When asked to draw a random number between 1 and 10 and any color, most people choose the number seven and the color blue. Some potential explanations is that 7 is a lucky number in many cultures and blue is pleasant (or at least not unpleasant or unlucky). See also:
I want to be able to use generative AI in spreadsheets to solve unique problems. I want to call OpenAI from a cell that passes in a prompt and a value from a column then returns an answer I can easily parse. Thankfully, you can do this in Google Sheets and Apps Script! First, create the script using App Script. In your spreadsheet go to Extensions -> Apps Script then add the script. Finally, call the App Script from a cell like you would any formula (Based on this thread in the OpenAI forum) See also:function AI(prompt, value) {
// Replace YOUR_API_KEY with your actual OpenAI API key
const apiKey = 'YOUR_KEY';
const apiURL = 'https://api.openai.com/v1/chat/completions';
const payload = JSON.stringify({
"model": "gpt-4-turbo",
"frequency_penalty": 0,
"presence_penalty": 0,
"temperature": 0,
"messages": [
{"role": "system", "content": `${prompt}`},
{"role": "user", "content": `${value}`}
]
});
const options = {
"method": "post",
"contentType": "application/json",
"payload": payload,
"headers": {
"Authorization": "Bearer " + apiKey
},
"muteHttpExceptions": false
};
try {
const response = UrlFetchApp.fetch(apiURL, options);
const json = JSON.parse(response.getContentText());
const resultText = json.choices[0].message.content;
return resultText;
} catch (e) {
Logger.log(e.toString());
}
}
=AI(PROMPT, COLUMN).
When selling a B2B SaaS product to a wide range of company sizes (e.g. SMB, mid-market, enterprise), you generally want smaller companies to pay less and larger companies to pay more. This makes pricing difficult—make it too one-size-fits-all and you can accidentally price out the smaller customers, make it too a la carte, and all customers will find your pricing confusing. What are some pricing techniques you can use when you have a wide range of business sizes to sell to? Pricing tiers using caps If you design pricing plans around the expected size of customer, you can make it clear who should be buying which plan and make a natural upgrade path. One way to do this is to include caps around one of the variable like the number of employees. For example, plan A is for up to 50 employees, plan B up to 500 employees, and so on. Simple and advanced Providing versions of the same feature can be used to appeal to different size customers and signal to the bigger companies which plans they should be on. For example, you might provide hosting on the basic plan for SMBs but faster CDN for mid-market companies and the ability to set custom headers for enterprises. Add-ons Pricing with tiered plans (which is most common for SaaS) comes with the downside that their may be a few critical features that a customer really needs but is stuck between plans. Using add-ons allows customers to pull forward from higher plans to get what they need. Have 51 employees and the cap is 50? Provide an upsell for each additional employee after the cap. Really need a CDN because you are an e-commerce shop? Purchase the CDN add-on for the basic plan. See also:
Selling something is already difficult enough but when you have to sell the problem first, it’s even hRder. That’s because convincing someone about a problem they didn’t know they have is like trying to push the bolder uphill. Sure it’s possible, it just takes a lot more effort. Compare that to nothing sales—merely by showing up, you’ll make a sale 30% of the time. All things being equal, you want the market that looks like that. Educating the market is expensive. If you have to convince someone of the problem, your best bet is to help shape the category altogether or hijack part of an existing one where customers are underserved. See also:
A lot of startups have a business model which roughly equates to: put a dollar in, get two dollars out. This “magic vending machine” is obviously unsustainable but easy to fool oneself into thinking you have excellent product market fit. Building a magic vending machine happens in straightforward ways—using venture capital money to subsidize the prices of a commodity to gain market share (Uber, Lyft, DoorDash). Building a magic vending machine happens in accidental ways—building a product that shifts cost from customers to operations of the business and not charging enough for the service. See also:
Global crises happening all at once re-inforce one another making the effects larger than any individual crisis alone. This polycrisis (originally coined by Edgar Morin), is an entanglement of events like pandemic, war, climate change, and inflation. For example, pandemic leads to inflation and greater political polarization giving rise to far-right movements and less action to address climate change, and so on. See also:
In an episode of Boy Meets World, Shawn is in a competition of who can wait longest on a billboard suspended high up in the air in order to win tickets to the Super Bowl. He stays there until the only two people left on a cold night are him and an Eskimo eating ice cream. When Corey tries to talk him down (literally), Shawn tells him that he has to stay. All his life there was always something in his way, preventing him from what he wanted to achieve. Shawn finally gives up on the contest, but goes to San Diego on his own, eventually finding a way into the Super Bowl. While the lesson of the episode was that Corey can’t solve all of Shawn’s problems for him, I remember it differently—there will always be someone better than you no matter how hard you try, but that is not a reason to give up. There is more than one path.
