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The Future of AI: Top Tools and Apps to Watch in 2023

Table of Contents

Background on Matt Wolfe's Pivot into the AI Space

Matt Wolfe has been creating digital marketing content since 2009. He started with a blog and YouTube channel covering topics like WordPress plugins and affiliate marketing. Over the years, he experimented with different content formats and online courses, trying to find what worked best.

In 2022, Wolfe noticed his content about AI tools like Dall-E and Stable Diffusion was gaining more traction. When ChatGPT launched in late 2022, Wolfe made a tutorial video that got over 60,000 views overnight. He realized AI content was hitting a nerve and decided to go all in.

To keep track of the explosion of new AI tools, Wolfe built the website Future Tools. This database of AI apps went viral on Twitter and now gets over 1 million views per month.

The Tipping Point: Creating Viral AI Content

Wolfe had been creating digital marketing content for over a decade, testing different topics and formats. In 2022 he noticed his AI content, especially focused on new generative AI tools, was getting far more traction than other topics. A tutorial video on ChatGPT went viral overnight, convincing Wolfe there was a huge appetite for material explaining the latest AI tools. He decided to pivot his content and focus exclusively on AI going forward.

Expanding with Future Tools to Track AI Innovation

To keep up with the rapid pace of advancement in AI, Wolfe built the site Future Tools as a personal database of new apps. He shared Future Tools publicly on Twitter where it spread rapidly. With over 1 million monthly views, Future Tools allows Wolfe and others to track the latest AI tools and innovations. It's become a vital resource in such a fast-moving space.

Investment Opportunities in the AI Landscape

When looking at investing in AI, the safest bets are picks and shovels plays like Nvidia that will benefit no matter which platforms come out on top. Microsoft is well positioned by partnering with both OpenAI and Meta across open source and proprietary models.

Many AI startups lack differentiation and defensible technology. With open source models and abundant access to APIs, competitors can easily replicate many AI products.

Economics will be challenging for AI firms relying purely on large language models or image/video generation. Customer churn and falling prices will make it difficult to build sustainable businesses.

The pace of innovation leaves many AI products obsolete quickly. Constant advancement makes it tough for startups to keep delivering novel value and prevent churn.

AI Chat Tools: Picks and Shovels Approach

LLM chat tools like ChatGPT, BARD, and Anthropic's Claude have limitations as standalone businesses. There is no strong moat as new models can be built on open source libraries like LaMDA and LLaMA.

Microsoft is smartly positioning itself on both the open source and proprietary side by partnering with Meta and OpenAI. This allows Microsoft Azure to benefit widely as more companies tap into LLM chat.

Pure play LLM chat startups will face increasing competition from open source alternatives. Most lack the resources to continually push state of the art proprietary models over the long term.

AI Creative Tools: Mind-Blowing Innovation

Tools like DALL-E 2, Midjourney, and Stable Diffusion showcase the enormous potential of AI for creative work. However, the core tech behind these systems is being open sourced, allowing competitors to quickly build similar offerings.

Small startups focusing solely on AI art, writing, and video will struggle to differentiate. But they help illustrate the tremendous possibilities before tech giants integrate these capabilities into multifunction platforms.

B2B AI Tools: Driving Efficiency Gains

CRM, sales, customer support and other B2B AI tools promise major efficiency gains. However, integrating AI into complex enterprise workflows presents sizable technical challenges.

Startups focusing narrowly on AI sales or support lack staying power. But the B2B giants have the data, industry know-how and resources to gradually infuse AI throughout their platforms.

The stand-alone AI startups will drive initial awareness and use cases before being outgunned by deep-pocketed incumbents owning the customer relationship.

Data Analysis AI: Unlocking Insights

AI model APIs that integrate with data platforms provide tremendous potential to automate analysis and uncover insights. Tableau's acquisition of startup Empath exemplifies the value.

Vertical AI startups focusing on specific data analysis use cases will be acquired or run over by large horizontal platforms adding smart features.

Data and analytics giants will dominate by leveraging broad data access and resources to build the best overall AI user experience.

Knowledge Work AI: Improving Collaboration

AI has great potential to capture and connect knowledge across rapidly growing teams. Startups like Glean work on knowledge graph tools to unlock insights from company data.

Capturing tribal knowledge will only grow more important as remote and hybrid work expands. Expect savvy collaboration platforms to add AI features that gather and connect information.

Over time company-specific AI models trained on internal data and interactions may become a vital asset. Platforms owning these rich integrated data flows will have the edge.

Productivity AI: Personalized Assistants

Personal productivity AI like Anthropic's Claude allows individual users to automate simple tasks and access info through conversational interfaces.

As AI chatbot tech improves, virtual assistants may become ubiquitous in both consumer and business settings. Tech giants are primed to bundle these bots into their core platforms.

Narrow assistive AI startups will be challenged to survive alone long-term. The winners will be productivity platforms with the best overall AI user experience.

Custom AI: The Future of Business

In the future, companies will leverage custom AI models trained on their unique data and workflows. Personal.ai allows users to create AI based on their own content and conversations.

Verticalized AI has the potential to automate processes and boost efficiency in nearly every industry. Domain expertise and proprietary data will drive competitive advantage.

Heavily customized in-house AI may become a vital enterprise asset. The winners will balance using prebuilt modules with crafting proprietary models.

FAQ

Q: How did Matt Wolfe get started in AI content creation?
A: Matt had been creating digital marketing content since 2009. In 2022, he noticed high viewer interest when he made some YouTube videos about AI art generators. He continued testing AI video topics, with his ChatGPT explanation video hitting over 1 million views.

Q: Where does Matt recommend investing in AI companies?
A: Matt suggests investing in the 'picks and shovels' companies that provide the infrastructure for AI models, like Nvidia and Microsoft Azure, rather than betting on individual AI startups.

Q: What AI creative tools blew Matt's mind recently?
A: ElevenLabs text-to-speech and tools like Midjourney for AI art generation impressed Matt with rapid innovation. However, he notes concerns around deepfakes and spoofing with such advancements.

Q: How can custom AI models benefit businesses?
A: Matt discusses AI trained on company data for customized chatbots, sales assistants, support agents or other interfaces. This allows tailored experiences for different customer needs.

Q: Why did the founders not understand the appeal of Character.ai?
A: Although unclear to them, Matt notes the digital companion app has significant appeal among pre-teen kids, similar to virtual pets. Major backers see growth potential there.

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