Ibbaka has a short survey open to inform our September 19 webinar. We are asking participants and other interested people some basic questions about generative pricing.

Some interesting patterns are emerging.

Most of the respondents believe that generative AI is transformative. Of course, this is a highly skewed data set. It is for people interested in attending a roundtable on generative AI. It does not represent the general population, or even the general technical population or business population.

Still, it does suggest that among the this group there is a lot of enthusiasm.

We had two open-ended questions in the survey.

“What is an application of generative AI that worries you?”

“What is an application of generative AI that gets you excited?”

Worries

We asked Perplexity to categorize and organize the responses using Open.ai GPT4.o.

1. Security and Privacy Risks

• Data Security: Concerns about data theft, data leaking to the public, and misuse of sensitive data

• Cybercrime: Use of AI for security breaches, phishing schemes, and scamming through impersonation

• Model Corruption: Risks of bad actors corrupting models and injecting false ideas

2. Ethical and Societal Impact

• Bias and Fairness: Bias in models affecting decision-making and pricing, leading to unfair outcomes

• Deepfakes and Misinformation: Creation of fake news, deep fakes, and realistic images that blur the line between real and fake, impacting public opinion and elections

• Propaganda and Manipulation: Use of AI for propaganda and influencing public opinion without awareness

3. Over-Reliance and Misapplication

• Over-Reliance: Dependence on AI for tasks it may not be suited for, leading to potential errors and loss of human oversight

• Misapplication: Using AI in areas where it does not add value or where human oversight is crucial, such as healthcare and military applications

• Generalization Issues: Overgeneralization by models and users, assuming AI can solve specific problems without proper framing

4. Economic and Competitive Concerns

• Market Impact: Flattening of the playing field, making it harder to maintain a unique selling proposition

• Economic Displacement: Concerns about AI replacing human jobs and the dumbing down of work

5. Technological Limitations and Hype

• Model Limitations: Concerns about model collapse, slowing of model evolution, and over-hyping AI capabilities

• Excessive Hype: Distraction from real issues due to over-hype and exaggerated expectations of AI

6. Ethical Use and Governance

• Intent and Supervision: Worries about the intent behind AI use and the lack of supervision and responsibility

• Policy and Regulation: Need for thoughtful alignment, trust, and safety strategies to prevent dangerous outcomes

Excitement

Open.ai GPT4.0 was also used to organize the responses on what gets people excited.

1. Productivity and Efficiency

• Automating routine tasks and eliminating mundane work

• Enhancing productivity in daily tasks and applications

• Summarizing and synthesizing large amounts of information

• Improving usability and functionality of apps

• Saving time in research and data analysis

2. Creativity and Content Generation

• Story generation and idea exploration

• Video and content creation

• Customizing marketing content

• Generating code and visual concepts

• Concept blending and creative task accessibility

3. Data Analysis and Insights

• New ways to gain insights from data

• Forecasting, scenario modeling, and predictive analytics

• Reducing errors in data summarization and analysis

• Connecting and integrating diverse data sets

• Exploring counterfactuals with synthetic data

4. Knowledge Management and Integration

• Capturing and amplifying human knowledge

• Integrating multiple perspectives and points of view

• Enhancing knowledge management practices

• Connecting distributed data and knowledge

5. Collaboration and Communication

• Using AI collaboratively to explore and share ideas

• Facilitating communication across languages and cultures

• Improving collaborative workflows and processes

6. Specific Applications and Innovations

• Robotics and healthcare diagnostics

• Business process augmentation and optimization

• Enhancing sales and marketing workflows

• Developing intellectual tools and exoskeletons

7. Personal and Professional Growth

• Helping with creative thinking and brainstorming

• Providing structure and guidance in tasks

• Enabling more creative and focused work environments

Contact info@ibbaka.com if you would like to see the same lists categorized by other foundation models.

Impact

We also asked, “What impact will generative AI applications have on B2B software over the next 5 years?”

Again, the data is skewed by all the optimists here, but it is still suggestive.

Interesting Generative AI companies

The survey is generating an interesting list of generative AI applications. The list deserves its own analysis and post. The usual suspects are here: ChatGPT, Microsoft Copilot, Google Gemini, and Salesforce Einstein. But there is a very long tail of companies that we want to explore. Here are a handful of these applications.

• Attri - generative AI and foundation models for enterprise

• Covariant - generative AI for automation and robotics

• DeepL - machine translation

• Descript - video generation

• Ivanti Neurons - unified endpoint management

• Monte Carlo - end-to-end data observability

• Powtoon - video and presentation generation

• Phrase - machine translation

• Salesloft - revenue orchestration program

• SnapLogic - drag and drop data integration

• StoryLab - content creation for marketing

• Synthesia - text to video

And many more…

It was nice to see some people calling our Hugging Face (which now has more than 895,000 models available) and LangChain as key applications.

Will Generative AI Continue to Improve?

The capabilities of generative AI applications are generally expected to grow, but opinion was evenly divided on whether that growth will be linear or exponential.

This makes a big difference to innovation and pricing strategy. Linear growth tends to sustain innovation. Incumbent vendors can keep up and layer generative AI into existing applications. Exponential improvements are harder to manage and tend toward disruptive innovation.

How will generative AI applications be priced?

We explore this by asking about pricing metrics. The pricing metric is the unit of consumption for which the buyer pays (the value metric is the unit of consumption by which a user gets value). This will also be the subject of a future post as it needs a lot more analysis, but here are the top level results as of the morning of Sept. 1.

There is a mix of metrics here. The common generative AI metric of tokens comes up a lot (it will be interesting to see the clustering patterns to see what other metrics are used with tokens). Users also come up quite a bit, which is not surprising as applications like ChatGPT, Microsoft Copilot, Google Gemini, and Perplexity were all cited as examples of compelling generative AI applications.

Other metrics that are more associated with vertical applications include process-based and domain-specific pricing. Almost 20% called out managed services, which suggests that part of the market, probably serving enterprises, will provide customized solutions integrating multiple applications and models.

Pricing Methods

How will these applications get priced? Again there is a diversity of approaches with value based pricing leading the way. One topic people want to see covered in the roundtable is “How does one apply value based pricing to generative AI?”

It is nice to see generative pricing getting some recognition (no doubt a sign of the bias in the survey). This is an approach to pricing generative AI applications that is being developed at Ibbaka. You can learn more about generative pricing here.

Book a demo today and discover how our unique B2B SaaS solution can increase sales, improve retention, and drive growth.