Generative AI Roadmap: Hype vs. Reality

9 min readMar 23, 2023

Written by Daniel Applewhite, edited by GPT3, sources included.

Generative AI and Natural Language Processing (NLP) are two of the most promising technologies that have taken the world by storm. NLP is a technology that enables machines to understand human language and respond appropriately. This unlock in technology has enabled Generative AI to leverage machine learning algorithms to generate content such as text, images, and videos across a variety of industries.

Generative AI and natural language processing (NLP) are the most promising technologies in recent history and have captured the imagination of us all. However, as with any new technology, generative AI has undergone a hype cycle. Is the hype deserved? How will our lives really change, and who’s profiting from it?

The hype cycle is a model that describes the stages of technology adoption. It was first introduced by Gartner in 1995 and is used to track the maturity, adoption, and social application of specific technologies. The hype cycle consists of five stages: the technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity.

STAGE 1 // The technology trigger is the first stage of the hype cycle. It is the stage when a new technology is introduced to the market. At this stage, the technology is still in its early stages, and there is a lot of excitement and speculation about its potential.

STAGE 2 // The peak of inflated expectations is the second stage of the hype cycle. This is where Generative is. Microsoft’s $10B commitment, the commercialization of GPT4, and the first product ever to capture 100 million users in less than ten months; the hype is alive. At this stage, the technology has gained a lot of attention, and there is a lot of hype surrounding it. People have high expectations about the technology, and there is a lot of speculation about its potential impact.

STAGE 3 // The trough of disillusionment is the third stage of the hype cycle. At this stage, people begin to realize that the technology is not as revolutionary as they initially thought. Some say this is what happened with altcoins and NFT’s. This stage can be generally marked by sharp decreases in news headlines, decreased investments, and the introduction of new hype.

STAGE 4 // The slope of enlightenment is the fourth stage of the hype cycle. At this stage, people begin to understand the true potential of the technology. They have a better understanding of its capabilities and limitations, and they begin to develop more realistic expectations.

STAGE 5 // The plateau of productivity is the final stage of the hype cycle. At this stage, the technology has become widely adopted, and its benefits are well understood. It has become an established technology, and its impact is felt across various industries.

Following the money — VC Investments in Generative AI

Generative AI has been making waves in the tech industry with its ability to generate new content such as text, images, and videos. The technology has found its use in various industries, including healthcare, finance, and entertainment. Venture capitalists often take notice of emerging technology before Fortune 500 companies, and this space is no different. Pre-empting massive bets from Microsoft, Google has long been chasing to unlock the potential of ai (although stumbling in their recent product BARD). The startups of yesterday and tech titans of today have been investing heavily in generative AI startups, but the past two years has seen a steep jump, from $7 billion in AI investments in 2016, to a staggering $68 billion in 2021.

According to a report by CB Insights, generative AI startups raised $704 million in venture capital funding in Q1 2021. This represents a 9% increase from the previous quarter and a 37% increase from the same period last year. The report also states that generative AI is one of the fastest-growing sectors in AI, with a 78% compound annual growth rate since 2015.

One of the main drivers of investment in generative AI is its potential to automate tasks and reduce human intervention. This has made the technology particularly attractive to industries such as healthcare and finance, where the automation of tasks can lead to significant cost savings.

Two examples of whales in this space are OpenAI and Primer, who raised significant amounts of funding in 2021. Primer raised $110 million in a Series C funding round in 2021 as one of the larger investments at the beginning of this cycle, while Big Tech and Open AI race to embrace the new tech.

Mapping Out the Use of Generative AI and NLP Models in Established Companies

More on this in my post on Where Startups can Succeed in Generative AI. Established companies and venture capital dollars are pouring into products leveraging generative AI and Natural Language Processing (NLP) technology due to their potential to automate tasks and reduce human intervention. The technology has proven to be useful in various industries, including healthcare, augmented coding, B2B SaaS, chip production, finance, and entertainment. With Adobe, Amazon, Google, Microsoft, Salesforce, and countless other big tech companies investing billions of dollars in generative AI technology, how will our lives change, and where do startups actually have a chance of succeeding?

1. Healthcare — Extending human life

In the healthcare industry, Generative AI is being used to analyze medical images and provide insights to doctors. For instance, GE Healthcare has developed a deep-learning algorithm that can detect tumors in mammograms with high accuracy. IBM Watson Health has developed an AI-powered system that can analyze medical images to identify potential health issues such as cancer. Companies such as Paige.AI and PathAI raised significant amounts of funding in the early stages of generative AI. Paige.AI, which uses AI to improve the accuracy of cancer diagnoses, raised $125 million in a Series C funding round in 2020. PathAI, which uses AI to analyze medical images, raised $165 million in a Series C funding round in 2021. Expect massive disruption from technology that can interpret images and irregularities, and label the data accurately. Personalized medicine, drug discovery, repetitive process automation, and imaging are all areas being disrupted by startups and corporations alike.

2. FinTech — Banks bet BIG on AI

Generative AI is also being used in the finance industry to automate tasks such as fraud detection and risk assessment. According to Bloomberg, major banks like JPMorgan, Bank of America, Citi, Goldman Sachs, and Wells Fargo recently banned using chat GPT for business purposes, but have each decided to build their own products leveraging AI. JPMorgan Chase, for instance, has developed an AI system that can detect fraud in real-time by analyzing large amounts of data. Similarly, Goldman Sachs has developed an AI-powered system that can analyze market trends and provide insights to traders. American Express and Bank of America have developed AI-powered systems that understand and answer customer queries with appropriate responses.

Fintech companies and institutions are using generative AI to train models behind know-your-customer (KYC) processes for account opening. Banks and hedge funds are using the tech to generate stress-test scenarios for illiquid financial products to inform proper compliance measures, risk strategies, and to reduce costs. Better understanding risk and improving customer service are just the tip of the iceberg.

3. Automating Human Creativity

In the entertainment industry, Generative AI is being used to create art and music. For example, Sony developed an AI system that can create music by analyzing existing songs and creating new ones based on the same style back in 2016. Similarly, Google has developed an AI-powered system that can create art by analyzing existing paintings and creating new ones based on the same style. How long until musicians and authors are displaced? South Park’s recent episode was largely written by Chat GPT. How long before human creativity is automated?

The Mass Adoption of GPT — Thank you Microsoft.

Microsoft’s Co-Pilot is an innovative AI-powered tool that aims to increase human productivity by automating repetitive tasks and reducing the need for human intervention. Microsoft has an equity stake in Open AI and a Windows OS operating on more than 1.6 billion devices. Similar to their introduction of Teams, Microsoft is uniquely positioned to introduce generative AI technology to hundreds of millions of users overnight; and they’ve done just that with the introduction of their Co-Pilot product. Similar to GitHub’s co-pilot product, Microsoft’s Co-Pilot utilizes machine learning algorithms to learn from human behavior, adapt to different workflows, and is essentially a virtual assistant that helps you complete tasks efficiently. I think their integration has a longggg way to go, but their ability to reach customers points towards how we can think about the mass adoption of this new technology.

One of Co-Pilot’s most significant features is its real-time feedback capabilities. The tool can analyze a user’s work and suggest ways to improve grammar, readability, and visual appeal, among others. This feature is beneficial for workers who need to produce high-quality work in a short amount of time.

Another notable feature of Co-Pilot is its ability to automate tasks such as scheduling, email management, and report creation. This automation enables human workers to focus on more important tasks that require critical thinking and creativity. Co-Pilot’s machine learning algorithms allow the tool to learn from its interactions with human workers and adapt to different workflows and this adaptability means that Co-Pilot can be used in various industries, including healthcare, finance, education, and more.

What’s Next?!

The hype is deserved. This will impact our pace of economic development faster than the internet, and big corporations are best poised to benefit from employing the use of this open-source technology. But who is creating the guard rails? How do we ensure the data sets used are accurate, unbiased, appropriate, and valuable? Who do we trust to do this? Who’s jobs will be eliminated?

“How do we balance the incredible potential of AI with the very real risks it poses to privacy, security, and human autonomy?” — Brad Smith, president of Microsoft

Jobs will be lost just as jobs were lost when manufacturing began to be automated. But new jobs will be created, and people will have the tools to increase productivity and focus on higher-order tasks to create value. I may be an optimist, but I don’t think that the percentages of occupation exposure to NLP correlate with job loss. I think that innovation, ingenuity, and vision will be more important than ever.

The occupations with the highest exposure to large language models, GPT software and sorted by different evaluation methods by human assessment (Human) and GPT-4 assessment (Model). | Image: Eloundou et al.

Note about the use of GPT

This article was produced with editing assisted by GPT3. Original sources were used as inputs along with novel and personal insights.

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