71: Find the top AI marketing tools and filter out the noise

What’s up everyone,

If you haven’t checked out our previous 3 episodes in our AI series you might want to before this episode, we give you a lot of context around some of the events that have happened and will shape the conversation today.

So basically:

  1. How fast could AI change or replace marketing jobs?
  2. How marketers can stay informed and become AI fluent
  3. Exploring new paths to future-proof your marketing career in the age of AI

Today we’re diving into specific tools… there’s a lot of noise out there right now.

  1. What tools you should play around with

In TMW #107 | ChatGPT and the artificial marketer, Juan Mendoza explains that

“…generative AI tools are already everywhere. From text generation to video and audio production, to image creation, there’s a thriving industry of technologies taking small slices out of our creative talents, packaging them up, and selling them as a SaaS product on a recurring revenue model. If you’re wanting to stay relevant five years from now in the marketing technology industry, you’re probably going to have to learn some of these platforms. In 2010 we used to say: “there’s an app for that”. In 2023, we will be saying: “there’s an AI for that.””

Juan Mendoza, TMW #107


Here are some of the topics for this third AI episode:

Here’s today’s main takeaway:

The key to future proofing your marketing career with the ever changing AI landscape is to stay curious, get your hands dirty and experiment fearlessly: Fill out some forms, spin up free trials, get on wait lists, and give new AI tools a chance. It’s only by actually getting your hands dirty that you’ll discover which tools truly work for you and which are just part of the ever growing sea of gimmicky AI tools.

Definition of tech terms

I’ll be using some of these terms throughout my analysis of some of these tools so here’s a primer explaining the three most common AI technologies used for marketing applications: 


Machine Learning: ML is a way to teach computers to learn by themselves, without having to be programmed for every task. They learn from examples and data patterns to make predictions or decisions. Applications include segmentation, predictive analytics and propensity models. 


Natural Language Processing: NLP is a subset of ML and focuses on enabling computers to understand, interpret, and generate human language. Includes sentiment analysis, machine translation, named entity recognition, text summarization, and more. NLP techniques usually helps computers understand and communicate with humans using everyday language. 


Graph Neural Network: GNN also a subset of ML is a type of neural network that aims to handle graph-structured data, data organized like a network or web of connected points. Applications include analyzing relationships between different things like users in a social network or users in your database or recommending additional products based on past purchase history. 

Real AI vs noise

Part of the reason AI gets a really bad rep, especially in martech, is that anything that’s built on if statements or simple Javascript logic gets called AI. There’s still plenty of AI startups that shout about their proprietary AI when it’s probably just a few decision trees and a few interns running spreadsheets.

Now though, you have an even bigger bucket of noise that’s essentially “slight tweak on Chat-GPT”. 

Developing AI that was comparable to human performance was a challenging feat prior to GPT’s arrival. To achieve this level of sophistication, a company would have had to:

  • make a substantial investment, amounting to millions of dollars
  • developing its own algorithms
  • performing extensive data cleanup

But it’s so easy now because GPT is so good out of the box. 

Allen Cheng puts it simply. Starting a new AI venture can be achieved by simply assembling a few elements: 

  • a product developed on GPT-4’s user-friendly API
  • a website, 
  • and a marketing campaign. 

This is why we’re seeing hundreds of AI tolls pop up every week.

A lot of these GPT-based products are pretty much indistinguishable from one another. Maybe a handful  have a significant advantage over others but most are gimmicky. And over the next few months, every tool is going to be integrating ChatGPT features inside their products in the hopes of making it stickier.

The threat of GPT-n

The part that I find trickiest and the most discouraging about building anything on top of GPT is that any progress you make on fine tuning GPT-4 will totally be wiped out by GPT-5 or GPT-n… Kind of like we talked about in a previous episode with all the tools GPT’s plugins killed. 

So let’s cut through the noise and dive into legit AI tools, the ones you should be playing with and experimenting. 

Content marketing tools

Copy.ai and Jasper



AI text generators are very common these days, the two most popular tools, especially for marketers are Copy.ai and Jasper. Both allow you to bypass the initial stage of writing where you face a blank page. 

The promise of these tools is that they help you in generating ideas, saving time on brainstorming and drafting, and ensuring a consistent production flow, freeing you to focus on higher-level strategic tasks, original research, and connecting with your audience.

