Due Diligence: Seven Bridges Genomics (Part 2)

Continuing with the top-down analysis from Part 1, lets look at the cloud genomics analysis industry with a focus on macro-scale phenomena. Seven Bridges Genomics, or any other individual firm for that matter, won't be able to do much about these, other than role with the punches.

In a future post, I'll go micro and drill-down to the unique selling points, enduring competitive advantages and economic moat that make Seven Bridges Genomics value proposition durable and secure (aside: hopefully).

Value Proposition

Provide the tools for scientist to do analysis without having to worry about the details of (1) compute/IT and (2) standardized work-streams.

Those are some hefty assumptions...

(1) Assumes scientists are compute limited.

(2a) Assumes there is a value-add in standardized work-streams

(2b) Which then, in-turn assumes, that there exists standard work-streams.

Making Money

As a private company, I can't do a deep-dive into their financials (aside: woe!), so I have to make some assumptions. From the marketing there seem to be two potential revenue streams...
(A) 'Compute Spread,' basically an interest rate spread but for AWS CPUs. The justify their mark-up over AWS compute pricing based on the perception of value-added. Note that this is a subclass of software as a service.
(B) Consulting

(A) must necessarily dwarf (B). Traditional consulting doesn't scale, which dooms a tech company before it can gain its sea legs / line-cross / other nautical right of passage, i.e. shark VC money. Consulting firms can bootstrap, but that doesn't seem like the growth trajectory they're going for.

 So for simplicity, lets reduce to (A). Taking the spread comes with both top-line and bottom-line risk. 

 Paramount amount them, the bottom-line risk of becoming an AWS whipping boy. You can scream for mercy, not that it helps. Honestly, other than try and take the compute in-house or trade masters.

In-house: Manage to do it even comparable to AWS...ha! 
Trade Masters: High switching cost...if it comes to this, were doomed a long time ago.

On the top-line, they need to either work in an highly inefficient market (alas, big banks) or continuously justify the spread they take through value-add. As I mentioned in the previous post, there is loads of competition with no clear market leader. Market structure will not save them, so value-add they must maintain, less open source eats their lunch.

Macro Swallow

The internet meme of near-misses between whales and humans, including such precious lines as “You’re gunna have to do more than clean that wet suit bro” [Youtube] are the impetus behind this section.

It IS a big ocean; however, there are lots of fishies $£€ to be had in a quite restricted space, the wind-up to a feeding frenzy. There are many ways to die.

Last post I based my mental model on drivers and constraints, but this time around a framework based on relative growth rates seems more suitable. A swallow, in this context, means the facet of growth that trumps the others.

Data swallow

Fail: Value Proposition 1

I/O swallow:

Problem: Impractical to upload data to cloud.

Solution: Co-locate with sequencing centers; however, this requires a) consolidation in sequencing industry (mass-market) or b) working with and servicing big co's exclusively.

Prognosis: Not great. a) Is survivable, but may kill the growth curve. b) Basically become just another IT integrator / service provider. Not scalable. Both mean having an additional whipping masters (AWS + core/big co). 

Storage swallow:

Problem: Impracticable to store data.

Solution: Stream data to be processed in real-time.

Prognosis: Would actually be a boon for Seven Bridges if they could solve the streaming and real-time analysis, as it enhances the value proposition.

Compute swallow

Fail: Tech swings against you

But personal processing power grows even faster:

Problem: New algos or technology lower the compute burden, making the cloud unnecessary. Can go back to on-laptop analysis, where other established firms, e.g. Acelrys, may well eat your lunch.

Solution: Go toe-to-toe away from the cloud. Convince that cloud is worthwhile for other reasons (hassle free, a la Google Docs).

Prognosis: If desktop, grim (infrastructure re-boot). If cloud, fine.

But processing doesn't grow fast enough:

Problem: Can't make money off the AWS spread because tasks are sucking too much compute

Solution: Hope parallelism and clever algo saves you, otherwise...

Prognosis: If AWS can’t do it, neither can you most likely. Hosed.

People swallow

Fail: Value proposition 2

Problem (2a): Scientist don't value your workstreams.

Solution: Hope your compute value proposition holds.

Prognosis: If your API doesn't suck, they build there own in your sandbox IF the compute justification is strong enough. Will become niche for low-end / small-time users, as more sophisticated users disintermediate you and take their algo straight to compute.

Problem (2b): Model fails since there are no standardized workflows. Everything must be custom/application specific.

Solution: Turn into a consulting company.

Prognosis: No scale. Either turn niche, or eaten by a bigger consulting fish with scale in consulting.

Takehome

If you're placing a positive bet on the cloud genome analysis industry, not just Seven Bridges Genomics in particular, you're taking a few implicit assumptions...

1. I/O swallow will not kill the industry in the cradle.
2. Compute challenges are Goldilocks.
3. Bioinformatics is amenable to automation and cross-application standardization.

I'm fairly confident of an all-clear on (2) and (3), but (1) worries me. There are solutions here if the company can pivot fast enough, but I'm not convinced that a start-up, as opposed to a core/big co, has the leverage to pull it off. 

There is also a get out of jail free card...alternate value propositions. 

One that sits quite well for the cloud is integration between different datasets, a task made much easier once all this disperate data is sitting on servers you control. One can imagine mining other peoples data and selling insights. This is NOT consulting in the traditional sense, but scalable returns from data integration and automated analysis.

Only time will tell...

Due Diligence: Seven Bridges Genomics (Part 1)

"Demonstrate your learning capabilities," how exactly to do that, I wondered. Develop a mental model! 

I've spent the past few hours reading about the field of genomics & next-gen sequencing, with respect to one firm: Seven Bridges Genomics. 

First I developed a sense of...

• Promise -- Hard: Routine genomic diagnosis; Harder: Personalized Medicine
• Problem -- Next-gen sequencing Data → Actionable Results
• Solution -- ???

After that, I was bit stuck. How can one summarize an entire field with one mental model, one graphic. 

I thought about...

• Competitive landscape (SWOT)
• BCG Matrix (Definite with ? for most firms)
• Key players (Companies, People, Locations)

But none of those are quite it. What is the root cause of the Problem. Here is a perfectly, imperfect mental model (all mental models are wrong, but some are useful) that seems to be working for me...

I think it comes down to drivers and constraints. Drivers being those things that push a technology forward, which of course require some metric to track changes in status (italics). Constraints being the rate-limiting resource which most hamper development, also complete with metrics (not-shown, but examples would be number of distinct technologies in the pipeline vs. maturity/ETA, cost per base-pair).

Each aspect of the ecosystem, from sequencing → assembly → analysis, has its own unique set of drivers and constraints. 

Now, is there a rate-limiting step in the ecosystem as a whole?  If so, that's as good a place as any to begin with high-impact solution...

• Sequencing -- Cost and time is already falling, with a healthy pipeline of new technologies (i.e. not a 'Pfizer').
• Assembly -- Incremental improvements in Robustness and speed. Throwing more compute (cheap!) at it generally seems to do the trick.
• Analysis -- More data (sequencing & assembly) don't seem to be resulting in more actionable insight. Ding ding. I think we have a winner.

One thing that may temper going for the rate-limiting step is relative easiness of attacking other problems first. They're all hard problems, so lets stick to our guns and go with rate-limiting.

Which brings us full-circle, back to Seven Bridges Genomics and their solution, Igor, a cloud-based analysis framework.

The software is constraint-oriented, knocking down barriers to compute and people. Let our clever architecture and Amazon Web Services (AWS) take care of the computational scaling. Let our clever bioinformaticians do the heavy-lifting, standardizing workflows for common problems, adapting and scaling existing solutions and maybe even banging out something completely novel.

The result -- time and cost savings due to the experience curve effects, standardization and economies of scale. Awesome, no?

It remains to be seen whether they can compete effectively. It's a crowded space, with no clear market leader; but that's a story for Part 2. Other takes here and here.

PS. I also quite enjoyed the play on the Seven Bridges problem (aside: at least I think it's intentional). Change the graph, e.g. bombing a bridge -- which is more or less what they hope to do with the analytics end of things, -- and you can force a solution.

Ryan’s Guide to Job Search Strategy: Assets

#1) Assets: Skills, Credentials & Experience

Goal: To enumerate our assets, vis-à-vis our skills, credentials and experience.

Assets: The attributes and abilities you bring to the table to get a job done.

Learn: How to use shotgunning, expansion and clustering to build skills clusters of your assets.

You've seen the big picture, now it’s time for the hardtack of reducing our newfound mental model to practice. For convenience, we’re going to work in linear order, starting with Assets. Here’s a mental model, native loop model on the top, and the linearization on the bottom.

A visible expression of an asset is a concrete skill, such as computer programming. This skill can be supported by credentials, e.g. a University degree in computer science, or experience, e.g. a software development internship with Amazon. Great, you’ve loads of credentials and experience. Following your broken mental model, you’ll list your credentials and experience on a resume, highlight them in a cover letter and force fit them to job posting. And you’ll get nowhere.   

The problem with simply listing your credentials and experience, which is what we were taught to do under our broken mental model, is that no one cares. No one cares that you have a PhD from Harvard. No one cares that you've volunteered with an after-school program.

People care about what you can do for them, about your skills that are useful to them. They care about your credentials and experience, only in so far as they present credible evidence of your skills, and only in so far as those skills that are useful to them.

We need a way of taking our credentials and experience and decomposing them into skills, skills that people and companies may find of use.

Our new mental model has the answer in three simple steps: Shotgun, Expand and Cluster.

1.1) Shotgun

Start by enumerating any skill, credential or experience you can think of. You can use your resume as a starting point, or work through the exercise with a friend.

I've grouped skills, credentials and experiences separately, but there is no need for structure at this point. You want to think as expansively as possible. If structuring helps, great. If a scatter plot works, also great.

1.2) Expand

Great, I've 9x solid items, 3x for each of skills, experiences and credentials. Let’s pick a few of these items and expand. Think about related items and write them down. Here’s mine:

I've chosen to expand a related experience and skill: NaturalMotion, a game middleware and free-to-play game company I interned with last summer, and computer programming, a skill I used extensively as an R&D intern building prototypes for NaturalMotion.

1.3) Cluster

Now that we've generated many skills and experiences, it’s time to cluster and connect them.

Think about the words and ideas that connect your varied activities. For clarity, I've colored connecting ideas in red, skills in dark blue and experiences in light blue. Ideas and skills are connected with lines. Related skills are connected with dashed lines. 

Things to think about:

• What is related?
• Python is related to Django, it's a web development framework.
• Where have I employed a particular skill?
• I wrote an integration test suite in Python
• What experiences did I have while earning my credential?
• I've written and presented loads of technical material during the course of my PhD

All paths should eventually end in a skill; however, it’s just fine to end with experience and cluster the skills elsewhere. This helps keep your clusters organized, which will be useful in the next step, putting your skills in the context for Mr. Market!

Tool Tip

draw.io is a wonderful tool for quickly making simple figures. Built-in integration with Google Drive and Dropbox allows it to easily tie-in with your existing workflow.

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Ryan’s Guide to Job Search Strategy: A More Effective Mental Model

A More Effective Mental Model

Here it is, my mental model of an effective job search strategy.

The first thing you’ll notice is that it’s loop driven, not the linear mental models to which we’re most accustomed.

The mental model is composed of two loops, the inner and outer. They are mutually interdependent, but due to their different focuses my be optimized independently.

The numbers at the top of each sub-loop, e.g. #1 for Assets, represent a linearization of the inherently non-linear loop-based mental model. I choose this linearization for convenience, but you can start anywhere on a loop-based model.

Inner loop - Assets, Mr. Market, Aspirations

The inner loop is primarily concerned with you. Your assets, your aspirations, and their context with respect to Mr. Market. This is the mechanism by which you choose where to play.

Outer loop - Companies, People, Applications

The outer loop is primarily concerned with others. Who they are, what they want, and the hoops they force candidates jump through, particularly for larger firms. This is the mechanism by which you actively position yourself to win an interview, and eventually a job offer.


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Ryan’s Guide to Job Search Strategy: Your Mental Model is Broken

Your Mental Model is Broken

Volumes have been written on strategy. In short, it comes down to choosing where to play and positioning how to win.

Unfortunately, the mental model of job search and acquisition that most of us were taught doesn't do either the where or how, let alone do them effectively.

If you’re like me, you were taught that if you gather enough qualifications/experience, work on your resume/cover letter enough and apply for enough positions, you would clear HR, land interviews and eventually a job offer.

The reality couldn't be further from the truth. For me, enough never happened. Enough doesn’t exist because my mental model was broken. My mental model, and I suspect yours as well, leads only to despair, not job offers.

I’m going to rework the first part of your mental model, the job search. If there is sufficient demand, I’ll cover interviewing and choosing between offers in a later guide. I've chosen to start with job search strategy because there downstream issues are a moot point if you don’t land interviews, or even better, so impress someone that you’re hired on the spot.

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