The other day I noticed a tweet from Justin Duke which outlined a plan to get his company’s codebase ready for Devin—a programming focused generative AI product. While many are skeptical about AI taking over coding tasks, progress happening quickly and it seems likely that these tools will help software engineers, though maybe not replace the job outright). If we think AI can positively impact many domains, the question becomes, how can we position ourselves today to take advantage of it in the future? Writing more is almost certainly one way. Large language models operate on content. That content is mostly text. If you want to have a future co-pilot that can help you build useful and unique knowledge (and potentially avoid knowledge collapse), it’s reasonable to believe you need to be writing a heck of a lot so that AI can draw from what you already know. For example, in a video of Reid Hoffman interviewing himself as an AI, the AI was trained on every book Reid has written and every talk he’s given. When it comes to code, writing comments explaining what the code is doing and why that’s important seems like it would help generative AI make better contributions that need less supervision. After all, reading someone else’s code is hard enough! I would love to read some research on performance improvements not just on writing code but about comprehension of business logic and how documentation helps or hurts. Running a business, building a culture where writing is thinking and the primary way teams work asynchronously will amplify every future investment in AI. When Notion rolled out their AI-powered search, a new hire at Mosey started asking it questions about projects, tools, processes, and terms he didn’t understand—the responses helped him get up to speed quickly. FAQs don’t work, but being able to respond to almost any question however it’s formulated sure does!
Net magic number is a measurement of go-to-market (GTM) efficiency for SaaS businesses. A magic number less than 1.0 indicates the business will lose money on each customer. Top SaaS businesses making less than $25MM in revenue per year have a magic number of 1.7 according to benchmarks from Iconiq Capital as in their business returns 1.7x for every sales and marketing dollar they spent to acquire customers. Net magic number is calculated by taking the current quarter’s revenue less previous quarter’s revenue times 4 divided by the previous quarter’s sales and marketing spend. By looking at the previous quarter, net magic number accounts for the average sales cycle to complete which makes it a better measurement for enterprise SaaS that has a sales motion. See also:
Most managers, at best, are a form of benign neglect. Continual attention (micro management) usually results in the opposite of what a manager hopes for—less motivation from employees, learned helplessness, and worse performance. Benign neglect leaves room for employees to have agency to do their work but it’s kind of like the placebo effect, maybe most managers are better off doing nothing at all. If that’s good, that’s an awfully low bar. When I first heard about this, I agreed it’s not such a bad outcome but surely it can be better. I like to ask new hires who the best manager they’ve ever had in their career. No one described benign neglect. See also:
A survey from Deloitte found that 43% of companies see compliance as their greatest challenge and several key trends. They found that having an overall data management approach and integrated systems are needed to address global tax compliance. They found that complying with constantly changing laws and regulations is a top priority, more than cost efficiency. They also found that 74% of companies are using outsourcing or managed services as their main approach strategy for tax compliance.
When companies run surveys and industry analysis that they share with the media, they’re gaining exposure for themeselves. While data may be contained within a blog post or report, the unit of value is the data point that a journalist can extract into their article (with a mention of the source).
The “the first AI software engineer” Devin, from Cognition Labs, was found to be fixing bugs of it’s own doing, solving problems in a roundabout way, and taking a long time, in a debunking video by a human software engineer. As someone who codes, I was surprised to hear so many people saying Devin AI was a breakthrough. I’ve used generative AI tools quite extensively now and have a good feel for their current limitations. In real-life scenarios, they’re best at augmenting a knowledgeable person, but they always need to be checked for many of the reasons found in the video. I would love if we could get to the point where these systems can be unsupervised, but it appears we will need to wait a little bit longer. See also:
What if the entire services industry merges with SaaS when it becomes possible to deliver a service with artificial intelligence? SaaS is really about automation and digitizing workflows in a way that is more efficient (faster, easier, cheaper). AI is about problem solving and customization that responds to exactly what the user is asking for. Read The AI Workforce is Here: The Rise of a New Labor Market from Nfx. See also:
I try to be careful about what I read in the morning as that has a way of setting the tone for the day. I stick to things that are informative or entertaining but don’t need a lot of energy to get into like a long-form essay.