I’ve played around with both Jasper and Copy.ai before ChatGPT came out… and they were super unique. But both Copy.ai and Jasper are built on top of GPT, they essentially rent usage of the platform. So they built a pretty nice UI on top of GPT… but now that ChatGPT came out, I’m sure they’ve seen a drop in usage. Plus GPT-4 is 3 times more expensive.

They still offer marketing specific value though and can get you up to speed faster than using CGPT in the form of templates, prompts and workflows. Both are super powerful, you could make a case that Jasper outshines its counterpart a bit, especially on the longer content format but it’s also way more expensive. 

Miguel Rebelo from Zapier has a solid breakdown comparison here https://zapier.com/blog/jasper-vs-copy-ai/ 



Grammarly, the popular spelling and grammar checker which has been using AI for over a decade already, also entered the generative AI niche last month unveiling GrammarlyGO. You guessed it, built on GPT. 

It has a particular advantage because Grammarly is already widely used and this is just an extra feature so to speak. Instead of just checking your grammar it can now also help you with drafting documents, composing and responding to emails, editing writing for tone, clarity, and length, and brainstorming ideas or outlines for projects within the apps you’re already working in. 

Lots of tools are going the Grammarly route in adding GPT features to their product, like Notion and more recently Superhuman.

Other AI writing tools

Some of these specialize in SEO, some in long form content, some in short form… they all do similar things:

Copysmith https://copysmith.ai/ 

Anyword https://anyword.com/ 

Writesonic https://writesonic.com/

Copymatic https://copymatic.ai/ 

Yaara https://www.yaara.ai/ 

Rytr https://rytr.me/ 

Frase https://frase.io/ 


Email is just a channel of potential generative content tools so it’s not totally distinguishable from the tools we chatted about in the content category. 

Chances are that the Marketing Automation platform or the customer engagement platform you’re already using as a suite of features they are throwing AI next to. Most of these though are just ML. 

  • Some tools like Iterable and Braze have propensity models to find users that are likely to perform a purchase event, that’s ML, and it’s only based on your data set. 
  • Some tools like Seventh Sense throw AI in front of Send Time Optimization features, these have been around for a long time and are only based on your recipients. This is also ML
  • Some tools throw AI in front of Sentiment Analysis features allowing you to analyze and classify the emotional tone of text into useful data. This is a subset of NLP that uses ML.
  • Some tools like 6sense throw AI in front of conversational email assistants that are supposed to be a step up from static drip emails sequences. This is also a form of NLP and generative AI.

You’re likely to start seeing copy assistants and additional AI features powered on GPT inside of these tools. 

I wanted to chat about one product that I feel like stands out from others in terms of being built around AI rather than it simply being a feature on the side.



One example I’ve seen (but haven’t used myself) of a CEP using more advanced AI is a tool called Persado. The two co-founders are former founders of Upstream in the mobile marketing space. Similar to 6sense’s conversational email platform, they’ve been around for a decade and they claim to leverage NLP + ML to create, optimize, and personalize engagement messaging. So they essentially analyze a bunch of data and identify the most effective emotions, CTAs, language, phrases to drive engagement. 

It’s worth calling it out because it’s more than just predicting user behavior and optimizing the best time to send a message, it takes it a step further by also incorporating NLP techniques, understanding the nuances of human language, and generating custom marketing messages that resonate with a unique user. 

One thing that makes Persado unique is it’s not built on GPT, it has its own model that’s trained on more than 100 billion digital business language impressions across industries. Potentially less data points than GPT but arguably better and more relevant impressions. As Lisa Spira, VP of Content Intelligence at Persado explains in an interview with Martechseries, “models like OpenAI’s ChatGPT are trained on the entire “Wild West” of the Internet, so their results, while delivered confidently by the model, can be riddled with inaccuracies, or even offensive content”. She adds that “Generative AI tools might be capable of writing anything, but we’re able to cut through that noise, and train our generative AI to write in a way that motivates recipients to take actions: to open an email, convert on a shopping cart webpage, or stop scrolling and click on a social media ad.”

And not just generative AI. Persado is pushing a term called Motivation AI and they have a really cool example of it. 

