ChatGPT: Anticipating Business Risks | Strategies for Success

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ChatGPT: Anticipating Business Risks | Strategies for Success

Problem 1: End of MOAT

Performance & Model Quality

Initially, OpenAI’s MOAT was its Performance & Model Quality. Now, other competitors like Google and Microsoft, etc., have not only better or equivalent Performance & Model Quality as that of OpenAI, but also an Ecosystem.

OpenAI’s MOAT is Ended!

Evident from the

LLM leaderboard⁠

below -

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Recently, in an internal document by Google, they claimed -

“Google has no MOAT, and neither does OpenAI”⁠

. This is what they had to say about OpenAI -

⁠

Therefore, I say - OpenAI has reached the limitations of Model performance for now, it’s MOAT is ended on performance in consumer markets. The focus now needs to shift from solely enhancing model performance to exploring diverse use cases, especially in enterprise settings. The era of relying solely on performance as a marketing edge is slowly fading.

Extension and Integration:

OpenAI also needs to prioritize expanding the utility of ChatGPT by integrating it with other tools and platforms. Examples include Gemani's extensions for YouTube and email, as well as Copilot has its own extensions for more use cases.

Free users lack avenues to explore additional GPTs, and the weight of the premium plan is heavily emphasized without much exploration for non-paying users.

Creating an Ecosytem:

Another area where OpenAI falls short is in providing a robust ecosystem akin to those offered by tech giants like Google, Apple, and Microsoft. These competitors seamlessly integrate their APIs across various platforms, amplifying their reach and utility.

In summary, while ChatGPT’s performance and model quality can no longer be improved, the next strategic shift necessitates towards integration, extension, and tailored B2B offerings to maintain its relevance and appeal in the market.

Solution 1: Lowering API Pricing

Reducing the prices of ChatGPT's APIs can help to get our MOAT back.

However, we're going to pursue this solution because it could lead to a downward spiral of pricing competition among competitors, which may not be financially viable in the long run.

Competing solely on pricing could lead to a race to the bottom where profitability becomes unsustainable.

Given our current focus on improving monetization, lowering prices doesn't align with our objectives.

Therefore, although it's an option, it's not the path that we must take.

Solution 2: Developing an Independent Ecosystem

One of the solutions for OpenAI can be to establish its own ecosystem of devices.

While this strategy holds promise, its execution is highly challenging.

Although this could be a component of our long-term vision, immediate implementation is impractical.

Moreover, success in this endeavour is contingent upon external factors beyond our control (Market Adoption, Technological Trends, market demand, consumer spending power, and investment trends)

Therefore, while building an independent ecosystem can remain a part of our overarching product strategy, it's not a recommendation for immediate action.

In terms of criteria, the Financial Impact of this solution will be great and will also help to bring OpenAI back in the race (and possibly leading) with its competitors. However, its execution is pretty long-term.

Solution 3: Shifting Focus to B2B offerings

The dissolution of our B2C MOAT necessitates a pivot towards B2B offerings.

Businesses represent a segment willing to invest, and we're already developing products tailored to their needs.

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This strategic shift will allow us to mitigate the market and revenue lost in B2C.

Enhancing Enterprise Appeal: Prioritizing the development of B2B tools and services is essential for making our Team (Enterprise plan) more appealing to businesses.

Custom Tools: We can enhance our repertoire of custom tools for businesses, such as "Data Analyst" GPT by ChatGPT.

Add a B2B Corner: Integrating more B2B tools and potentially establishing a dedicated B2B section within the GPT store can augment our offerings.

This emphasis on B2B tools and offerings aims to make the Enterprise plan more attractive to businesses, thereby expanding OpenAI's market reach.

At the end of the day, OpenAI need to make Team (Enterprise plan) more attractive as a business, so they need to work on more B2B tools and offerings. This can include custom GPTs, specialized tools, or value-added services, aligning with our overarching business objectives.

Solution 4: Enhancing Playground - Design Improvements

The v1 Playgrounds had been a great marker of success of the OpenAI ecosystem allowing users to interact with different models in a free-play mode. The Assistants playground is a successor to this and is a key feature in the current OpenAI ecosystem that lets you build and interact with custom AI assistants. These assistants are like chatbots, but with the power of OpenAI's language models and ability to use custom tools with them opening up new grounds for experimentation and innovation.

The cool thing about Playground is that it's super handy for quickly putting together your MVP using GPT and testing it out. All you need is just a frontend, and you're good to go, Assistants API takes care of the rest.

To get MOAT OpenAI is building various features like Assistants API and the Custom GPT store for B2B & B2C respectively.

Assistants have great potential because they help users eliminate at-least 2 other layers of development (Vector Databases and Embedding) and have a very ambitious goal of making backends irrelevant in the age of AI.

Many cheap API models are available in market that provide you similar quality at 0.5-10X less cost however assistants help OpenAI defend its moat of an AI provider by enabling users to create an AI agent in a matter of minutes.

We need improve the experience of the Assistants playground by adding “starter kits” that can be used by users to understand how our Assistant API works, this enables a friction free demo experience for our B2B users who will later become paid users of our assistant API.

Marking: High, Medium, Low (Possibility)

Prioritization