Summary. Many entrepreneurs are considering starting companies that leverage the latest generative AI technology, but they must ask themselves whether they have what it takes to compete on increasingly commoditized foundational models, or whether they should instead differentiate on an app that leverages these models.
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I am thinking of starting a company that employs generative AI but I am not sure whether to do it. It seems so easy to get off the ground. But if it is so easy for me, won’t it be easy for others too?
This year, more entrepreneurs have asked me this question than any other. Part of what is so exciting about generative AI is that the upsides seem limitless. For instance, if you have managed to create an AI model that has some kind of general language reasoning ability, you have a piece of intelligence that can potentially be adapted toward various new products that could also leverage this ability — like screen writing, marketing materials, teaching software, customer service, and more.
For example, the software company Luka built an AI companion called Replika that enables customers to have open-ended conversations with an “AI friend.” Because the technology was so powerful, managers at Luka began receiving inbound requests to provide a white label enterprise solution for businesses wishing to improve their chatbot customer service. In the end, Luka’s managers used the same underlying technology to spin off both an enterprise solution and a direct-to-consumer AI dating app (think Tinder, but for “dating” AI characters).
In deciding whether a generative AI company is for you, I recommend establishing answers to the following two big questions: 1) Will your company compete on foundational models, or on top-layer applications that leverage these foundational models? And 2) Where along the continuum between a highly scripted solution and a highly generative solution will your company be located? Depending on your answers to these two questions, there will be long-lasting implications for your ability to defend yourself against the competition.
Foundational Models or Apps?
Tech giants are now renting out their most generalizable proprietary models — i.e., “foundational models” — and companies like Eluether.ai and Stability AI are providing open-source versions of these foundational models at a fraction of the cost. Foundational models are becoming commoditized, and only a few startups can afford to compete in this space.
You may think that foundational models are the most attractive, because they will be widely used and their many applications will provide lucrative opportunities for growth. What is more, we are living in exciting times where some of the most sophisticated AI is already available “off the shelf” to get started with.
Entrepreneurs who want to base their company on foundational models are in for a challenge, though. As in any commoditized market, the companies that will survive are those that offer unbundled offerings for cheap or that deliver increasingly enhanced capabilities. For example, speech-to-text APIs like Deepgram and Assembly AI compete not only with each other but with the likes of Amazon and Google in part by offering cheaper, unbundled solutions. Even so, these firms are in a fierce war on price, speed, model accuracy, and other features. In contrast, tech giants like Amazon, Meta, and Google make significant R&D investments that enable them to relentlessly deliver cutting-edge advances in image, language, and (increasingly) audio and video reasoning. For instance, it is estimated that OpenAI spent anywhere between $2 and $12 million to computationally train ChatGPT — and this is just one of several APIs that they offer, with more on the way.
Instead of competing on increasingly commoditized foundational models, most startups should differentiate themselves by offering “top layer” software applications that leverage other companies’ foundational models. They can do this by fine-tuning foundational models on their own high quality, proprietary datasets that are unique to their customer solution, to provide high value to customers.
For instance, the marketing content creator, Jasper AI, grew to unicorn status largely by leveraging foundational models from OpenAI. To this day, the firm uses OpenAI to help customers generate content for blogs, social media posts, website copy and more. At the same time, the app is tailored for their marketer and copywriter customers, providing specialized marketing content. The company also provides other specialized tools, like an editor that multiple team members can work on in tandem. Now that the company has gained traction, going forward it can afford to spend more of its resources on reducing its dependency on the foundational models that enabled it to grow in the first place.
Since the top-layer apps are where these companies find their competitive advantage, they lie in a delicate balance between protecting the privacy of their datasets from large tech players even as they rely on these players for foundational models. Given this, some startups may be tempted to build their own in-house foundational models. Yet, this is unlikely to be a good use of precious startup funds, given the challenges noted above. Most startups are better off leveraging foundational models to grow fast, instead of reinventing the wheel.
From Scripted to Generative
Your company will need to live somewhere along a continuum from a purely scripted solution to a purely generative one. Scripted solutions involve selecting an appropriate response from a dataset of predefined, scripted responses, whereas generative ones involve generating new, unique responses from scratch.
Scripted solutions are safer and constrained, but also less creative and human-like, whereas generative solutions are riskier and unconstrained, but also more creative and human-like. More scripted approaches are necessary for certain use-cases and industries, like medical and educational applications, where there need to be clear guardrails on what the app can do. Yet, when the script reaches its limit, users may lose their engagement and customer retention may suffer. Moreover, it is more challenging to grow a scripted solution because you constrain yourself right from the start, limiting your options down the road.
On the other hand, more generative solutions carry their own challenges. Because AI-based offerings include intelligence, there are more degrees of freedom in how consumers can interact with them, increasing the risks. For example, one married father tragically committed suicide following a conversation with an AI chatbot app, Chai, that encouraged him to sacrifice himself to save the planet. The app leveraged a foundational language model (a bespoke version of GPT-4) from EluetherAI. The founders of Chai have since modified the app to so that mentions of suicidal ideation are served with helpful text. Interestingly, one of the founders of Chai, Thomas Rianlan, took the blame, saying: “It wouldn’t be accurate to blame EleutherAI’s model for this tragic story, as all the optimization towards being more emotional, fun and engaging are the result of our efforts.”
It is challenging for managers to anticipate all the ways in which things can go wrong with a highly generative app, given the “black box” nature of the underlying AI. Doing so involves anticipating risky scenarios that may be highly rare. One way of anticipating such cases is to pay human annotators to screen content for potentially harmful categories, such as sex, hate speech, violence, self-harm, and harassment, then use these labels to train models that automatically flag such content. Yet, it is still difficult to come up with an exhaustive taxonomy. Thus, managers who deploy highly generative solutions must be prepared to proactively anticipate the risks, which can be both difficult and expensive. The same goes for if later you decide to offer your solution as a service to other companies.
Because a fully generative solution is closer to natural, human-like intelligence, it is more attractive from the standpoint of retention and growth, because it is more engaging and can be applied to more new use cases.
• • •
Many entrepreneurs are considering starting companies that leverage the latest generative AI technology, but they must ask themselves whether they have what it takes to compete on increasingly commoditized foundational models, or whether they should instead differentiate on an app that leverages these models.
They must also consider what type of app they want to offer on the continuum from a highly scripted to a highly generative solution, given the different pros and cons accompanying each. Offering a more scripted solution may be safer but limit their retention and growth options, whereas offering a more generative solution is fraught with risk but is more engaging and flexible.
We hope that entrepreneurs will ask these questions before diving into their first generative AI venture, so that they can make informed decisions about what kind of company they want to be, scale fast, and maintain long-term defensibility.
FAQs
What is the difference between conversational AI and generative AI? ›
Conversational AI Vs. Generative AI: Purpose, Functionality, and Technology. Now, the differences between these two AI subfields lie in their purpose, functionality, and technology. While conversational AI is about interacting in human-like conversations, generative AI focuses on creating new, unique content.
What is generative AI and why is it suddenly everywhere? ›Generative AI exploded onto the scene in late 2022 when OpenAI, a San Francisco-based tech company, released Dall-E, an image generator, and ChatGPT, an AI chatbot, that allowed anyone to use them to create art or text. Competitors responded in kind, flooding the market with similar products.
How can companies use generative AI? ›Companies that use AI to streamline processes, automate workflows, augment human creativity and improve operational efficiency can use generative AI's ability to understand and optimize complex systems. This will expand its accessibility across the enterprise, driving next-level productivity and cost savings.
What are the risks of Gen AI? ›- Poor development process. ...
- Elevated risk of data breaches and identity theft. ...
- Poor security in the AI app itself. ...
- Data leaks that expose confidential corporate information. ...
- Malicious use of deepfakes.
Generative AI technology uses natural language processing (NLP) and machine learning (ML) to generate new data or content that mimics human-generated content.
What are three 3 main categories of AI algorithms? ›There are three major categories of AI algorithms: supervised learning, unsupervised learning, and reinforcement learning. The key differences between these algorithms are in how they're trained, and how they function.
Is generative AI the future? ›In the future, we can expect many more designers to adopt these processes and AI to play a part in the creation of increasingly complex objects and systems. Generative AI has the potential to significantly impact the way video games are designed, built, and played.
What is the market potential of generative AI? ›Across the 63 use cases we analyzed, generative AI has the potential to generate $2.6 trillion to $4.4 trillion in value across industries.
What is the best generative AI right now? ›- GPT-4.
- ChatGPT.
- AlphaCode.
- GitHub Copilot.
- Bard.
- Cohere Generate.
- Claude.
- Synthesia.
The global Generative AI Market size was valued at USD 8.2 Billion in 2021 and is projected to reach USD 126.5 Billion by 2031, growing at a CAGR of 32% from 2022 to 2031.
What companies are already using generative AI? ›
Major companies like Alphabet, Apple, Microsoft, Nvidia, among many others, are already drooling over the countless applications of generative AI.
What are the benefits of generative AI in business? ›Businesses can benefit from generative AI to achieve results faster than manual labor. For instance, generative AI can create graphics and films faster than humans can accomplish the same activity. Businesses may benefit from this by completing tasks more quickly and effectively.
Who is the expert in generative AI? ›Nina Schick
Renowned as one of the first GenAI experts, Nina is now dedicated to informing people on how artificial intelligence will change humanity as we know it, a message that she spreads as the Founder of Tamang Ventures and the Creator of The Era of Generative AI.
These negative effects include unemployment, bias, terrorism, and risks to privacy, which the paper will discuss in detail.
What can generative AI not do? ›Generative AI can't generate new ideas or solutions
One of the key limitations of AI is its inability to generate new ideas or solutions.
Generative AI was introduced in the 1960s in chatbots.
How many generative AI models are there? ›As for now, there are two most widely used generative AI models, and we're going to scrutinize both. Generative Adversarial Networks or GANs — technologies that can create visual and multimedia artifacts from both imagery and textual input data.
What are the 3 C's of AI? ›Collaboration, Compassion, and Consciousness (3 C's) are the keys to building AI and saving humanity.
What is the easiest algorithm in AI? ›Linear Regression is the most simple and effective regression algorithm.
Which is the most advanced form of AI? ›Deep Learning is the most advanced form of Artificial Intelligence out of these three. Then comes Machine Learning which is intermediately intelligent and Artificial intelligence covers all the concepts and algorithms which, in some way or the other mimic human intelligence.
What AI will never replace? ›
But what AI will never replace is the ability to create relationships with clients that serve the alignment of business and communications strategy. Because while AI listens to reply, people listen to understand. - Starr Million Baker, INK Communications Co.
Why is everyone talking about generative AI? ›Disruptive potential in various industries
Generative AI has disruptive potential in a wide range of industries. Its ability to generate creative and compelling content can have a significant impact in areas such as entertainment, advertising, design, fashion, and education, among others.
Improved Natural Language Generation
In 2023, we will see improved NLP technology as one of the latest AI trends for virtual assistants, sentiment analysis, named entity recognition (NER), multilingual models, semantic search, conversational AI, and reinforcement learning.
Revenues of generative artificial intelligence (AI) technology offerings are forecast to reach $3.7 billion in 2023 and expand to $36 billion by 2028 with a compound annual growth rate (CAGR) of 58 percent from 2023 to 2028, according to a new S&P Global Market Intelligence report.
Which sector is AI fastest growing? ›The demand for AI and machine learning (ML) specialists will grow at the fastest rate in the next five years, as per a report by the World Economic Forum. They are followed by sustainability specialists, business intelligence analysts and information security analysts, says WEF's Future of Jobs Report 2023.
How much will the AI industry be worth 2025? ›AI industry will reach worth of $90 billion by 2025.
Who are the main players in generative AI? ›| Aspects | Details |
|---|---|
| Key Market Players | Rephrase.ai, D-ID, Google LLC, Genie AI Ltd., MOSTLY AI Inc., Adobe., Microsoft Corporation, Synthesia, IBM Corporation, Amazon Web Services, Inc. |
Ameca is the world's most advanced, most realistic humanoid robot created by Engineered Arts in 2021. Ameca's first video was released publicly on Dec 1, 2021, and received a lot of attention on Twitter and TikTok.
What is the most popular generative AI? ›- GPT-4. GPT-4 is the most recent version of OpenAI's Large Language Model (LLM), developed after GPT-3 and GPT-3.5. ...
- ChatGPT. ...
- AlphaCode. ...
- GitHub Copilot. ...
- Bard. ...
- Cohere Generate. ...
- Claude. ...
- Synthesia.
Can you sell AI-generated art? Yes, AI-generated art can be sold just like any other artwork. In fact, there is a growing market for AI art, with some pieces selling for significant amounts of money. Here is a summary of the most popular styles of AI art which can be sold online.
Can you make money selling AI-generated art? ›
Print-on-demand products made from your AI art are one of the most lucrative ways to make money. You should simply transfer your plans to sites like TeeSpring and RedBubble. Your artwork will be printed on a variety of products from mugs and t-shirts to posters and notebooks by these websites.
How popular is generative AI? ›Popularity of generative AI in marketing in the U.S. 2023
According to a 2023 study conducted with marketers in the United States, 73 percent of respondents reported using generative artificial intelligence tools, such as chatbots, as a part of their company's work.
On January 26, 2014, Google confirmed its acquisition of DeepMind for a price reportedly ranging between $400 million and $650 million. and that it had agreed to take over DeepMind Technologies. The sale to Google took place after Facebook reportedly ended negotiations with DeepMind Technologies in 2013.
What are the two main types of generative AI models? ›- Generative adversarial networks (GANs): best for image duplication and synthetic data generation.
- Transformer-based models: best for text generation and content/code completion. ...
- Diffusion models: best for image generation and video/image synthesis.
Generative AI models can generate new financial data or conduct automated financial analysis tasks. One example is the Variational Autoencoder model, which can create artificial financial data to train machine learning models for financial analysis.
What is generative AI and how much power does it have? ›"Generative" refers to the ability of an AI algorithm to produce complex data. The alternative is "discriminative" AI, which chooses between a fixed number of options and produces just a single number. Generative AI can create much more complex outputs, such as a sentence, a paragraph, an image, or even a short video.
Who is the most smartest AI in the world? ›AlphaGo is the most advanced artificial intelligence software developed by Google DeepMind that was designed to play the board game Go.
How do I start learning generative AI? ›- Data collection for training and testing the model. ...
- Preprocessing data to ensure quality and relevance. ...
- Exploring and selecting appropriate generative AI algorithms. ...
- Setting up the development environment. ...
- Building the prototype model and testing it.
- Alan Turing (1912-1954) The earliest significant work in the field of AI was done by Alan Turing. ...
- Allen Newell (1927-1992) & Herbert A. Simon (1916-2001) ...
- John McCarthy (1927-2011) McCarthy earned the A.M. ...
- Marvin Minsky (1927-2016)
Speaking via video link to a summit in London, Musk said he expects governments around the world to use AI to develop weapons before anything else. Elon Musk has hit out at artificial intelligence (AI), saying it is not "necessary for anything we're doing".
How does AI make humans lazy? ›
4. Make Humans Lazy
AI applications automate the majority of tedious and repetitive tasks. Since we do not have to memorize things or solve puzzles to get the job done, we tend to use our brains less and less.
AI's three biggest limitations are (1) AI can only be as smart or effective as the quality of data you provide it, (2) algorithmic bias and (3) its “black box” nature.
What is the danger of generative AI? ›Without proper governance and supervision, a company's use of generative AI can create or exacerbate legal risks. Lax data security measures, for example, can publicly expose the company's trade secrets and other proprietary information as well as customer data.
