The key to avoiding failure as a company grows is ensuring that business leaders agree from the very start on what its products, ideal customers, and internal processes are. Solidifying these is critical before scaling up, as they clearly define a company’s business model. Failing to do this step is one of the primary reasons some startups fail in the scaling process, as they didn’t have a clear vision of where their company should’ve gone before the unprecedented growth took place.

“If you develop an understanding of the archetypal challenges associated with rapid scaling, you can build in design for scalability,” says Harvard Business Professor School Professor Jeffrey Rayport. “There are decisions you can make early to mitigate risk.”

When asked what those challenges are, Rayport breaks them down into what he calls the “Six S Framework.” Or rather, the six areas founders should focus on when building their venture. They're:

Staff Shared values Structure Speed Scope Series X Here’s a closer look at what each area means and its potential impact on your growing business.

People frequently abuse or misuse financial metrics in order to paint a picture that fits their narrative. Often this is unintentional, but it can be extremely dangerous to the company if not fixed.

When misused or misunderstood, metrics can hide problems that should be surfaced and fixed quickly. Make sure every leader at your organization really understands what the metrics mean and how they should inform decision making.

The same concepts are true for investors.

If you are unfamiliar with “community adjusted EBITDA”, it is the infamous metric created by Adam Neumann (founder at WeWork) to show how WeWork could be a profitable business…

You're probably here because you watched the very impressive and vaguely terrifying demo of OpenAI's new AI model, GPT-4o. It's already rolled out to a lot of ChatGPT users, so let's dig in and see what it can do. (And yes, the capitalization upsets me too.)

What is GPT-4o? GPT-4o is the latest flagship AI model from OpenAI, the company behind ChatGPT, DALL·E, and the whole AI boom we're in the middle of. It's a multimodal model—meaning it can natively handle text, audio, and images—and it offers GPT-4 level performance (or better) at much faster speeds and lower costs. It also marks the first time free ChatGPT users will be able to use a GPT-4 model (they've been working with GPT-3 and GPT-3.5 Turbo up until now.)

GPT-4o vs. GPT-4: What can GPT-4o do? The "o" in GPT-4o stands for "omni." That refers to the fact that, in addition to taking text inputs, it can also natively understand audio and image inputs—and it can reply with any combination of text, images, and audio. The key here is that this is all being done by the one model, rather than multiple separate models that are working together.

Take ChatGPT's previous version of voice mode. You could ask it questions, and it would reply with audio, but it took ages to reply because it used three separate AI models to do it. First, a speech-to-text model converted what you said to text, then GPT-3.5 or GPT-4 would process that text, and then ChatGPT's response would be converted from text to audio and played back. According to OpenAI, the average response time was 2.8 seconds using GPT-3.5 and 5.4 seconds using GPT-4 to reply. A neat demo, but not particularly practical.

But now, because GPT-4o is natively multimodal and is able to handle the audio input, natural language processing, and audio output itself, ChatGPT is able to reply in an average of 0.32 seconds—and you can really feel the speed. Even text and image queries are noticeably faster.

If this speed was coming at the cost of performance, that would be one thing—but OpenAI claims GPT-4o matches GPT-4 on English text and code benchmarks, while surpassing it on non-English language, vision, and audio benchmarks. In particular, the new tokenizer—which converts text into small chunks that the AI can understand mathematically—is much more efficient for languages like Tamil, Hindi, Arabic, and Vietnamese, allowing for more complex prompts and better translation between languages.

It's also possible to interrupt the model when it's speaking to you, though that feature is rolling out over the coming weeks (I still had to tap to interrupt when I was testing it). OpenAI also says that GPT-4o has a better capacity to speak with emotion, as well as understand your emotional state from your tone of voice.

The situation is similar with image input. GPT-4o is noticeably quicker to respond to questions about images as well as process things in them like handwriting. This quick context-switching makes ChatGPT feel like a much more useful real-world tool.

Asking GPT-4o to search the internet and then create an image And this is all on top of GPT-4's existing features. You can still use it for brainstorming, summarizing, data analysis, market research, cold outreach—the list goes on.

How does GPT-4o work?

First things first, don’t get overwhelmed by all those revenue, growth and acqusitions screenshots and posts on the Internet. All of that content is nothing but engagement farming.

Everyone has their own journey and it takes a lot of time and effort to build something worthwhile. If you are just getting started -

Build something small and ship

Don’t spend too much money on these projects

Be very clear of what you want to achieve

Build something that excites you

But but but what should you exactly build? There is a lot that you can build on the Internet. SaaS maybe sexy, but there’s a lot more that you can build on the Internet and make a shit ton of money.

Listing down everything that you can build online 👇🏻

Less than 24 hours after posting his first thread, PJ’s following doubled. By the end of the day, it tripled. He remembers being more baffled than elated, given he hadn’t posted anything new:

“My expectations up until that moment were: I just have to create good enough work to be seen, and I'm not there yet.

But at that moment, I realized that it wasn't necessarily about the quality of the work; it was the packaging of the work.”

By the end of 2022, PJ joined LinkedIn because he kept hearing from friends who’d seen his work reposted on the platform without credit. When he eventually posted a carousel version of the same 10-piece thread that had done so well on Twitter, he blew up. It’s the single most-engaged LinkedIn posts I’ve ever seen:

That post alone drove PJ’s LinkedIn following from around 12,000 (his one-off illustration posts were already popular) to 15,000, then around 65,000 by the time the post had been up for a week. He continues to post daily, and every post attracts hundreds of likes. In the two weeks between interviewing him and writing this sentence, his following has gone from 98.8k to 102.4k.

Welcome emails can arguably be one of the most powerful tools for setting an incredible first impression for pre-email customers. Nevertheless, the process of creating a persuasive welcome email is not always smooth.

Good welcome email examples can serve as the inspiration to make an amazing first impression.

Having so long been in the role of a copywriter, I always find myself in search of good email examples. In this blog, I‘ve pulled together the most inspiring welcome emails that I’ve come across that will help you create your own.

What is a welcome email?

A welcome email is the first email you receive after signing up, subscribing, or submitting your email to an online store. It’s important because it's the first impression a company makes with a new customer, blog subscriber, or newsletter subscriber via email. Welcome emails can deliver videos, special offers, a sign-up form, or just a friendly hello to establish a relationship with a new contact.