Let’s say you’re in D2C and you’re selling sweaters. Generative AI gives you level 1 and 2 content which is Intelligibility and Relevance so it might spit out content like:

  • Sweaters are a knitted garment that usually have long sleeves and are designed to be worn over other clothing
  • In the fall, sweaters provide an extra layer of warmth in the cooler temperatures. They are stylish choice for fall fashion because they come in a variety of colors and styles

But the next stage of this is including Emotion to get an output like this:

  • You’re going to love these cozy sweaters for fall

And the following stage, where Persado claims to play is Motivation where you would get an output like this:

  • A cozy invitation: indulge in our luxuriously soft sweaters this fall

Now this might all be semantics. I’d argue that with great prompting you can get to generative content that includes motivation and emotion. 



This tool could actually go in the CRM, the CDP or even the email category with the acquisitions they’ve made in the last few years but another name that I’ve heard a few times is Optimove. They play in the enterprise arena and primarily serve retailers and gaming operators but they have an interesting suite of AI tools for marketers. 

I’ve personally not used the tool but they claim to provide personalized recommendation campaign orchestration with their AI-based marketing bot (Optibot). It provides what they call self-optimizing campaigns that are essentially advanced A/B/n campaign testing that automatically serves the best treatment to individual customers through the use of ML. 

Source: https://www.optimove.com/pdf/Optimove-Product-Features.pdf 

Predictive Analytics

Predictive analytics is a super cool niche of AI application. It essentially enables us to anticipate trends and patterns of consumer behavior based on a bunch of user data. Done right, you can do cool things like uncover users ready to buy amongst a sea of tire kickers, find free users primed for upsells and anticipate customers about to churn.

Vidora Cortex (mParticle Intelligent Attributes)


I wanted to start by calling out a few no-code / low-code predictive analytics / ML pipeline platforms. There’s not a ton in this space but it has been growing over the past few years. Many of the big analytics players like Qlik’s AutoML, IBM’s Watson Studio, Amazon SageMaker, Google’s AutoML, have a ML specific tool that does this but it’s built for data scientists. Vidora is worth calling out here because it was built more for knowledge workers.

Vidora was founded by 3 academics while pursuing their Ph.Ds Caltech and Berkeley, they built systems that utilized machine learning to convert raw data into informed decisions. Their initial vision was to democratize machine learning capabilities. They are a small 20 person startup in SF but their product offers huge potential, so much so that they were acquired by mParticle, a popular CDP. 

Vidora’s platform, Cortex, provides you with the ability to build distinct pipelines for your customer data, so you can then produce various predictive attributes tailored for particular applications. For example if your goal is to identify the customers with the highest probability of purchasing your product in the next 30 days, the pipeline allows you to enhance your retargeting ROI by focusing on this user segment. 




It’s worth highlighting 6sense here as well. Not specifically an ML tool but it has a wider set of use cases that are a mix of intent data and predictive analytics and a slice of lead scoring… but they’ve been around for a decade. They use real-time data about your buyers to predict their buying intent and what their current purchasing stage might be. They uncover some of the magic in a blog post about their predictive engine here. They claim to be using:

  1. Intent data, web visits, content engagement (1st party) and 3rd party user behavior data from all over the Internet
  2. Historical data, events that led up to sales in the past
  3. ICP, firmographic and technographic data

So they layer intent data on top of historical data and filter it through your ICP and the output is a target list of accounts that show intent to buy and are a good fit for your solution.

The secret sauce here is really the 3rd party intent data. This is hidden data that they are able to assign to leads and accounts. Part of the source here is 6sense’s proprietary intent network and other B2B review sites like G2, TrustRadius, and PeerSpot.



Founded by Jure Leskovec, former Stanford University computer science professor who’s known for his bold assertion that AI would eventually be capable of predicting the future. He partnered with the former CTO of Pinterest and Airbnb as well as the former Head of Growth AI at LinkedIn to build Kumo.

Using Kumo, companies can not only analyze past events but also predict future opportunities. Kumo uses GNNs (Graph Neural Networks) to identify patterns and relationships in complex data sets that cannot be easily analyzed using traditional statistical or machine learning techniques. This essentially allows marketers to anticipate customer behavior (how much they will spend, which new products they will be more interested in, things that would make them leave for a competitor) and offer personalized product recommendations, promotions, and communication.

Predictive analytics isn’t a new concept though. We talked a bit about this in our first episode when we mentioned propensity models which tons of larger companies employ today. But this is operationalizing it a step further and not just on your company’s datasets. And imagine unlocking this capability for startups. 

So the idea is that marketing operations teams would change their focus to future customer behaviors. 




There are countless AI productivity tools that are all super similar. But one that’s been buzzing lately is Tome. They are founded by product leads from Facebook and Instagram, and recently got $43 million in their Series B funding round. 

They launched an innovative document-to-presentation AI tool, which utilizes GPT-4 to generate text and images and transform a document into compelling presentations, narratives, and stories. 

Tome’s tile system sets it apart from other generative PowerPoint tools on the market and gives Microsoft a run for its money. Not only does it offer robust functionality, but it also boasts a sleek and impressive design.


3D Animation

Spline AI


Spline AI was created by Alejandro León, it’s a YC21 startup that’s building a 3D design platform comparable to Figma, it basically allows you to convert text to 3D. For decades, creating in 3D has been super hard. Spline is changing this.

You can easily create objects and scenes, edit objects, colors, and properties, add physics and randomness, create animations and events, generate style alternatives, collaborate with others in real-time, and much more. The applications for videos and product marketing are endless here, see a demo here


AI-powered sales tools are popping up every week. Especially in the niche of email outreach. Personalization, subject line optimization, send-time optimization, sales rep coaching, auto suggest follow-up cadences… just a few of the areas where AI can enhance your email campaigns. NLP can analyze email responses and suggest appropriate next steps, helping your sales team respond efficiently. 

There’s tons of players in this space like Cresta, Lyne, Regie. 

Cresta was funded out of the AI Grant program organized by some of the brightest tech minds in AI. Their founder, S. Zayd Enam chose to leave his PhD program at Stanford to establish the startup. They specifically provide AI guidance software and support that elevates each representative, behavioral mentoring of agents to enhance performance and locate solutions and areas to streamline monotonous tasks.

AI Sales Email Coach. It assists you in real-time. Get more positive replies and write better emails faster.





Web creators

Butternut AI


Tagline says it all: Create a website for your business in 20 seconds with AI. Butternut.ai uses generative AI technology that allows users to create websites by simply answering text prompts. The AI technology is designed to function like a personal developer, providing users with an efficient and cost-effective solution for creating and editing websites.

Butternut.ai is continuously improving its website builder and is currently working on its v2 version that will offer more design and editing functionalities. Users can even command the AI to perform specific tasks like creating a pricing page with a CTA to sign up.





Albert is an autonomous and self-learning digital marketing tool that uses ML + NLP as well as analytics to automate and optimize your paid ad campaigns, specifically Google’s search and programmatic channels, as well as Facebook, Instagram, YouTube and Bing. It can automate bidding and budget allocation, keyword and audience targeting, as well as creative testing and optimization. 

So obviously the part that stands out here is that unlike many other campaign AI tools that just spit out recommendations and then a marketer takes the action, Albert claims to be one of the first tools that’s an autonomous AI, it does the action also. Not only that, it’s also making adjustments and improvements constantly. You seem to be able to set guardrails of course. 

They also claim the ability to identify trends, uncover new audience segments, and optimize ad placements. In their docs they say they are more useful in large data sets and B2C environments. 



AutoGPT and AI agents

I don’t think we can go a full episode about AI tools without talking about AutoGPT and AI agents.

Essentially you can assign an objective to an AI agent and they work on tasks that lead to accomplish this objective. It’s making prompting a bit easier, instead of giving full instructions, the AI identifies the necessary steps to achieve your goal and some of the more advanced ones generate additional AI to assist. 

You may have seen this on Twitter, I think the first true demo was Yohei Nakajima’s impressive demonstration of babyAGI.

Things started blowing up with AutoGPT, released by SigGravitas just last week. Described as a self-running AI agent that can write its own code, heal itself from errors, and access the internet via Google search. 

It does sound really cool for several reasons:

  • Internet access, 
  • long-term and short-term memory management, 
  • text generation, 
  • and integration with 11 Labs for AI text to speech generation. 

It’s arguable that all of this will be possible with GPT plugins but this is doable today and has different applications, let’s unpack a few examples:

Social media is primed for automation, lots of the work here can already be automated. But imagine setting an AI agent loose with the objective of creating content, scheduling posts (not just posting them) and replying to comments and questions. 

Replying to questions is a huge part of customer service, you could argue this is another area that’s already being automated with chat bots, especially the better ones connected to help docs. But imagine setting an AI agent loose with the objective of really understanding customer questions (not just picking up a keyword and recommending a help article), it could speak any language and you could train it on your brand tone.