Who is the godfather of AI? ›Geoffrey Hinton is known as the godfather of artificial intelligence. He helped create some of the most significant tools in the field. But now he's begun to warn loudly and passionately that the technology may be getting out of hand.
Is AI warning of extinction? ›Leading experts warn of a risk of extinction from AI : NPR. Leading experts warn of a risk of extinction from AI The AI programs we are creating could outsmart us and lead to our collective demise, according to the tech industry's leading experts who say it's time to address the threats they pose.
What is the difference between analytical AI and generative AI? ›1. Analytical AI: Traditional AI that focuses on analyzing existing data that can be used for predictions and automation, and is therefore capable of taking over time-consuming, manual tasks in place of humans. 2. Generative AI: A new type of AI that learns from data and creates new data based on what it learns.
What is a generative model AI? ›Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can be used to create new content, including audio, code, images, text, simulations, and videos. Recent breakthroughs in the field have the potential to drastically change the way we approach content creation.
What is the key difference of conversational AI? ›The key differentiator of Conversational AI is the implementation of Natural Language Understanding and other human-loke behaviours.
What is a conversational AI? ›Conversational AI is a type of artificial intelligence (AI) that can simulate human conversation. It is made possible by natural language processing (NLP), a field of AI that allows computers to understand and process human language.
Are chatbots considered generative AI? ›The short answer is that, yes, chatbots do have a purpose in this new world of large language models (LLM) and generative AI. And, if you want to deliver a great customer experience, you'll still be able to with a traditional chatbot – though you'll need to reckon with new expectations from customers. What do I mean?
Is generative AI the same as general AI? ›
Generative AI focuses on creating new content or ideas based on existing data. It has specific applications and is a subset of AI that excels at solving particular tasks. General AI, also known as artificial general intelligence, broadly refers to the concept of AI systems that possess human-like intelligence.
Is generative AI the same as machine learning? ›Purpose: Traditional machine learning algorithms focus on understanding data and making accurate predictions. Generative AI, however, seeks to create new data that resembles the training data.
Can generative AI write code? ›Rather than scouring the internet or developer community groups for help with code examples, generative AI models can be used to help generate new programming code with natural language prompts, complete partially written code with suggestions, or even translate code from one programming language to another.
Is Grammarly generative AI? ›With GrammarlyGO, the Grammarly experience now comes with the power of generative AI—across the digital spaces you write in most. The AI communication assistant that's up to speed on your context and preferred writing style, GrammarlyGO keeps you moving. GrammarlyGO capabilities are available on desktop only right now.
What is Level 3 of conversational AI? ›Level 3: Contextual Assistants
Conversations are unique and not linear so there isn't an option to catch all and predict what the user will say. Contextual conversation means understanding every part of the conversation. Even when you are not expecting it.
Artificial intelligence is generally divided into two types – narrow (or weak) AI and general AI, also known as AGI or strong AI.
What are the disadvantages of conversational AI? ›No empathy: Machines lack emotions so they cannot empathize with a customer who may be feeling low, angry, or frustrated. Live, human customer agents can understand and relate to the sentiment of the customer. However, this is not the case with machines.
How much does a conversational AI earn? ›As of Jun 19, 2023, the average annual pay for an Ai Conversation Designer in the United States is $55,954 a year. Just in case you need a simple salary calculator, that works out to be approximately $26.90 an hour. This is the equivalent of $1,076/week or $4,662/month.
What is the most advanced conversation AI? ›The best overall AI chatbot is the new Bing due to its exceptional performance, versatility, and free availability. It uses OpenAI's cutting-edge GPT-4 language model, making it highly proficient in various language tasks, including writing, summarization, translation, and conversation.
Which programing language is best for AI? ›Python is a popular choice for artificial intelligence (AI) development due to its simplicity, readability and versatility. It has a vast collection of libraries and frameworks for machine learning, natural language processing and data analysis, including TensorFlow, Keras, PyTorch, Scikit-learn and NLTK.