“We should learn from history, but not dwell on it. We should measure our progress, but remain adaptable. We should consider long-term implications, but not lose sight of the present.”

My good friend Lauren Anderson mentioned that I should have provided examples, so here we go!

We should learn from history, but not dwell on it.

Proposed in 1926, Murray’s Law described how natural vascular structures efficiently transport fluids with minimal energy expenditure. This law worked well for biological systems like blood vessels and plant veins. However, as synthetic materials with diverse shapes emerged, the traditional theory struggled to accommodate these new structures accurately.

Rather than dwelling on the limitations of the original Murray’s Law, researchers at Cambridge recognized the need to learn from history and build upon the existing knowledge. They proposed a “Universal Murray’s Law” that bridges the gap between biological vessels and artificial materials, expanding the applicability of the theory to a broader range of systems and flow types.

By not dwelling on the constraints of the past and instead using it as a foundation for further development, the researchers were able to create a more comprehensive and versatile theory that can benefit energy and environmental applications.

We should measure our progress, but remain adaptable.

Researchers recently found a specific neural signal that seemed to relate to how confident people were in their decisions. Previously it was theorized that confidence was linked to other factors like making accurate decision s in the past, how fast the decision was made, or even the quality of evidence. But this finding implies that simply looking at overt behavior or performance metrics does not fully capture the underlying cognitive processes. If true, it would seem that confidence emerges from a more complex set of computations in the brain.
In a sense, the researchers had to go beyond simply “measuring progress” via standard decision-making outputs like correct/incorrect responses or reaction times. They dug deeper to uncover a subtler neural signature that gave insight into the participants’ subjective sense of confidence. This reminds us that relying solely on surface-level metrics can miss important nuances.

We should consider long-term implications, but not lose sight of the present

The long term implications of your daily cup of tea are a giant pile of used tea bags in some landfill, somewhere, that is directly your fault. But using loose leaf tea can be a hassle, especially if you don’t want to get into the nitty gritty details of the whole thing and just want your hot drink in the morning.

But recent research has shown that biodegradable tea bags may not always be … biodegradable. And in some cases, they could kill earthworms, causing even more harm.

So, if the decision to use biodegradable tea bags was based on the long term implications, you now have additional information saying that it may not be a valid solution. And now, in this present, you must come up with a new solution.

And I think that about covers my thoughts on the idea of Stumbling Progress

Take the workplace, for instance. Despite decades of evidence about the perils of excessive meetings, we still hold conferences when there's critical work to be done. It's almost as if we've forgotten the CIA's 1944 Simple Sabotage Field Manual, which advised saboteurs to disrupt productivity through unnecessary meetings.

Then there's our tendency to prioritize optics over substance. Look at the ongoing issue of hiring discrimination. Companies have hired diversity officers, provided training, and diversified their boards – yet these measures haven't significantly reduced discrimination against Black applicants.

And what about our myopic view of the future? We obsess over market share, even though research shows it has little impact on profitability, especially in digitalized industries. This short-term thinking leads to dark design patterns that trap users within platforms, sacrificing their interests for the sake of growth.

So, what can we do? Should we strive to learn from the past, meticulously measure our actions, and broaden our perspectives? Or should we accept that humans are fundamentally flawed and prone to repetitive mistakes

Perhaps the answer lies in a balanced approach.

We should learn from history, but not dwell on it. We should measure our progress, but remain adaptable. We should consider long-term implications, but not lose sight of the present.

And above all, we must forgive ourselves and others when we inevitably stumble, for that is the nature of our imperfect humanity.

These are the kinds of medical breakthroughs and marvels that exist today. That’s right, currently exist. Not in the future. Not in some cool sci-fi movie. In a lab, with a bunch of grad students, possibly while they panic over grant proposals and why the coffee machine is empty…again…STEVE?!?

Yet, as John Green poignantly reminds us in this video, the gap between cutting edge medicine and general adoption and availability is…business.

Sure, sure, you could say, “We just have to get rid of the corporations!” and assume that would ‘solve’ the problems. But that is a dumb thing to say…and you aren't dumb.

Nothing in the history of our species has ever been accomplished by advocating the elimination of a Thing without having it’s replacement available. So until we have a replacement for the ability of corporations to coordinate and incentivize the efforts of thousands of people, we need to accept them the same way we accepted fossil fuels as an energy source until renewable tech got cheap enough.

Without corporations, those lollipops and implantable batteries will stay in the hands of grad students and not in the hands of medical professionals and consumers. Without corporations, the cost of each unit will be unreachable, but with them, the benefits of scale can be realized.

What we can do is put laws and regulations in place to limit the extent of any systemic failure caused by said corporations. And other laws and regulations to incentivize the greater good.