We’re still early but there’s already been a ton of innovation here.

Here are some of the tools you can play around with today without reading a GitHub repo:



Cognosys allows you to operate your personal AutoGPT Agent in-browser which is currently available and free to utilize. I’ve personally played around with it quite a bit and it’s pretty wild, it’s not perfect yet but it’s really cool seeing how it will add new tasks to get to the objective. The beta is still on GPT-3.5. Here’s my favorite example:

  • Main objective was to: Build a list of AI tools that have a marketing application, that were released in the last 3 months. Here were the follow-up tasks it added to get to the objective:
    • 1. Retrieve a list of AI tools that were released in the last 3 months.
    • 2. Filter the list to only include tools that have a marketing application.
    • 3. Compile the filtered list into a final list of AI tools with marketing applications released in the last 3 months.
    • The final list of AI tools with marketing applications released in the last 3 months is as follows:
      • 1. Receptiviti: A tool that uses AI to analyze consumer emotions and sentiment in order to optimize marketing strategies. 
      • 2. Cortex: A platform that uses AI to help marketers personalize content and improve customer engagement. 
      • 3. Adext AI: A tool that uses AI to optimize ad campaigns and improve ad performance. 
      • 4. Acoustic: An AI-powered marketing platform that helps businesses automate and optimize their marketing strategies. 
      • 5. Pathmatics: A tool that uses AI to provide insights into ad spending and campaign performance for competitive analysis. 
    • It then added a few extra tasks because it didn’t seem satisfied with the list.
      • Research and compile a list of AI tools that have a marketing application and were released in the last 6 months.
        • The result was 6 tools, but not all 5 from the first task were there.
      • Identify the unique features and functionalities of each AI tool on the list and create a comparison chart to evaluate which tools are most suitable for specific marketing applications.
      • Conduct a survey of marketing professionals to gather feedback and opinions on the identified AI tools and their effectiveness in improving marketing strategies.
        • But then it apologized and said it cannot perform that task… 
      • Research and compile a list of case studies showcasing the successful implementation of AI tools in marketing campaigns.
        • It spun up a case study about Coke using “Albert” to optimize their social media advertising campaigns and Amazon using “Rekognition” to analyze customer behavior and personalize their shopping experience. 
      • Create a step-by-step guide on how to integrate an AI tool into a marketing strategy, including best practices and potential challenges.
        • The step by step wasn’t too bad.
          • 1: Define the problem or opportunity, 
          • 2: Identify the right AI tool by researching and comparing different tools, 
          • 3: Understand the data requirements like type of data, format, frequency updates, 
          • 4: Prepare the data for integration like cleaning and formatting 
          • 5: Integrate the AI tool 
      • Research and compile a list of AI tools that have a marketing application and were released in the last 2 months.
        • Not sure why it did this for 2 months but this time it gave me tools with release dates which was pretty cool but also obvious that I wasn’t getting tools released in the last 2 months, I was only getting tools released in the last 2 months since GPT’s latest batch of data which was mid 2021. 
      • Create a comparison chart to evaluate the unique features and functionalities of each AI tool on the list and determine which tools are most suitable for specific marketing applications.

Also try:




You may have seen their public investor pitch on Twitter, the founder is the former creator of Optimizely and his team built a way for you to record and store everything you’ve said or heard or seen and they make it searchable. Obviously there’s huge privacy considerations with something like this. But people don’t seem to care haha they went from 0 to 700k in ARR in 3 months. 

Perplexity AI


Perplexity is a startup that’s changing the way we search the web. With their conversational search engine, you can ask questions in plain English and get accurate answers from various sources. And with $26 million in Series A funding, (including investments from Ed Gil) they’re looking to revolutionize the search engine game.

Their AI technology sets it apart from traditional search engines like Google, and their chatbot-like interface is user-friendly and intuitive, it is built on top of GPT.

Perplexity’s focus on accuracy is a breath of fresh air in an era where search engines can be manipulated by advertisers and search engine optimization. The Series A funds will be used to optimize their knowledge database and expand their reach. All in all, Perplexity AI is definitely one to watch in the coming years!

Character AI


Imagine being able to have a one-on-one conversation with your favorite celebrity or fictional character – well, that’s now a possibility with Character.ai, an innovative AI website created by two former Google engineers. The platform has been growing in popularity since its launch last September, offering users the opportunity to chat with a wide range of characters for free. What’s more, the interactions are so seamless that some users have reported spending hours chatting with their chosen personality, almost forgetting that they’re talking to a machine.

