đź’€ Zombie Watchlist $TWLO: Not Yet Profitable, And Already Obsolete


The Threat of Next-Generation Language Models

Language models in AI are essentially a type of transformer models capable of learning and improving from a base data set over time to enable human-like speech and understanding output, like “translating text and speech in near real-time, [and/or] making online recommendations”. A basic iteration of transformer models used commonly in our daily life settings include Google’s “BERT” (Bidirectional Encoder Representations from Transformers), which is deployed in Google Search (GOOG / GOOGL) to help the search engine better understand queries. Transformer models like BERT can also perform “sentiment analysis” by combing through data like emails to gauge opinion and emotion – a NLP feature largely found across customer relationship management tools like Microsoft’s “Viva Sales” embedded in Dynamics 365 and Salesforce’s (CRM) “Social Studio” offered as part of its Marketing Cloud solutions. Twilio’s messaging solutions, like the APIs used by vendors to enable customized customer service chatbots, is also a transformer that can learn, understand, and process queries.

But the capability of language models have moved on by large margins from what we are most familiar with today in the day-to-day setting. OpenAI’s ChatGPT bot may be talk of the town in recent weeks, but its underlying language model, GPT-3, is not the only SOTA AI algorithm of its kind on the market today. Remember a few months ago when Google faced the whole debacle on whether its chatbot is sentient? Yea, that was another language model, known as LaMDA (Language Model of Dialogue Applications).

And ChatGPT is impressive. Consisting of 175 billion parameters, the underlying GPT-3 language model on which OpenAI’s latest chatbot is built on is more than 100x better-performing than its predecessor, “GPT-2”, and 10x better-performing than Microsoft’s (MSFT) “Turing NLG” language model introduced in 2020. And over the past week-and-a-half, we have learned that the newest chatbot in town can do a lot more than spitting out search results like Google Search or FAQ-based responses like Twilio API-enabled automated customer service reps.

1 Like

$TWLO daily, weekly, and monthly charts:

Organic revenue rose 32%, while Zipwhip, an SMS marketing platform it recently acquired, contributed $34.8 million. On the bottom line, Twilio’s adjusted loss per share of $0.27 was down significantly from a per-share profit of $0.01, but it was better than estimates at a per-share loss of $0.36.

While those numbers were better than expected, Twilio’s fourth-quarter guidance set off alarm bells as the company said revenue growth would slow to just 18% to 19%, to $1 billion at the midpoint, which was worse than the consensus at $1.07 billion. The company blamed macro headwinds for the slowdown in growth. The guidance cut comes after the company said it was laying off 11% of its staff in September, as part of a push to profitability. For the fourth quarter, it sees an adjusted loss per share of $0.06 to $0.11.

Twilio was greeted by a chorus of downgrades in the aftermath as the story around the stock seems to be changing from that of top-line growth to bottom-line profits, which have yet to materialize.


Wall Street Journal today adds Carvana to the zombie list. Big debts. Declining car prices. Restructuring likely.