A decade earlier, building an AI startup was incredibly hard.

I founded my first AI startup, Qurious, right out of college. I had zero work experience, no successful exit under my belt, and no PhD. Looking back, I must’ve been crazy.

Think about all the challenges a non-AI startup already faces:

• achieving product/market fit

• delivering value to customers

• sales and marketing distribution

• customer support

• hiring and retaining talent

With an AI startup, formidable challenges were stacked on top of all those:

• curate and preprocess large data sets

• achieve high accuracy in real-world production environments

• train multiple ML models for varied tasks

• continuous QA and finetuning to improve accuracy, handle edge cases, and avoid model drift

• manage all the infrastructure and cloud costs associated with running multiple large models

• raise funding to finance this herculean effort

Today, Gen AI technologies have simplified building AI startups, making them accessible to all entrepreneurs, without having to raise millions in funding just to get started.

Still, AI startups are not easy. Startups remain a challenging, although rewarding, endeavor. But now, you don’t have a whopping list of formidable challenges stacked on top of the fundamental startup challenges.

Old Way vs. New Way

Here’s the Old Way of building an AI startup:

• Hire an expensive AI/ML team, including scientists to train models from scratch and engineers to manage the deployment pipeline.

• Curate, clean, and preprocess large datasets, required for training models from scratch and continuous finetuning

• Train and deploy models, managing all the cloud infrastructure while striving to achieve (often unrealistic) customer expectations of high accuracy in real-world environments

• Train multiple models for distinct tasks, for example, sentiment analysis and summarization.

Here’s the New Way, much faster and leaner:

• Integrate with a Gen AI API, like OpenAI, minimal coding required

• Write prompts in plain English, no coding required

• Finetune with small datasets to improve accuracy, rather than curating and preprocessing large datasets for model training from scratch

• A single LLM prompt can handle multiple varied tasks, which would’ve previously required training multiple distinct ML models

Example

Here's an example showcasing the power of ChatGPT to perform 4 complex NLP tasks, analyzing my original LinkedIn post and its comment thread:

1. Text Classification with custom classes: classify comment as either "sarcastic", "agree", "disagree", "funny", "didntgetit", or "dontcare".

2. Text Summarization: summarize thepost's comments

3. Sentiment Analysis: describe the overall sentiment of comments

4. Text Generation: refute the post's summarization


I fed this prompt into ChatGPT along with the LinkedIn post and comments:

Here’s ChatGPT completing the text classification task:

Here’s ChatGPT completing the summarization and sentiment analysis tasks: