Vertical AI Agents Could Be 10X Bigger Than SaaS

Gary, Jared Harge, and Diana discuss the transformative potential of vertical AI agents, predicting they will create $300 billion companies by automating tasks traditionally handled by SaaS products and human teams. They highlight the evolution of software from early SaaS to AI-driven solutions, emphasizing that AI can replace repetitive administrative work across industries, thus significantly reducing the need for large teams. The conversation touches on the competitive landscape, with new AI models challenging OpenAI, fostering a dynamic marketplace. They also explore how startups can identify viable verticals by targeting mundane, repetitive tasks.

• Vertical AI agents are poised to revolutionize industries by replacing entire teams and functions within enterprises.
• The growth and potential of vertical AI are not fully appreciated, especially by startup founders.
• Predictions indicate the emergence of multiple billion-dollar companies within this category.

"Every 3 months things have just kept getting progressively better and now we're at this point where we're talking about full-on vertical AI agents that are going to replace entire teams and functions and Enterprises."

• The rapid advancement of AI technology is leading to transformative changes in enterprise operations.

"I think people especially startup Founders especially young ones are not fully appreciating just how big vertical AI agents are going to be."

• There is a lack of awareness among startup founders about the vast potential of vertical AI agents.

• The market is experiencing increasing competition, with multiple players entering the field dominated by OpenAI.
• A competitive market fosters innovation and provides consumers with more choices.

"There used to be only one player in town with open AI but we've been seeing in the last batch this has been changing thank God it's like competition is you know the the soil for a very fertile Market Marketplace ecosystem."

• The emergence of new competitors in the AI space is crucial for a healthy and innovative market ecosystem.

• The growth trajectory of vertical AI is compared to the historical development of SaaS.
• SaaS has been a dominant focus in Silicon Valley, with a significant portion of venture capital directed towards it.
• SaaS companies have historically outnumbered other types of startups, highlighting their prevalence and success.

"Most of what has been coming out of Silicon Valley it's over 40% of all venture capital dollars in that time period went to SAS companies and we produced over 300 SAS unicorns in that 20-year time period."

• SaaS has been a major focus of investment and innovation in Silicon Valley over the past two decades.

• The introduction of XML HTTP request was pivotal in enabling rich internet applications, catalyzing the SaaS boom.
• The transition from desktop software to web-based applications marked a significant shift in software delivery.

"In 2004 browsers added this JavaScript function XML HTTP request which was the missing piece that enabled you to build a rich internet application in a web browser."

• The introduction of XML HTTP request was a key technological advancement that facilitated the rise of SaaS.

• The SaaS landscape saw three main categories of successful companies: obvious mass consumer products, non-obvious consumer ideas, and B2B SaaS companies.
• Incumbents often dominated obvious consumer categories, while startups found success in less obvious or niche markets.

"There is no like Microsoft of SAS like there is no company that somehow does like SAS for like every vertical and every product."

• The diversity of SaaS applications has prevented any single company from dominating the entire market.

• The development of large language models (LLMs) is seen as a new computing paradigm similar to the early days of SaaS.
• There is potential for startups to capitalize on new opportunities created by AI, similar to the SaaS boom.

"I see this llm thing as like actually very similar um it's like it's a new Computing Paradigm that makes it possible to just like do something fundamentally different."

• The emergence of LLMs represents a transformative shift in computing, akin to the advent of SaaS.

• Incumbents often hesitate to enter risky or highly regulated markets, allowing startups to innovate and capture market share.
• The fear of regulatory challenges and potential financial risks deter established companies from pursuing certain innovations.

"If you're Google and you have basically a guaranteed you know giant a pot of gold that you know sort of comes to you every single month like why would you endanger that pot of gold to sort of pursue these things that uh might be scary or might ruin the pot of gold."

• Established companies prioritize protecting their existing revenue streams over pursuing potentially risky innovations.

• The success of B2B SaaS companies is attributed to their specialization and deep understanding of specific domains.
• The complexity and niche nature of B2B applications make it challenging for large incumbents to dominate every vertical.

"Each B2B SAS company really requires like the people who are running the product in the business to be extremely deep in one domain and care very deeply about a lot of really obscure issues."

• The specialized knowledge required for B2B SaaS success creates opportunities for focused startups to thrive.

• Salesforce revolutionized enterprise software by proving that SaaS solutions could be as powerful and sophisticated as expensive enterprise installations.
• Traditional enterprise software often offers a poor user experience due to its need to cover a broad range of functions, leading to a "jack of all trades, master of none" scenario.
• Vertical SaaS companies can provide a significantly better user experience by focusing on specific business needs.

"Salesforce comes along with like a SaaS solution and it just seems like it could never be as powerful or sophisticated as like the expensive Enterprise installation you just paid for but they proved that it totally was the case."

• Salesforce's success demonstrated that SaaS solutions could match or exceed the capabilities of traditional enterprise software installations.

"If you go and build a B2B SaaS vertical company you could do literally a 10x better experience and more delightful because there's this stark difference between consumer products and Enterprise user experience."

• Vertical SaaS companies can offer a superior user experience by focusing on specific needs rather than trying to cover everything like traditional enterprise software.

• Software is typically priced at three levels: $5 per seat for consumer, $500 per seat for SMB, and $55,000 per seat for enterprise sales.
• Enterprise software often suffers from poor quality because the end users are not the ones making purchasing decisions.

"There's only uh what three price points in software: $5 per seat, $500 per seat, or $55,000 per seat, and that maps directly to Consumer, SMB, or Enterprise sales."

• Software pricing models are segmented into three main categories, each corresponding to different market types.

"Enterprise is terrible software because it's not the user buying it, you know some high-up Mucky muuk inside Fortune 1000 is the person who's getting whined and dined for this mega seven-figure contract."

• Enterprise software often lacks quality because purchasing decisions are made by executives rather than the actual users.

• The rise of LLMs is expected to change how startups grow, potentially reducing the need for large employee numbers.
• Startups might shift towards hiring more software engineers who understand LLMs to automate tasks and reduce costs.

"I'm starting to sense that the Met is shifting a little bit like you actually might want to hire more really good software engineers who understand large language models."

• There is a growing trend towards hiring engineers skilled in LLMs to automate tasks, which may change traditional startup growth strategies.

"It means that I'm going to build LLM systems that bring down my costs that cause me not to have to hire a thousand people."

• LLMs can significantly reduce operational costs by automating tasks, potentially leading to leaner startup teams.

• Employing engineers in roles like marketing can lead to innovative and efficient solutions, as exemplified by the success of Triplebyte.
• Engineers can apply their problem-solving skills to non-traditional areas, achieving high-impact results.

"I remember just talking to him about it and realizing this would be so much better to have an MIT engineer working on like our marketing efforts than any of the marketing candidates I've spoken to."

• Engineering skills can be effectively applied to areas like marketing, leading to innovative solutions and improved outcomes.

"If you put a really smart engineer on some of these tasks, they just find ways to make they find leverage."

• Engineers can bring leverage and efficiency to various business functions by applying their analytical skills.

• Vertical AI agents are poised to disrupt existing SaaS companies by integrating software and human tasks into a single product.
• Enterprises currently face challenges in identifying specific needs for AI agents, but this will evolve over time.

"Literally every company that is a SaaS unicorn you could imagine there's a vertical AI unicorn equivalent in like some new universe."

• The potential for vertical AI agents to disrupt existing SaaS models is considerable, with each SaaS company having an AI counterpart.

"Enterprises often don't know exactly what they want to use these things for."

• Enterprises are still exploring the potential applications of AI agents, indicating a growth phase for the technology.

• The software industry has evolved from general-purpose solutions to specialized vertical solutions, a trend likely to continue with AI.
• Enterprises are increasingly open to adopting vertical AI solutions due to their familiarity with the benefits of specialized software.

"There's no reason it has to reset back like LLMs don't have to reset back to a few general-purpose like Enterprise LLM platforms."

• The evolution of software suggests that AI will continue to develop into specialized solutions rather than reverting to general-purpose platforms.

"Some of our companies are getting faster traction in Enterprises for these vertical AI agents than like we've ever seen before."

• The adoption rate for vertical AI agents is accelerating, indicating strong enterprise interest and potential for growth.

• Companies are spending significantly more on payroll than on software, highlighting the potential for more efficient solutions.
• Vertical AI agents can be 10 times larger than the SaaS companies they disrupt due to increased efficiency and reduced need for human intervention.
• Smaller companies can leverage AI to perform tasks traditionally done by humans, such as data entry or approvals, more efficiently.

"The spend for companies big chunk is still a payroll and software's tiny... it's very possible the vertical equivalence will be 10 times as large as the SaaS company that they are disrupting."

• This quote illustrates the significant disparity in spending between payroll and software, suggesting the potential for AI to drastically increase efficiency and reduce costs.

• Selling AI solutions requires targeting decision-makers high enough in the organization to avoid resistance from employees who might feel threatened by automation.
• Companies must navigate the delicate balance of offering solutions that enhance efficiency without directly threatening employees' job security.

"You have to go high enough in the organization so that the people you're selling to are not afraid that their whole job or their whole team's job is going to go away."

• The quote emphasizes the strategic approach needed in selling AI solutions, highlighting the importance of targeting decision-makers who can see the broader benefits of AI beyond immediate job displacement.

• Outset leverages LLMs in the survey space to provide insights for product and marketing teams by interpreting customer language.
• MCH focuses on QA testing, allowing companies to operate without a traditional QA team by providing AI-driven testing solutions.
• AI solutions in recruiting, such as those developed by Triple Vet and Priora, aim to streamline the hiring process by automating technical screenings and recruiter screens.

