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Netflix Becomes Enemy It Swore To Destroy 👹

Netflix evolves with the times, ChatGPT gets an update, and more...

Today’s Menu 🥠

  • Netflix Is Built Different
  • ChatGPT’s Laziness Is Unacceptable
  • 5 Trending AI-Powered Tools
  • I’m Blue, If I Was Green…

 

Fast Snacks 🥡

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Nextflix Is Different Now

The year 2022 marked a turning point for Netflix.

Subscribers began to dwindle, and the once-unstoppable growth trajectory faltered. In a world where change is the only constant, Netflix found itself at a crossroads: evolve or face the consequences.

Once upon a time, Netflix was the titan of the streaming world, a synonym for cozy nights and binge-watching extravaganzas.

Its ascent was meteoric, its ambition limitless.

For about a decade, this digital Goliath seemed invincible, with a subscriber base that only soared higher and a market cap peaking at over $300 billion in 2021. Its content was king, and its reach, seemingly boundless.

In a dramatic pivot, Netflix, under the guidance of its co-founder Reed Hastings, did what was once unthinkable. It introduced a cheaper, ad-supported tier.

This move, initially met with skepticism, was a gamble to attract a new demographic while tapping into the lucrative world of advertising. It was a strategy that Hastings had long resisted, but the changing tides left little room for nostalgia.

The new ad-supported tier had a rocky start, but persistence paid off. Within six months, it boasted 5 million subscribers. Forty percent of new subscribers were opting for this budget-friendly option, proving its strategic worth.

Netflix didn’t stop there; it enhanced the plan with features like 1080p video and dual stream capability, further sweetening the deal.

Yet, the pursuit of rejuvenation led Netflix down paths previously untraveled. The company, once known for its lenient stance on password sharing, took a hard turn. Cracking down on this practice was a controversial but calculated move to boost subscriptions. Despite grumbles over frequent price hikes, this strategy seemed to pay dividends, with more signups and increased revenue.

The changes kept coming.

Price hikes became more frequent, and the cheapest ad-free plan was phased out, nudging subscribers towards either the ad-supported tier or the pricier standard option. This counterintuitive strategy was anchored in a newfound truth: ads were not just an addition to Netflix’s revenue model; they were becoming its cornerstone.

Finally, Netflix ventured into new territory with its $5 billion deal for WWE Monday Night Raw. This acquisition was more than just content; it was a strategic play to capture a younger demographic and to diversify its revenue streams.

For Netflix, WWE represented more than sports; it was “sports entertainment,” a year-round spectacle that promised high engagement and consistent viewership.

The streaming landscape has changed, and profitability, not just subscriber count, is now the new benchmark of success.
 

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ChatGPT’s Latest Updates Cures Its ‘Laziness’

Recently, the OpenAI team unveiled a significant update to its groundbreaking AI model, GPT-4, aptly named GPT-4 Turbo.

The announcement came in a rather understated fashion – a blog post, without much fanfare but loaded with potential.

OpenAI explained that GPT-4 Turbo was designed to tackle a specific issue: the model’s occasional reluctance or “laziness” in completing tasks, particularly in areas like code generation. The promise was one of greater efficiency and reliability, a stride towards an AI that was more responsive and thorough.

And GPT-4 Turbo really was a different beast. Trained on information as recent as April 2023, it was available only in a preview.

It’s not surprising then that more than 70 percent of GPT-4 API users swiftly transitioned to GPT-4 Turbo, eager to harness its updated knowledge base and improved functionality.

Parallel to the development of GPT-4 Turbo, OpenAI introduced another groundbreaking innovation: embeddings.

Defined as a sequence of numbers representing the concepts within content like natural language or code, embeddings are designed to enhance applications using retrieval-augmented generation.

To cater to a range of needs, OpenAI launched two models: text-embedding-3-small and its more powerful counterpart, text-embedding-3-large. These models promised to further refine the AI experience, providing more nuanced and context-aware interactions.

 

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I’M BLUE, IF I WAS GREEN…


 

Correct Answer ✔️


 

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Netflix Becomes Enemy It Swore To Destroy