it can perform tasks by moving a cursor, clicking, and typing on its own.
  2. This technology could change how companies use AI, making it possible to automate many jobs, which might reduce the need for human workers in some areas.
  3. The introduction of this API means that more people can experiment with AI at home and in small businesses, which could lead to creative and practical uses for technology in daily life.

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.

What does it mean to be "Enterprise Ready"

Brick by Brick • 0 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.

The Curious Case of ChatGPT API Pricing

Sector 6 | The Newsletter of AIM • 0 implied HN points • 05 Mar 23
🕹 Technology AI Software APIs Development Enterprise
  1. ChatGPT's API has made it easier for businesses to use its features at a lower cost. This is helping many companies save money and improve their services.
  2. The reduction in costs associated with ChatGPT's API is likely to attract more users and increase its popularity. More people will want to try it out for their projects.
  3. Keeping some services free for a longer time can be beneficial for businesses, as it can lead to better long-term results. A company mentioned that their runway improved significantly after using ChatGPT's API.

When will the ‘Jio Moment in AI’ Arrive? 🙂

Sector 6 | The Newsletter of AIM • 0 implied HN points • 28 May 24
🕹 Technology AI Innovation Collaboration Enterprise Research
  1. Reliance Jio is working with NVIDIA to develop AI models in India, using advanced technology like GH200 GPUs. This partnership aims to enhance AI capabilities in the country.
  2. Despite this collaboration, there haven't been any major announcements about specific AI projects from Reliance Jio yet. People are eagerly waiting for updates.
  3. Reliance aims to transform industries with AI solutions, employing a team of 100 experts to work on these innovations. They believe this will create big changes in the enterprise sector.

GenAI the Oracle Way

Sector 6 | The Newsletter of AIM • 0 implied HN points • 23 Jan 24
🕹 Technology AI Cloud Data Enterprise Software
  1. Oracle has launched its own Generative AI service that aims to provide better data security for businesses. This means companies can explore AI without worrying as much about safety.
  2. The service is integrated across all levels of Oracle's technology, making it easier for users to access AI tools. This can help businesses adopt AI solutions more effectively.
  3. Oracle's introduction of new Large Language Models (LLMs) like Llama 2 shows its commitment to staying competitive in the cloud service market. This supports a growing demand for advanced AI capabilities.

Now or Never

Sector 6 | The Newsletter of AIM • 0 implied HN points • 09 Mar 23
🕹 Technology AI Software Enterprise Data Innovation
  1. Generative AI is changing how businesses operate, and major companies like Microsoft and Salesforce are competing to be the best at it.
  2. Companies that don't quickly adapt to using AI might fall behind in the market.
  3. Experts believe Microsoft may struggle to regain market share from Salesforce in the CRM area, especially with their partnership with OpenAI.

Struggle Continues: Is the Cloud Giant Losing Ground?

Sector 6 | The Newsletter of AIM • 0 implied HN points • 03 Jan 23
🕹 Technology Cloud Computing Software Data science AI Enterprise
  1. Salesforce is facing tough times with declining demand for its software. It's struggling to keep up with changes in the market.
  2. The company's leadership is under pressure, which raises questions about its future and stability.
  3. Investors are worried about Salesforce's valuation as it experiences a dip in performance compared to competitors.

What Is The Future Of Prompt Engineering?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 26 Sep 23
🕹 Technology AI NLP Chatbots Enterprise Language Models
  1. Prompt engineering might not be the main way we interact with AI in the future. It seems we'll use more natural and voice-based communication instead.
  2. Understanding context and reducing ambiguity are key challenges in human-like conversations with AI. This helps the AI to provide better answers.
  3. For businesses, fine-tuning models and using tools like context references help improve AI responses. Both methods work together to make AI better.