salt-licker

I’ve hired people who have not passed the coding screen (but did pass subsequent interviews) and they’ve been some of my best hires. But the type of failure really matters. Did they write generally correct code but ran out of time because the algorithm they came up with under time pressure was too complicated? Or did they fail to debug a list indexing off-by-one error, even after printing the list repeatedly? It’s sometimes a judgement call but I believe there’s a lot of signal in watching people write code in real-time

It’s definitely not an exact science.

1. I try to make the parameters of the interview as friendly as possible — language of their choice, don’t have to worry about code style or computational complexity, or talk while they’re coding, just write working code that solves the problem. They can use google freely. I’m there as a resource to talk through their solution, answer questions, and help them get unstuck if needed.

2. The algorithm to solve the question doesn’t require a trick and there are many viable solutions so most candidates can verbally explain to me a working algorithm quickly, +- a couple edge cases.

3. Candidates often get visibly flustered or hung up on irrelevant details, or unsure what exactly I want for them, and I focus on noticing this. I try to guide them down a correct path if they’re close and tell them not to over-architect if they start doing that.

Despite all this, too manly candidates do not have a dependable metaprocess for writing working code - a common scenario is they write code, it gives the wrong result, they drop some print statements to debug, but can’t effectively trace the program to figure out why they’re getting the debug output and so they guess and try to fix the wrong thing, causing further problems. I know that some of this is nerves but it can’t account for all of it.

Generally you don’t get all the resumes at once, this is literally the secretary problem. Unfortunately you need some objective standard of measurement across candidates that is static over at least months, and ideally takes a minimum of you and your candidates’ time. Though imperfect, real-time technical questions are a pretty good solution to this problem, especially since a lot of the best engineers prefer them to take-homes.

Also, anecdotally after interviewing hundreds of candidates, I’ve found resumes to have a relatively high false positive rate. Surprising numbers of engineers look great on paper but can’t write working code for semi-complex problems when tested, even accounting for interview anxiety.

Genesis Therapeutics | Burlingame, CA | Onsite (Flexible WFH) | Full-time | https://www.genesistherapeutics.ai

We’re a hybrid AI + biotech start-up, developing novel neural networks to predict molecular properties and using them to accelerate the development of new medicines.

Looking for great software engineers and ML researchers with an interest in drug discovery -- no biology or chemistry experience required. We all learn from each other here.

- We currently have a small team of excellent AI + software engineers: graduates from Stanford, UC Berkeley, MIT. Previously worked at Facebook, Google, Memsql, Jane Street

- Core deep learning tech was invented by co-founder + CEO Evan Feinberg during his PhD at Stanford’s Pande lab (the lab that did Folding@Home). See the peer-reviewed PotentialNet paper: https://pubs.acs.org/doi/10.1021/acscentsci.8b00507

- Seed round led by Andreessen Horowitz, and we currently have lots of runway

- Our platform was validated in collaboration with a top-five pharma company, in a public paper: https://arxiv.org/abs/1903.11789

- In addition to strong software + AI talent, our small team has top drug discovery chemists who have collectively discovered several FDA-approved drugs before

Here are our open roles:

- AI Engineer (Research Scientist): https://jobs.genesistherapeutics.ai/apply/NAtx0CsIIL/AI-Engi...

- Infrastructure Engineer: https://jobs.genesistherapeutics.ai/apply/l4acEy0fZl/Infrast...

- Software Engineer: https://jobs.genesistherapeutics.ai/apply/ILdjypU87G/Softwar...

Interview process: 1-2 one-hour technical phone screens, 1 day on-site (now virtual) with 3 one-hour technical questions. All these include extra time to chat, answer your questions about Genesis, and meet us. Can go from first email to offer in a week or two

Tech Stack: python, C++, pytorch, postgres, docker, kubernetes, various computational chemistry libraries + tools

Please apply online, or email me your resume: ben@genesistherapeutics.ai

There’s a lot of startups purporting to bring hard science to market but most of them are incompetent or snake oil and it’s very difficult for software engineers (or investors) to identify the diamonds in the rough with 0 domain expertise. Meanwhile many of these startups suffer from a lack of exceptional software engineering that is often necessary to solve novel cross-disciplinary problems from first principles.

It would be a boon for society if it were common for great programmers interested in hard problems to take a year off from their lucrative dead-end big tech co careers and study a subject outside of CS that they’re interested in, so at least they’d be able to evaluate the feasibility and importance of technical challenges in that field and apply their skills at a point of high leverage in that domain.

Genesis Therapeutics | Burlingame, CA | Onsite (Flexible WFH) | Full-time | https://www.genesistherapeutics.ai

We’re a hybrid AI + biotech start-up, developing novel neural networks to predict molecular properties and using them to accelerate the development of new medicines.

Looking for great software engineers and ML researchers with an interest in drug discovery -- no biology or chemistry experience required. We all learn from each other here.

