GTM Pricing

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?”

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

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.

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.

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.

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.

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.

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.

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