The real question is how far we can push the idea that systemic failures can be addressed by regulations and laws.

For example, the reality is that people experiencing homelessness face a significantly higher risk of dementia at younger ages compared to the general population. Moreover, socioeconomic inequalities continue to widen the gap in the prevalence of multiple long-term conditions, a trend projected to worsen by 2049. On a global scale, we find that socio-economic differences had a massive impact on how deadly the SARS-CoV2 pandemic was/is/will continue to be.

So, in the same way that we all learned that making vaccines free kept the spread of SARS-CoV-2 down, addressing problems that affect individuals provide outsized benefits to society as a whole.

Which suggests, for health reasons, we would need to figure out how we house everyone. After all, it’s cheaper to house the homeless than to provide emergency healthcare for the acute symptoms of the chronic diseases that arise as a direct result of homelessness.

Too much of a leap? What about free internet for all? Because even that has direct impacts on healthcare.

Okay, okay, let’s try it from another direction. How about mandated bacterial infections? I mean, if you agree with the research that shows a link between gut bacteria and the risk of hospitalization for infections. Which means, if we insist that everyone have measurable levels of that gut bacteria, then we reduce the number of people who get hospitalized.

In either case, the real question isn’t “what new marvels in medicine will solve these problems for us?” but “what new forms of socio-economic changes must we have in place in order to make use of these marvels?”

And the answer for that question won’t be found in a lab, but in the public square and the marketplace of ideas. And it won’t be found by individuals describing solutions but by groups reaching consensus. It won’t be easy, especially as opinions continue to be held to the same standard as facts.

But if we can solve that little problem, it’ll be like taking a cancer-detecting lollipop from a baby.

When I started writing down the BizMech Ideas, the goal was to clear them out of my to-do list. The intention was to free up any mental resources I’d been dedicating to the attic of ideas.

And after sharing five of them, it feels like I just reorganized my bedroom and now I have all this free space in my head.

But, with all that space, a flood of new ideas are rushing in and I’d like to spend more time working on them. So, here are the last two BizMech Ideas. These are the least developed so I’ll just stick to the Value Proposition / Business Model portion.

Timeline

By guiding the user through a series of questions, Timeline will help them construct a clear, more detailed view of their lives and trajectory in this world. This detailed view will allow generations to come to have a better understanding of the decisions and milestones of the generations that have passed.

The primary goal would be to act as a tool for interviewing our parents, grandparents, aunts and uncles. One of the main issues with just ‘asking them’, is that episodic memory in humans is notoriously problematic. By providing general milestones, like births and weddings, we can guide the conversation in a more effective manner.

The process would be:

1. Start with general questions (when/where were you born? when/where were you married?)
2. Then ask, between this event and that one, what did you do?
3. Then, continue this process, splitting out the timeline until you have major events and minor events

The final output can then be shared with other family members who can clarify and correct events, providing information and photos as ‘proof’ or ‘counterproof’.

Gamify the GED

Using the requirements of the GED, build a game that uses game theory and instructional systems design to teach each topic using an MMPORG model. Once the gamer provides proof that they passed the GED, they are provided a trophy/badge and identified as a Sage.

If a Sage chooses to continue playing the game, they can play with other users but they cannot participate in raids or other clan-type actions.

The GED requirements for Basic Math, Geometry, Basic Algebra, Graphs and Functions will be used for magic skills. Harder questions for hard magic, easy ones for easy magic.

The requirements for Reading for Meaning, Identifying and Creating Arguments, Grammar and Language will relate to accepting and deciphering quests. To add to the sense of immersion. the different game-races will speak different languages. When these languages are translated into English, the grammar is problematic. The user is given puzzles that require them to identify arguments, meaning of sentences, and so on. As the user get’s more proficient, more quests are opened to them.

The requirements for Reading for Meaning in Social Studies, Analyzing Historical Events and Arguments in Social Studies, Using Numbers and Graphs in Social Studies will be incorporated into the world lore. A deeper understanding of the lore could unlock missions. As in, a legendary weapon can be found in X place but you have to travel to certain sites in order. To find those sites, you have to decipher and understand certain world related facts.

The requirements for Reading for Meaning in Science, Designing and Interpreting Science Experiments, Using Numbers and Graphics in Science could be incorporated into combat skills, armor and weapon skills. For example, ice creatures are weak against fire. But how? Is it the heat, the light, something else? Accept the quest and begin the experiment to find out, kill enough ice creatures with enough different methods, return to The Keep and report your findings. Then you are walked through correlation vs causation, drawing incomplete conclusions, etc. etc.

There are definitely opportunities to combine the actions for each requirement into interesting teaching methods.

The first thing I published on Medium was back in January 2019. Back then, I wasn’t even sure I could write an article a month. After 70 articles in 4.5 years, the math works out to 1.2 a month so I guess I was wrong.

Moving forward, I’ll be contributing to EYO Media’s Substack and the first BizMech post there will be up shortly.

Thank you to all the people who subscribed to me here. I hope you enjoyed what I wrote and I hope it inspired you to write.

Because if I can do it, anyone can.

UPDATE: …yeah, that whole substack thing didn’t work out so I guess I’m back.

And I think everyone should have that chance.

Value Proposition / Business Model

Temp works with gig workers, helping connect them to clients, but it goes beyond. When the worker is ready, Temp will help them set up their own business, guide them through the legal and taxation minefields, and support them in their journey towards being a small business owner.

By partnering with small business service providers, Temp will also serve as a ‘road test’ for various tools and platforms. This will allow for real-world testing and verification of claims, as well as actual usage data for market research.

Mission, Vision, Values

Nothing proved the value of the service industry more than the 2020 lockdowns. The demand for service workers outstripped the demand for programmers, executives, and other ‘white collar’ jobs.

And yet, we watched those same workers struggle to be treated with the respect they deserve. For every WFH policy rolled out in corporations, there were hundreds of gig workers who were put into dangerous situations with no healthcare coverage.

Temp is not just focused on connecting workers to clients. Temp wants every worker to become an entrepreneur. We will support them in managing multiple clients, until they can scale their business. We will help them with contracts, forming LLCs, and general operations.

While other gig apps try to focus on how to increase their slice of the pie, Temp will focus on making the pie bigger.

Our dream is to give everyone the opportunity to participate in the dearest wish of every American: to be able to turn down work because “you’re doing okay”.

People, Process, and Technology

People

There is almost no way to overemphasize the importance of building relationships with the providers and the clients. If 80% of your team is focused on this one aspect, everything else will be a walk in the park.

Without a clear understanding of what clients are looking for and what providers need to know, none of this will work. Market research is great, in aggregate, but detailed regional data will be the difference between adoption and avoidance.

Process

Providers join for free. They can list hours, rates, locations, details about what they will and won’t clean. They can also see a report on what clients in their area are looking for (30% need general cleaning, but 10% need help organizing, sort of thing)

Clients can pay more to get a specific cleaner at a specific time. That extra fee is evenly divided between the platform and the provider. This incentivizes providers to distinguish themselves, while also rewarding Temp’s internal staff.

A differentiator of the Temp process will be to encourage direct relationships between provider and client. Providing contract generation services, LLC formation, and general bookkeeping guidance will allow the providers to transition from the Temp service, to a more permanent business model. Once providers are there, institutional clients and larger contracts will be within their reach.

Technology

Not nearly as important as the people and the process. General IT skills for maintenance, or specialized knowledge of gig-type apps should be more than sufficient.

Scale and Scope

While I propose starting with house cleaners, that’s just because it was the first thing that popped into my head. This would apply to almost any other type of business, such as plumbers or general construction.

At first, it would make sense to limit the scale to large metro areas and to localize support to the region (LLCs in the US vs EU). Eventually, an international scale would be feasible. The need for increased support resources will be dependent on the variations between localities. (more variation = more resources)

Comments and commentary are more than welcome. Also, I could use some subscribers so feel free to pass this around!

Like a lot of readers, I tried my hand at writing. I did the NaNoWriMo thing. I did the “submit a short story to a publication you like” thing. And eventually, I even did the “hire an editor to polish up a manuscript” thing.

My editor, Amy Tipton of Feral Girl Books, was kind, supportive, and completely honest. Her feedback was clear, comprehensive, and insightful.

And that’s when I realized I am a terrible writer.

I had an image in my head, an idea of a narrative, but I lacked the skill to construct a decent plot or avoid trite ham-fisted stream-of-consciousness flourishes. And hearing that what was in my head, was not on the page, was a wakeup call.

So I started from scratch. I wondered, “What kind of novel am I writing? What are the basic beats/plot points of this kind of novel? How can I write like other authors, in this genre, that I adore?”

And so, as only someone avoiding a project they feel ill-equipped to tackle could, I thought of a business idea.

Value Proposition / Business Model

By leveraging the expertise of publishers, authors and their bodies of work, Writer uses machine learning to turn writers into authors.

Through partnerships with publishing houses, the value of authorship will not be diluted but enhanced. Publishers will see which writers are worth investing in, long before they have a finished draft. And authors themselves will have a chance to interact with their greatest fans, those who were inspired to write by the strength of their authorship.

In return, writers will see how closely their work aligns with their favorite authors. They receive prompts and suggestions on how to be like their heroes, while also seeing what aspects of their writing are unique and worth developing.

The balance between stakeholder value, the guiding force of publicly traded companies, and artistic value, the guiding force of writers and authors, is the Hard Part of publishing. After all, Fifty Shades started as pretty terrible Twilight fanfic, but became something incredibly lucrative. And some publishers, authors, and writers see that level of financial success AS success.

The goal of Writer is not to support only these stakeholders, but also the ones with an artistic vision, an ideal they hold more important than financial success. And by doing so, we can make the Hard Part, the Easy Part.

Mission, Vision, Values

The power of authorship isn’t in the ability to write. It is in the ability to understand how Michelangelo’s David was already there inside the marble, waiting to be set free.

The three groups we work with are the writers, the publishers, and the authors. The writers are our customers. The publishers and authors are our partners. Our mandate is to provide a clear and direct connection between our customers and our partners and to avoid injecting our own ideas and biases into the conversation.

People, Process, and Technology

People

You’re absolutely going to need a few ML engineers, preferably those with some experience in LLMs. A few more tech-savvy resources to ensure site stability and throughput would definitely be a good idea.

But the other half of the company will be client and customer relations focused.

Customer relations will be focused on UI/UX design for the platform. Remember, this entire edifice is functioning at the speed of computing, not at the speed of humans. If the interface is even a little complicated, writers will drop the platform and revert to, to paraphrase Pressfield, whatever environment will summon the Muse. There is evidence of this in distraction-free writing environments and their adherents.

The client relations will focus on publishers and authors.

Publishing, as an industry, has had a rough few decades with the advent of digital media and it’s distribution. As such, most established publishing houses will see this as an attempt to wrest further control of their ‘product’ from them. Having experienced staff, preferably those with previous publishing experience, will help assuage these concerns and foster healthier interactions.

Part of the negotiation with publishers will be around what the value for them would be. One major benefit would be alerting publishers that a writer happens to be working on a book that’s similar to a previously lucrative author’s work but unique in a particular way. Publisher’s would then be able to connect with the writer directly.

Authors, more so than publishers, are concerned about the impact of machine learning on their craft. Imagine is a learning model had access to every novel, novella, and short story of a particular author. How do you think they would feel about it? But ask an author if all the research they used to do, themselves, could be done in seconds by a machine? I suspect they’d have a far different response.

Meeting the authors where they are and walking them through their perfectly reasonable concerns will also be crucial to the success of this venture.

Process

Publishers are the starting point since they would have multiple authors and several works as a base. Their editorial staff should also be engaged by the UI/UX team to understand the kinds of feedback writers expect.

Writers start by registering and setting up a payment protocol. They will be charged a fixed monthly fee to store their content with the understanding that it will be mined and learned from.

Writers then upload a body of their own work with timestamps. The model can learn how they’ve progressed over time. By comparing this to a model of a famous author, the areas of development can be illuminated.

General suggestions could look like, “Spend more time developing the main character.” More specific ones might look like, “Spend less time discussing the flora and fauna and more time on the battles.”

It should also be possible to combine authors, genres, and even lengths. A short story vs a multi-part series, for example.

Monetizing the publishers’ and authors’ interaction with the platform would be tricky, but achieveable. Publisher’s can license their work with the explicit understanding that it would not be used to generate new works. Authors can do the same. The licensing costs can be managed in tiers.

Lowest tier could be where the publishers/authors are paid an amount, per month, based on the number of writers that refernce their work. The highest tier could be a very low payment, but access to the platform and it’s findings.

Technology

Same as TOChanger except that the reporting out of the model will be less pure statistics and more interpretation of the statistics.

LLMs are great at detecting how close one piece of text is to another, but perfect matches are plagiarism and, to be honest, boring. But there is a sweet spot where one piece ‘reads’ like another and that will have to be determined by all the stakeholders.

If publishers are looking for something ‘like’ a certain author, they have to define what ‘like’ looks like to the LLM. Is it a 50% match or a 70% match? Is that 20% difference enough to make readers of an author’s previous works enjoy a new writer’s work?

Is 20% the difference between Clive Barker and Stephen King? Or is it the difference between Cliver Barker and Neil Gaiman?

Data is not information. And information is based on interpretation. Simon and Shuster’s target range will be different than an indie publisher, simply because the risk tolerance for a large publisher is naturally lower.

This goes for the writers themselves as well. Fostering a unique voice might require prompting the writer to choose more appropriate target authors or genres or even finished product.

Scale and Scope

I suggest starting off with a free version for writers. It would allow them to upload their text, compare it to an author’s and get a series of suggestions. The free version, doesn’t know who you are so the suggestions end up being vague. Also, the content uploaded is ingested into the model so it can report what kinds of writers want to write like which authors. This information could be used by publishers and authors themselves.

The paid version, described earlier, should be launched after this free version has ‘made the rounds’. If there isn’t enough traction, the pivot might be to license the model and method to publishers for their in-house usage. This turns a B2C idea into a pure B2B idea and there’s nothing wrong with that, per se.

As far as expansion beyond prose, screenplays would be a good next step. In this case, you’d have to engage with studios instead of publishers. And unless you want to run afoul of unions (and you don’t), you’d better get the various guilds on board with the idea.

I still have a problem with getting too many books. But I’ve gotten better at giving them away after I’ve read them.

Still working on being a better writer, though. After all, this article may be my least-edited, most-freeformed one ever!

[Update: So I published this on May 1, and on May 5, this crossed my feeds.]

Anyway, Mike taught me a lot of things (and still does) but one thing that I cannot seem to unlearn is how to listen. See, Mike’s hearing borders on supernatural. This makes his skills as an engineer and producer just plain eldritch. With his help, I learned how to hear how a small tweak can make a snare pop or guitars chug. With a lot of focus, I can hear the difference in compression rates in a double blind A/B test. With his help, my hearing became more active than passive.

And that completely ruined me. Here’s why.

The signal chain is a phrase most musicians know. It means the things between an input and its output. If you’re listening to music on your phone, the chain is “file -> music player app -> phone speakers”

Change any of those things and you change the output. Its why you hear more bass on big speakers than small ones. Its why old mp3s sounded worse than CDs and why they got better.

Imagine you record a singer. You play it back on your big speakers and the singer sounds great. You play it back on your cellphone and it sounds horrible. Obviously, the problem isn’t the recording, it’s the speakers, right?

So, if you’re working in a studio, you want to be sure that everything in your signal chain is as clean as possible. That way, you can really hear what’s going on. This is called a “flat frequency response. “

The problem is that most commercial headphones do not have a flat frequency response. In fact, some brands (rhymes with Meats by May) color the output. This isn’t a bad thing! Who doesn’t like a little more bass in their face?

Still, I needed a comfortable set of headphones with a flat frequency response. After digging through forums, reviews, specification documents, and bulletin boards, I needed to study the lingua franca of the headphone industry to find the headphones I wanted. (Image below is not of the ones I have, but Sennheiser does make some pretty great headphones)

This sort of process would repeat itself when I went looking for a carbon steel pan, a laptop, a tablet, etc. etc. If you are particular about what you want, going off reviews and popularity isn’t a good way to find something. That’s why review sites like The Wirecutter and subreddits like r/BIFL are better than just buying the item with the most reviews on the massive shopping site of your choice.

With the recent flood of AI-hype, I wondered, “Could that solve this problem?”

Value Proposition / Business Model

Through the use of LLMs, Choosier turns complex product choices into simple decisions. By answering a few basic questions, users will be able to navigate from desire to purchase, lowering the barrier into specialist product spaces.

People, Process, Technology

People

Data scientists and marketers are going to be the backbone of this venture. We’ll also need some developers and designers to make the site user friendly and stable.

Marketing specialists will identify market spaces with high barriers of entry from the consumer perspective. Then, data scientists would analyze the space and identify the decision points.

For example, in the case of a suit, the first decision point is probably budget range. Too low and the word bespoke would not appear in the options. Too high and ‘off the rack’ disappears as an option. But this might not be true for other products, especially if price is not the primary separator.

To determine the primary categorization variables, AKA decision points, you’d need a massive library of every review and comment of a product. Then, you’d have to determine which variables were dependent vs independent. Then you’d have to offer those variables to users to determine which of them were most aligned with their decisions.

Understanding how the data translates into human behavior, decisions, and intentions, is a whole other thing. As long as human decisions are not purely rational (which they aren’t), this isn’t the kind of thing that can be fully automated away.

Process

From the user’s perspective, it would be as simple as interacting with a website and clicking ‘Buy’ at the end of the process.

From an internal perspective, it gets a little trickier.

Not only will every user journey need to be incorporated into the decision tree, but the final output of the journey will need to be understood. What percentage of journeys led to an online sale? If they didn’t lead to a sale, did they at least result in a decision? And what does that teach us about the space? And how does that inform changes to the LLM?

Technology

Leaning heavily on cloud services here is the right decision. While LLMs are capable of being run on smaller and smaller devices, more information regarding the efficacy of this particular application requires we start big and whittle it down.

Another component will be the UI/UX development around this kind of service. Do users want to be informed of the ramifications of their decisions? As in, “Choice A means it will be machine washable, B means handwash or dryclean only, choose one” or “Cotton vs silk?” The LLM might be able to break down the decision tree, but how that is communicated is a different question. The LLM can still provide the related terms or phrases.

Using the example before, the LLM would be able to, if prompted, provide the answer, “what are the pros and cons to cotton and silk socks?”. But do users who see that information have a different experience, post-purchase, than those who don’t? That’s a question for UI/UX.

Scale and Scope

To start, focus on luxury or niche goods. The kinds of items that aren’t a simple commodity choice. Once that is tested and verified, the expansion into other luxury goods is preferable to moving towards commodity goods. The value prop is diluted as the product becomes more generic.

With that in mind, the service should not be free or funded with advertising. Users will need a subscription to use the service, and its cost should be high enough to make it obvious this is only for luxury goods.

Additionally, to maintain the trust of the customers, avoid using affiliate links or tweaking the algo to promote products. If anything, there is a chance to sell the information about Choosier’s decision trees to the manufacturers themselves.

Hopefully, this is the most vague of all the BizMech Ideas I plan to discuss. There are a lot of questions and areas for improvement but I hope this sparks discussion and even debate.

If you want to run with this idea, or you want to tell me how horrifically wrong I am, reach out. No one accomplishes anything great all by themselves.

And no idea stands perfectly on its own.

And after moving here, I think that’s a little off the mark.

What it should say is “You could eat a different cuisine, three meals a day, for the rest of your life, and still not eat at every restaurant in NYC”

You could open your day with congee, have a light lunch of taco salad, and a dinner of Ghanaian curry and fufu. You could have a breakfast smoothie, a pastrami on rye, and yuca con chicharron for dinner.

Then the lockdowns came. And NYC delivered.

A number of restaurants went from no-delivery to pick your delivery platform. And, with the eruption of ghost kitchens, there were now delivery-only restaurants of dizzying varieties!

The problem is that most restaurants never accounted for selling their food via delivery. And that’s a serious problem for food that’s supposed to be crispy but arrives soggy from the trapped steam.

(There’s no data to support this, but I suspect the air fryer trend was a result of people wanting crunchy snacks but only getting damp disappointment when the delivery arrived.)

Then, while gnawing on mushy tempura, the idea struck. So here it is, a solution to soggy snacks.

Value Proposition / Business Model

By approaching the process holistically, Crispier will revolutionize food delivery. Better package design will ensure crispy food never arrives soggy. Better customer reports will help restaurants tweak their offerings. And better engagement with the customers will forge a sustainable path forward.

People, Process, and Technology

People

This workforce is going to be intentionally siloed. Each silo will focus on a single aspect of the full system and competition between silos will be encouraged. I’m not suggesting outright warfare, but the occasional skirmish might not be a bad idea.

The silos will be Product and Customer.

The Product silo will need researchers and specialists in material science. It will also need packaging designers. Their mandate is the better takeout container. At first, it will be a standard container that allows crispy food to be transported without becoming soggy.

This could be something as simple as micropores in the lids or some other neat trick of packaging design. Maybe there’s a way to have a food-safe glue that unsticks at a certain temp/humidity. If that glue is used to hold some vents closed, they would open at that threshold, venting the steam. But, since I’m not a packing designer, I have no idea if that is even feasible.

The Customer silo will interact directly with the end users, gathering their feedback. Their mandate is happier customers. Regardless of Product’s designs, or stress tests, the Customer silo can veto a design because of a valid complaint.

To provide an outlet for this antagonism, and improve engagement, regular team events that pit the silos against each other would be a good idea. Team sports are a good example, but hackathons, potlucks with prizes, and other types of competitions would work here too.

For introverts and people who would prefer to avoid socialized contests, there could be other sorts of outlets. For example: Who can crochet the longest scarf? Who can recite Pi to the most digits? Who can solve a Rubix’s cube the fastest?

Process

Before anything can get started, a prototype container must be made by the Product silo. Without the prototype, there is no business.

The prototype must have clear specifications, tested by the Product silo. The spec’s should cover things like ‘maximum transit time’ and ‘maximum volume capacity’. The prototype is then handed off to the Customer silo. They will field test the container by sending to interested parties (chefs, restaurants, etc). After verifying Product’s claims, the prototype can be offered to the test market.

Once the containers are in use, the Customer silo will wait for customers to engage. To incentivize the end users, those who provide a number of reviews should be given a gift. This gift should be a unique physical object of some type, rather than a discount code or monetary reward. That way, the gift box itself can also be used to send those containers back to Crispier.

Alternately, the customers could drop them off at one of the restaurants that use the containers. Giving the users the option to drop to said restaurants does two things. First, it allows the users a more sustainable option than relying on mail carriers. Second, it demonstrates to the restaurant how many people use the containers.

In either case, getting the product back creates a cyclic economy of sorts, reducing carbon footprint and ensuring that damaged/faulty containers aren’t bouncing around ‘in the wild’.

The data provided by the customers can be used to define clusters. If the clusters are around the same restaurant, then the issue could be the food prep rather than the containers. If the clusters are around the model of the container, that information will be brought back to the Product silo to address the problem.

This is why the silos must be antagonistic towards each other. If Customer’s complaints do not have to do with the packaging, it will be up to Product to prove that. If the complaint is valid, it’s up to the Customer silo to champion the issue and ensure that it is not minimized or ignored.

And as the data is gathered, the containers’ value to restaurants will be easier and easier to prove. The Customer silo can also directly interface with restaurants with the customer data. As in, “Your fries show up soggy. Other restaurants do not have this issue. This is how they do it.”

In this way, the Customer is not just the end user, but also the restaurants themselves.

Technology

Similar to the MVC of TOChanger, but with a few differences

Model — The database will contain the unique ID of each container, where it was sent, if a customer reviewed it, which version of container it is, etc. etc. This will allow the identification of failed designs and facilitate recalls.

Adding a sticker to the lid of the container would be fine as a first step, but in time, incorporating a QR code into the container would be a better idea.

It should also contain a comprehensive listing of the restaurants that have the containers and an up to date listing of their menu.

View — The end user view will allow the customer to enter the container’s unique ID and to report their experience. This should include the restaurant the food came from, and hopefully which specific food was ordered. It should also allow them to report shipping/dropping off containers.

The restaurant view should allow for ordering more containers, returning unused or deprecated models for a discount on future orders, as well as notifications for recipe changes that might reduce customer complaints.

There should be one internal view for each silo, providing full transparency on customer feedback for both teams.

Controller — An analysis suite for identifying correlations should be part of the internal view. The other controller will be a CRUD interface into the Model for the end user and restaurant views.

Scale and Scope

At first, the scale of this venture should be kept within a small urban area. Nothing as sizeable as NYC, but a city the size and density of Zurich would be ideal. That way, the initial designs can be tested and refined in a more forgiving environment.

As the scale of the venture expands, so should the scope. More designs for larger and smaller containers and more sustainable versions would be great expansion opportunities.

So that’s the idea. It’s a little less detailed than I’d like but that’s because I don’t know too much about material science and packaging.

As with all the BizMech ideas, if you’d like to run with this, be my guest and reach out if you have any questions.

Let’s say you want to reduce the number of people end up back in jail after being released. Your TOC might be “if we find jobs for people who were incarcerated, then we can reduce the number of them that go back to jail”

What was fascinating to me was how close to the scientific method this was. You have a theory, you try something, and based on the results, you prove or disprove the theory. And since this was to be applied to non-profits, it would work the same way that market forces work in the for-profit world.

But I chalked the whole idea of a theory of change into the “nice idea in a perfect world” and tossed it into my backlog of ideas.

Then, one day, in the middle of a shower, it bubbled back into my head. After slip-sliding my way to my notebook and furiously drying my hands, I managed to sketch out an idea.

So here it is, a Theory of Change based business idea.

Value Proposition / Business Model

Donating to a non-profit is seen as a ‘good thing’. But sometimes, doing a ‘good thing’ doesn’t result in a better world. And other times, the smallest act can have massive ramifications.

But what if you could say, within a reasonable margin of error, that the things you’ve done have caused a measurable change in the world?

By using public data sources, TOChanger can empirically demonstrate the impact of a non-profit. TOChanger will use machine learning to identify not just the intended effects, but the also the unintended ones.

Non-profits will be ranked and scored based on their verifiable impact. You can choose to donate directly to one of them, choose which group you want to help, or leave it up to us to direct the funds where we think you’ll make the highest impact.

To ensure impartiality, TOChanger will be funded by a fixed percentage of the incoming donations. TOChanger will also avoid any external funding that may impact it’s impartiality or potentially endanger the data of those being served by the NGOs and non-profits.

People, Process, and Technology

Let’s do this in reverse order.

Technology

Using the MVC framework, the tech stack might look like:

Model — Big, unstructured, non-RDBMS, secure, cloud based

View — Data input for orgs seeking funding, and another for funders, dashboards for orgs and funders, and an analysis suite for the data.

Controller — CRUD for batch and individual records (heavily permissioned). Many cloud DBs have these as a feature.

The core of the stack will use machine learning (ML) to find the correlations between actions and results. To start, this stack will need as much base-level demographic information as possible. Taking the pre-existing public databases of populations will provide base-level demographic data. The US Census is a good example of this kind of public data, but other data sources exist. Note that the main reason for using unstructured data is because many of these data sources are not standardized.

With that base level data, subdivisions can be constructed by the variables in the TOC. If the TOC focuses on a cohort that isn’t covered by this base-level data, the information must be provided by the funded orgs. For example, if the TOC is about low income children, the base-level data from the US census should suffice. But if the TOC focuses on students with learning disabilities, the funded org will need to provide that information or it will need to be gathered from alternate sources. The ML analysis will have to factor in the size of the target cohort vs the total cohort to prove/disprove the TOC.

Finally, after the inital TOC is tested, other variables of interest will be tested. It may be that the non-profits impact is greater in a different area than intended. This allows the discovery and highlighting of outsized impacts.

Process

For Non-profits

1. Org applies for funding, clearly outlining their TOC
2. Their TOC is tested against the base-level data. If it is validated, they are listed as a potential target. If not, they are informed what interventions are more tightly correlated with their TOC.
3. If the TOC is inconclusive, the org is given an interface into the tech stack to provide their data. There will be requirements for the kind of data that must be provided, but the stack will allow for various forms of ingestion via API or other options.
4. After one funding cycle, the inconclusive orgs are then reevaluated. If they remain inconclusive, they will receive guidance and coaching towards changing their interventions based on current findings.
5. At the end of each funding cycle, all the orgs are reevaluated, scored, and the most impactful orgs are highlighted to the funders.

For Funders

1. Create a profile and connect a payment method
2. Identify which areas you want to make an impact
3. Define what percentage of your donations should be dedicated to each area
4. The platform will provide suggested orgs and their information
5. You can then accept or reject the suggestions
6. Every fundraising cycle, the most impactful funders will be highlighted alongside the most impactful orgs

People

The workforce will need the following skills

1. Data analysis for the core of the stack
2. Software development for the interface into the stack. This will include three views: funders, funded, and inconclusive.
3. Soft skills, especially coaching and empathy for non-profits

Minimizing the customer support roles can be done by improving the interface, but staff in this role can be cross-trained to provide support to inconclusive orgs. If the staff are also cross trained in data analysis and UI/UX, then headcount can shift between roles. This keeps operational costs down and improves employee engagement, simultaneously.

For example, a customer support rep could raise the issue of a particular data entry field being problematic for that type of NGO. That rep, leveraging their experience, weighs in on the UX and then the UI. With a little additional training, that same rep could develop and test the new solution. Finally, that rep could reach out to the NGO that triggered this issue, and work with them to perform user tests.

For more ideas on how to work with the People part of this puzzle, check out Brushfire and The Flywheel and The Lever, two short ebooks I wrote on the topic.

Scale and Scope

So far this model has focuses on crowdfunding, but there exists the opportunity to work with local and state governments on this sort of work. That said, this sort of scaling effort will require substantial effort (as anyone who’s working in fundraising will tell you)

Further, there are international aid organizations that would benefit from the ML core of the stack for their own internal audits.

So, that’s my idea. There are a lot of gaps and holes and missing details but nothing that can’t be addressed with a little time and focus. If you’d like to run with this, be my guest and reach out if you have any questions.

In ‘13, I started using checklists to organize myself and relied heavily on GQueues. Everything went into a checklist, from work related projects to personal hobbies. I even had my meals planned out in it!

When I started my BizMech consultancy, I relied heavily on the app to keep an eye on the operational challenges of my clients. I can honestly say that the last five years would have been an impossible without GQueues.

But then, a friend of mine introduced me to Habitica and told me how much it changed her life. It didn’t work for my professional needs but it seemed like an interesting change of pace. And the gamified aspects roused the part of me that stayed up all night to grind levels.

I started to migrate my personal checklists over to Habitica in late December ’22 and immediately ran into a problem. See, in GQueues, I could have items without deadlines. My ‘Business Ideas’ checklist was full of half-finished ideas and rough sketches. Habitica , in contrast, doesn’t let you just ‘sit’ on an idea.

After reviewing the list, I realized there was no way I could do all these things, all at once. So instead of trying to do them myself, I‘m going to throw them out into the internet. Someone, somewhere, might have the time and drive to make these ideas a reality. Even if that person isn’t you, maybe you know someone who might find the idea interesting.

Every idea will have one or all of the following:

1. Value Proposition / Business Model
2. Mission, Vision, Values
3. People, Process, and Technology

I’m pretty sure that some of these ideas are being addressed by others, probably in better ways than I can imagine. So, follow and leave comments if you can think of a way someone else has done it.