"MCH with AI can actually just replace the QA people, so their pitch is not oh this like makes your QA people faster it's like this just means you don't need a QA team at all."

• The quote highlights the transformative potential of AI in eliminating the need for traditional QA teams, showcasing a shift in how companies can operate more efficiently.

• The customer support sector is crowded with AI solutions, yet only a few companies are capable of handling complex workflows at scale.
• Hyper-specialization in AI solutions allows companies to tailor software for specific industries or needs, avoiding a one-size-fits-all approach.

"To actually replace a customer support team for like an at-scale company that has like 100 customer support reps... you need like really complicated software."

• This quote underscores the complexity required in AI solutions to effectively replace human teams, emphasizing the need for specialized, sophisticated software.

• Firms can only grow to a point where they remain efficient, after which networks and ecosystems become necessary.
• AI tools for managers and CEOs could potentially increase the scale of firms by enhancing managerial capabilities.

"Any given firm will grow only so much to the point where it becomes inefficient to be larger than that... tools for managers and CEOs are going to get much more powerful."

• The quote reflects on the limitations of firm growth and how AI tools might expand these limits by improving managerial efficiency and decision-making.

• AI SaaS tools are set to expand leaders' ability to process and manage large amounts of information, potentially reshaping organizational structures.
• The evolution of AI tools could lead to the consolidation of multiple SaaS companies into larger entities capable of offering comprehensive solutions.

"With having all these AI SaaS tools it's going to give the ability to all these leaders and all these Orcs to basically open the aperture of the context window of how much information they can parse."

• This quote envisions a future where AI tools enhance leaders' capabilities, allowing for more informed decision-making and potentially larger, more efficient organizations.

• The Dunbar number traditionally limits meaningful relationships to about 150 people, but AI could extend this limit.
• AI technologies, such as large language models, enable broader communication capabilities.

"With AI, because all of these rocks now can read, I think we will be able to extend that Dunbar limit that we have."

• The potential of AI to increase the number of meaningful relationships by enhancing communication capabilities.

• AI can facilitate communication between CEOs and employees, providing summaries of employee activities.
• This technology can enhance organizational understanding and efficiency.

"It will call up all your employees and then your employees can just ramble about what they've been doing, and it will just extract the meaning out of it and give the CEO a bullet point summary."

• AI tools can streamline communication within organizations by summarizing employee feedback for management.

• AI's potential to perform tasks traditionally requiring human cognition, raising questions about the future role of AI in management.
• AI could surpass traditional summarization and engage in deeper analytical thinking.

"I just wonder if at some point do the LLMs go beyond just summarizing and reading and doing actual thinking, at which point who's actually running the organization?"

• The evolving role of AI in organizations may lead to AI taking on more strategic roles traditionally held by humans.

• Rippling's strategy involves recruiting founders to run specific SaaS verticals, creating a diverse and innovative team.
• The company focuses on horizontal expansion and shared infrastructure, similar to Amazon's approach.

"Rippling is essentially the case against verticalization, trying to horizontalize like lots of value, and he wants to recruit founders and teams that build on top of the platform."

• Rippling's business model emphasizes horizontal growth and leveraging founder-driven innovation.

• AI voice technology is rapidly advancing, automating tasks like debt collection calls, which traditionally require large human workforces.
• This technology offers significant efficiency improvements and reduces the need for monotonous human labor.

"Salient has been able to actually get very, very accurate and it has been going live with a lot of big banks, which is super exciting."

• AI voice technology is transforming industries by automating repetitive tasks and improving operational efficiency.

• LLM-powered applications have evolved from basic text generation to sophisticated AI agents capable of replacing entire teams.
• The rapid progression of AI technologies is unprecedented and continues to accelerate.

"We're talking about full-on vertical AI agents that are going to replace entire teams and functions in enterprises."

• The evolution of AI applications highlights the potential for AI to revolutionize business operations by automating complex tasks.

• The emergence of multiple players in the AI foundation model space is fostering a competitive and innovative ecosystem.
• This competition benefits consumers and entrepreneurs by providing more choices and opportunities.

"Claude is a huge contender. Thank God it's like competition is the soil for a very fertile marketplace ecosystem."

• The growing competition among AI foundation models is driving innovation and offering more options for businesses and consumers.

• Successful AI startups often target boring, repetitive administrative tasks that can be automated.
• Founders with personal experience or relationships with these tasks are more likely to identify promising AI opportunities.

"If you can find a boring repetitive admin task, there is likely going to be a billion-dollar AI agent startup."

• Identifying mundane tasks ripe for automation is a common thread among successful AI startups, emphasizing the importance of domain knowledge.

• The discussion highlights the transformative potential of AI across various domains, from extending human relationships to revolutionizing business operations.
• The rapid advancement and competition in AI technologies offer exciting opportunities for innovation and efficiency improvements.