Henrietta Lacks passed away from cervical cancer in 1951, but her cells have been continuously used in medical tests due to their ability to replicate quickly. She might be the first immortal women as her lineage of cells have been used for the last 70 years. Her cells were extracted without her permission and given away freely for use in medical research. See also:
A products is not just collection of features, but a promise to solve a problem or enhance the customer’s life in some material way. Faithfully delivering on a promise makes for a great product. Failing to deliver on a promise makes for a flimsy product. A good product can be made bad by promising the wrong thing. Packaging and pricing can lead to failing to live up to customer expectations customer in the same way that poor functionality can lead to broken promises. I like to keep this in mind because it avoids overcomplicating product development. Don’t make a promise you can’t keep and don’t break your promises! See also:
The energy spent making a decision should be proportional to the consequences. The larger the consequences, the more energy should go into making the decision. The smaller the consequences, the less energy should go into them. While this is akin to Amazon’s type 1, type 2 decisions (trap door vs two-way door), proportional decision making describes a continuum which better matches the variety of decisions we see in reality (or at least I think so). What does this mean? We should be quick to make decisions that are inconsequential to avoid bike shedding. We should be rigorous and scruntinize decisions that deeply effect our interests. Mixing up the two would clearly be bad. From Dharmesh Shah on Lenny’s Podcast. See also:
While well intentioned, a utilitarian approach to maximizing expected value of the greater good (or minimizing suffering) has unintended side effects. Actions taken in service of effective altruism can cause harm (aid in the form of misquito nets to reduce malaria leading to overfishing, financial aid bostering oppressive governments, etc.). Deciding what is best for humanity (combating threat of AGI, colonization of space) becomes increasingly more abstract and longtermism leads to justificationism. Read The Deaths of Effective Altruism from Wired. See also:
Knowledge collapse is the paradox where increasing access to certain types of knowledge actually harms understanding. If you visualize public knowledge as a collection of viewpoints that fit a normal distribution, the “collapse” is when the head and the tail drop off and the center is amplified. A recent paper found that a “discount on AI-generated content generates public beliefs 2.3 times further from the truth.” While the paper centers on LLMs, there could be many explanations for the phenomenon. See also:
Business strategy should concisely answer who the most import customers are, how to attract them, and what the entire company must work towards to win that market. From What makes a strategy great: Great strategies accomplish this with the following characteristics: Putting this into practice is difficult so I find that it’s useful to write a draft and then ask the list above as questions: Is the strategy simple? People don’t expect much from simple ideas. Anything too complicated is a sign that a lot of things have to go right in order for it to work (execution risk). Elegance is a heuristic guide to truth. What difficult truths does it reveal? Confronting the most challenging parts of the market, the customer persona, and the state of the world is important—at some point contact with reality must be made—better to be considered now than when putting the strategy into practice. Is it decisive? If it sounds too wishy-washy it probably is. When will you decide if not now? Does it compound in an obvious way? Look for elements of the strategy that build on itself and compound for long periods of time keeping in mind most of the benefits occur at the end. There also should be room to make many bets that have out-sized rewards with small effort. Is it long-term and ambitious? Look for meaningful changes in the world if you are wildly successful and what must be true in order for that to happen. Build on that to take it one step further.
The big orange cones with steam coming out of them in Manhattan are from the leaking network of steam pipes that heat commercial buildings. The 142 year old system of pipes sometimes breaks and the steam shoots up from underground. The stacks help direct the steam up rather than fogging the streets and sidewalks. Read Orange Steam Funnels Are a New York Symbol. What Are They For? by the NY Times. See also:
A theory of how plants cooperate comes from research of how fungus connects tree roots together into common mycorrhizal networks (CMN). Scientists have found evidence that important nutrients are shared over CMN and can connect multiple species of plants together. Some researches take this theory further. One idea is that a “Mother tree” supports it’s seedling kin by sending resources to them preferentially. Another idea is that plants cooperate, which conflicts with evolutionary biology which centers on competition. A recent article in Nature Vol 627 discussed a recent review of the research and calls into question several of these ideas, most notably doubting the explanation of a “mother tree”. Evidence for the wood-wide-web are not as clear as the headlines may make them appear. See also:
In a two party system you never want both party A and party B buffering at the same time because it creates artificial delays in the system. As an example, let’s take a common setup between two teams and a ticketing workflow. The ops team doesn’t want to bother the engineering team so they wait until they think there is a lull before reporting an issue. The eng team triages requests coming in into tickets and then the team prioritizes tickets before assigning and completing them. The two teams are both buffering and the time it takes to resolve an issue is now the sum of: With both sides buffering, an unpredictable amount of time is introduced into the system causing delays. In this example, it would be much more efficient to report issues immediately. Now imagine several teams in a complicated system where tickets are passed and prioritized between them. Each stage is buffered, adding more time to the system, and decreasing throughput. (This is also why I don’t think teams should introduce ticketing too early because it’s too easy to double buffer!) See also: