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It’s only been ten months since ChatGPT launched, and since then, we’ve seen a huge increase in AI applications being created and used globally. OpenAI’s Head of Sales, Aliisa Rosenthal, joined OpenAI a year and a half ago — before ChatGPT launched. After the launch of ChatGPT, they went from 30 leads a week to 10,000.
For each of the 14 weeks, more people searched for ChatGPT than Taylor Swift according to Google Trends data. But Minnesotans & Idahoans & Vermontans are searching for ChatGPT. People are curious about AI. They want to test it, prod it, break it, be surprised by it. Platform shifts arouse curiosity.
I’m fortune that SaaStr Fund was the first investor in RevenueCat in 2018 and today it’s grown into the #1 tool to manage mobile subscriptions, powering 40% of all mobile subscription apps, and even ChatGPT’s mobile app itself. So they see 40% of all mobile subscriptions — and a ton of data from it.
and try it here : I did a deep dive live on Workshop Wednesday with my Top 10 Learnings, and brought in Simon Farshid, founder of Assistant UI , which offers the leading platform to add ChatGPT-like interfaces to your own app and to add some color. Importantly, it was vastly better than what I would have gotten from a generic ChatGPT prompt.
Speaker: Shreya Rajpal, Co-Founder and CEO at Guardrails AI & Travis Addair, Co-Founder and CTO at Predibase
Large Language Models (LLMs) such as ChatGPT offer unprecedented potential for complex enterprise applications. However, productionizing LLMs comes with a unique set of challenges such as model brittleness, total cost of ownership, data governance and privacy, and the need for consistent, accurate outputs.
We wanted to kick it off with some of the key insights with Box CEO Aaron Levie and IBM VP AI Raj Datta: The Numbers Don’t Lie – ChatGPT Hit 500M Users in Just ~2 Years Let’s start with the cold, hard metrics that SaaS leaders seem to underestimate: AI adoption is happening at an unprecedented velocity.
What Is AI Infrastructure Infrastructure is everything you might think of behind a ChatGPT-like experience. It starts with silicon chips, GPU, and data centers. After that, you have infrastructure around the models, helping you pick the right models or managing the data to be fed into the models.
In the past, the core engineering team & the data science/machine learning teams worked separately. For most companies, the data science team analyzed data & wrote machine learning models to support the business functions : sales, marketing, customer support. These teams operated downstream of the data warehouse.
Rather than committing to single solutions, successful companies are: Testing multiple AI models continuously Using self-served platforms for rapid experimentation Empowering engineers to make model-selection decisions Avoiding long-term vendor lock-in Data Management Data quality and integration have emerged as critical factors in successful AI implementation. (..)
Joselyn Goldfein , Managing Director at Zeta Venture Partners, which invests in AI and data infrastructure-focused startups from inception through seed stage And see everyone at 2025 SaaStr Annual, May 13-15 in SF Bay!! ” Weavi Founded in 2020, they anticipated the growing importance of unstructured data and embeddings.
OpenAI, the Microsoft-backed firm behind the groundbreaking ChatGPT generative AI system, announced this week that it would allow users to turn off the chat history feature for its flagship chatbot, in what’s being seen as a partial answer to critics concerned about the security of data provided to ChatGPT.
At SaaStr Annual , he was joined by Jordan Tigani, Founder and CEO of Mother Duck Maggie Hott, GTM at OpenAI , and Sharon Zhou, Co-Founder and CEO of Lamini to discuss the new architecture for building Software-as-a-Service applications with data and machine learning at their core. He was just using ChatGPT to generate Python code.
It’s not a SQL statement that would work today in a cloud data warehouse. Sure enough, ChatGPT answers the question : This pseudocode blends the structured queries of data analysis with the unstructured data contained in a classic novel. Benn views the future of BI as the narrative, the story behind the data.
It’s so funny how many people are concerned about ChatGPT and Bard. But here is the thing, when you currently perform a search on Bing, Google, or even ask ChatGPT a question neither of them is always correct, which I will get to in a bit. Here’s what ChatGPT spun up. When Google announced Bard, their stock fell 7%.
The European Data Protection Board (EDPB) plans to launch a dedicated task force to investigate ChatGPT after a number of European privacy watchdogs raised concerns about whether the technology is compliant with the EU's General Data Protection Regulation (GDPR). To read this article in full, please click here
Brainstorming content ideas, scheduling posts, replying to comments, analyzing your social media marketing performance, tweaking your marketing strategy based on data — and repeating it all over again day in and day out for multiple social media platforms. You’re spinning multiple plates at once as a social media marketer.
I use ChatGPT , Slack , Gong , 1SE , Midjourney , and Webflow , of course. #2. This last year ChatGPT has been my best new app. #3. Additionally, she’s supported private and public companies through profitability and hyper growth — managing engineering, product, data, and design teams at LiveRamp, Euclid, and Talkdesk.
OpenAI, the Microsoft-backed company that developed the ChatGPT generative AI chatbot, is the defendant in a federal class action lawsuit filed this week in California, where it is accused of misappropriating personal information for training purposes.
From analyzing market trends to churning user needs and technical feasibility into golden product ideas, there are many benefits of ChatGPT for product managers. A potent tool, ChatGPT has proven to be a strategic addition to the product management toolkit, churning out ideas in even the most unlikely scenarios.
I asked ChatGPT about the numbers 1 & 4. If you’re integrating GenAI into your product & interested to hear others’ plans, please fill it out, & we’ll send you the anonymized raw data. Which one is bigger? Sometimes, 1 was bigger. Othertimes, 4 was bigger. The answers have inconsistent logic.
Not just ChatGPT for day-to-day stuff, but apps like Copy.ai Other than your own, what app would you never let anyone take away from you no matter what: Looking through previous answers, everyone correctly points to their data / analytics systems as being irreplaceable. How could it not be AI related?
When ChatGPT finds formatting foibles in the data, it fixes them. It’s unclear how to replicate or share the analysis if I needed to because the session doesn’t store the data file & it’s not linked to Github. Here’s the video of my session with ChatGPT. My columns contained extra spaces.
Generative AI, and ChatGPT in particular have been catnip to the tech press, the mainstream media, and the conversations of professionals in nearly every field. How is it going to disrupt your work?! Will AI replace you? Are Hollywood writers, real estate agents, dog walkers, and anesthesiologists even useful anymore?
18:56) Using product data for marketing purposes. (21:17) Attention’s generalized insights allow you to ask any question across all of your conversations – think of it like ChatGPT for your sales calls. 9:10) Will the vast majority of software companies have a PLG motion in the future? (18:56) But that’s just the beginning.
And AI (particularly ChatGPT) has supercharged my productivity and learning — and helped me get back some of my oh-so-precious time. I’ll walk you through how AI can make your job as an AE way easier — including specific ChatGPT prompts you can put to use in minutes. Personally, I use ChatGPT for a ton of things. Don’t worry.
For dashboarding and analytics a lot of our data makes its way into Looker , which is simple to use but also offers a lot of depth. Figma is where we collaborate on design, and – increasingly – ChatGPT helps out across both design and written work. for our podcast.
I’ve been using large-language models (LLMs) most days for the past few months for three major use cases : data analysis, writing code, & web search 1. Third, LLMs could replace search engines if their indexes contain more recent or evergreen data for summarization searches but not for exhaustive ones.
This is where G2 differentiates itself from Gartner — with a data-driven platform vs. an analyst. G2’s OpenAI-Powered Monty Changes The Game G2’s Monty, powered by ChatGPT, will be the best way to discover longtail apps for software buyers. Why ChatGPT and not some other language model? All of their data is in Snowflake.
Subscribe now Data + Compute = AI Two of the largest data companies in the world, Snowflake and Databricks, held their annual conferences this week. Foundation models and their outputs are only as good as the data that feeds / trains them. To truly take advantage of AI, enterprises need organized data in the cloud.
Pulling in recent studies Anytime there’s a new industry market report, there are probably journalists writing about it, so if you can tie your press release to that data, you may increase your chances of getting coverage. Just double check they aren’t already being used by another company before clicking publish!)
Samsung has reportedly issued a memo prohibiting the use of generative AI systems like ChatGPT to prevent the upload of sensitive company data on external servers.
Reflecting warnings given earlier , Apple is now among the growing number of businesses banning employees from using OpenAI's ChatGPT and other similar cloud-based generative AI services in a bid to protect data confidentiality.
Below, I’ve listed those trends with data & excerpts from the earnings call transcript. We will soon add support for ChatGPT, enabling customers to use it in their own applications for the first time. I’m watching public company earnings to identify early weaknesses in the software market.
the system of engagement, and all of a sudden, it wasn’t just recording and storing data. There’s an evolution from being data-driven to a more sensing approach. We must ask ourselves the implications of such personal data being used and shared with employees, organizations, customers, and users. You move one over to 2.0,
Sure – the rise of ChatGPT has gone mainstream for consumers and smaller companies, but what about the big guys? And in the beginning, since AI is handling so much data – trust is fundamental. While the first generation of Generative AI is great, it’s not quite ready to solve Enterprise problems. Compliance matters.
If you take ChatGPT, an interface to a model, OpenAI needs to be the best at the thing. Data Will Be Your Advantage Data will give you a competitive advantage. This work mainly involves curating the data you train the base model on, and fine-tuning it to the task you’re trying to accomplish.
Automatically a new sales lead will go through the following steps before ever reaching a human person: it goes to Runway’s database and then through Zapier automation, then a ChatGPT prompt then filters out all free email accounts. This is the raw prompt they use for qualifying leads.
Ever since ChatGPT catapulted generative AI into public consciousness over a year ago, we’ve been introduced to thousands of new consumer products that incorporate the magic of AI — from video generators to workflow hacks , creativity tools to virtual companions. ChatGPT also leads here — but by a much tighter margin.
It’s been 9 months since ChatGPT was released and 7 months since it became the fastest consumer application to reach 100 million monthly active users , ushering in a new era of generative AI. But other than ChatGPT, how are consumers interacting with generative AI (GenAI) products? Who might be the next “big winner”?
ChatGPT & AI upended the ecosystem. The Paradox of AI and Data Roles: How Automation Will Increase Demand for Data Professionals. As data becomes critical to developing products, the need for data professionals only grows, even if AI automates rote data analysis & retrieval.
You’ve probably heard about ChatGPT’s AI chatbot and maybe you’ve even taken it for a spin, but have you discovered how incredibly useful it is for analyzing your organization’s data? Need to create a really quick table to visualize some data you were sent by a 3rd party, or want to find if there’s any insights in it?
Many people see ChatGPT as a way to streamline Customer Success processes like preparing for customer meetings or literally creating those processes upon which their organizations run. One approach some Customer Success pros are taking is to use ChatGPT to generate Customer Success playbooks. I am fully aware.
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