However, there’s a catch – the interactions are not real, and the AI’s responses may not always be accurate or reliable. Despite this, Character.ai has been embraced by fans who are looking for new ways to engage with their favorite personalities, particularly online. This is especially true for fan fiction lovers who are excited about the tool’s potential for creating new experiences and making the barrier to entry for writing fan fiction much lower.

But as with any new technology, there are concerns about its impact on real-world relationships, especially if users spend more time on it than with the people they care about. Furthermore, the AI technology could be used by stans to go after a perceived enemy of their favorite star, which could be a problem if it leads to harmful interactions or behavior towards other users.

Despite these concerns, Character.ai represents a new frontier for fan culture, offering a new way for fans to engage with their favorite personalities and characters. The platform’s growth in popularity is a testament to the human need for connection, and while it may not substitute actual interactions, it provides a unique and exciting way for fans to engage with their favorite personalities.



Magic, a startup developing an AI-driven tool to help software engineers write, review, debug, and plan code changes, just raised $23 million in a funding round led by Alphabet’s CapitalG. The platform’s CEO, Eric Steinberger, was inspired by AI at a young age and is using his experience as an AI researcher to create a tool that will communicate with users in natural language, helping to improve the speed and cost of developing software.

Steinberger claims that Magic can understand legacy code and collaborate with users on code changes, operating like a pair programmer that learns more about the context of both coding projects and developers. The AI colleague that Magic offers will be able to understand code and can help developers navigate it, allowing companies to scale the impact of their current employees and train new employees with less personal coaching.

What sets it apart is that it allows developers to describe what they want in English, and the AI will understand it and collaborate with them on the changes. This means that developers can work with Magic like a colleague and send messages telling it what changes they want to be made to the code. 

This one isn’t built on GPT, the company has developed its own neural network architecture that can rapidly read code and is capable of detecting, warning about and overcoming potential bugs in the code. 

Honorable mentions

Galileo AI – Create editable UI designs from text description


Notocat – Write your newsletters in Notion and send them to your subscribers

Brainf FM – music made by AI that’s scientifically proven to increase focus


Meeting notes and transcription apps



Image Vectorizer – Turn small images to vector


Speech-to-text generator for podcasts that creates notes, timestamps and summary content



Text-to-speech AI voice generator



Text-to-music AI music generator 


Text-to-SQL query, connect your database, ask a question, get an answer


Teachable’s AI course curriculum generator


The opportunities are endless with AI tools and applications go far beyond marketing. I see too many people on Twitter dunking on using it to help you generate text or as a search engine or fact checker… and they’re missing the big picture. People are using AI to

  • Help them build custom meal plans
  • Custom exercise plans
  • Help them build sleeping plans
  • Help them build routines and schedules with their newborns
  • Planning road trips
  • Planning date ideas
  • Acting as a therapist
  • Getting movie and book recommendations
  • Planning a party or event
  • Designing personal budgets
  • Assisting with resume and cover letter writing
  • Summarizing long articles and youtube videos
  • Write SQL queries
  • Explain python and CSS code in plain English


It’s not like our marketing jobs are gonna vanish overnight, but the shift is happening faster than many of us realize. AI’s no longer just a loosely backed buzzword; it’s doing things today that we used to think were impossible. So, as marketers, we’ve gotta take this tech seriously.

There’s 4 main things marketers should be doing to future-proof their careers:

  1. Instead of asking if AI’s gonna replace our roles in marketing, we should be talking about how quickly it could happen and what it’ll look like if it does.
  2. Staying informed and learning from some of the best podcasts and newsletters about AI. Ben’s Bites, No Priors Podcast, A Guide for Thinking Humans and the AI Exchange are great resources.  (See more)
  3. Now is the time to figure out if you need to make changes to your current area of speciality. Ask yourself if you should double down on additional areas like data and API services, getting closer to product and customers or starting to learn about ethics and data privacy.
  4. Stay curious, get your hands dirty and experiment fearlessly: Fill out some forms, spin up free trials, get on wait lists, and give new AI tools a chance. It’s only by actually getting your hands dirty that you’ll discover which tools truly work for you and which are just part of the ever growing sea of gimmicky AI tools.


Intro music by Wowa via Unminus
Cover art created with Midjourney

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