- We currently have a small team of excellent AI + software engineers: graduates from Stanford, UC Berkeley, MIT. Previously worked at Facebook, Google, Memsql, Jane Street

- Core deep learning tech was invented by co-founder + CEO Evan Feinberg during his PhD at Stanford’s Pande lab (the lab that did Folding@Home). See the peer-reviewed PotentialNet paper: https://pubs.acs.org/doi/10.1021/acscentsci.8b00507

- Seed round led by Andreessen Horowitz, and we currently have lots of runway

- Our platform was validated in collaboration with a top-five pharma company, in a public paper: https://arxiv.org/abs/1903.11789

- In addition to strong software + AI talent, our small team has top drug discovery chemists who have collectively discovered several FDA-approved drugs before

Here are our open roles:

- AI Engineer (Research Scientist): https://jobs.genesistherapeutics.ai/apply/NAtx0CsIIL/AI-Engi...

- Software Engineer: https://jobs.genesistherapeutics.ai/apply/ILdjypU87G/Softwar...

Interview process: 1-2 one-hour technical phone screens, 1 day on-site (now virtual) with 3 one-hour technical questions. All these include extra time to chat, answer your questions about Genesis, and meet us. Can go from first email to offer in a week or two

Tech Stack: python, C++, pytorch, postgres, docker, kubernetes, various computational chemistry libraries + tools

Please apply online, or email me your resume: ben@genesistherapeutics.ai

Yep. It was also an amazingly natural way to learn to code. Gen Z has a lower barrier to entry than before for creating just about every form of multimedia content except games — I’d love to see someone build a fully integrated, easy-to-learn game design platform to encourage more of them to get into programming.

Genesis Therapeutics | Burlingame, CA | Onsite (Flexible WFH) | Full-time | https://www.genesistherapeutics.ai

We’re a hybrid AI + biotech start-up, developing novel neural networks to predict molecular properties and using them to accelerate the development of new medicines.

Looking for great software engineers and ML researchers with an interest in drug discovery -- no biology or chemistry experience required. We all learn from each other here.

- We currently have a small team of excellent software engineers: graduates from Stanford, UC Berkeley, MIT. Previously worked at Facebook, Google, Memsql, Jane Street

- Core deep learning tech was invented by co-founder + CEO Evan Feinberg during his PhD at Stanford’s Pande lab (the lab that did Folding@Home). See the peer-reviewed PotentialNet paper: https://pubs.acs.org/doi/10.1021/acscentsci.8b00507

- $4.1m seed round led by Andreessen Horowitz, and we currently have lots of runway

- Our platform was validated in collaboration with a top-five pharma company, in a public paper: https://arxiv.org/abs/1903.11789

- In addition to strong software + AI talent, our small team has top drug discovery chemists who have collectively discovered several FDA-approved drugs before

Here are our open roles:

- Software Engineer: https://jobs.genesistherapeutics.ai/apply/ILdjypU87G/Softwar...

- AI Engineer: https://jobs.genesistherapeutics.ai/apply/NAtx0CsIIL/AI-Engi...

Interview process: 1-2 one-hour technical phone screens, 1 day on-site (now virtual) with 3 one-hour technical questions. All these include extra time to chat, answer your questions about Genesis, and meet us. Can go from first email to offer in a week or two

Tech Stack: python, C++, pytorch, postgres, docker, kubernetes, various computational chemistry libraries + tools

Please apply online, or email me your resume: ben@genesistherapeutics.ai

Yes, one of the founders is a coder who developed RSI and couldn’t find a good tool. In a form of UX bootstrapping, I’m pretty sure the product is being used to build itself

Genesis Therapeutics | South San Francisco, CA | Onsite (Flexible WFH) | Full-time | https://www.genesistherapeutics.ai

We’re a hybrid AI + biotech start-up, developing novel neural networks to predict molecular properties and using them to accelerate the development of new medicines.

Looking for great software engineers and ML researchers with an interest in drug discovery -- no biology or chemistry experience required. We all learn from each other here.

- We currently have a small team of excellent software engineers: graduates from Stanford, UC Berkeley, MIT. Previously worked at Facebook, Google, Memsql, Jane Street

- Core deep learning tech was invented by co-founder + CEO Evan Feinberg during his PhD at Stanford’s Pande lab (the lab that did Folding@Home). See the peer-reviewed PotentialNet paper: https://pubs.acs.org/doi/10.1021/acscentsci.8b00507

- $4.1m seed round led by Andreessen Horowitz, and we currently have lots of runway

- Our platform was validated in collaboration with a top-five pharma company, in a public paper: https://arxiv.org/abs/1903.11789

- In addition to strong software + AI talent, our small team has top drug discovery chemists who have collectively discovered several FDA-approved drugs before

Here are our open roles:

- Software Engineer: https://jobs.genesistherapeutics.ai/apply/ILdjypU87G/Softwar...

- AI Engineer: https://jobs.genesistherapeutics.ai/apply/NAtx0CsIIL/AI-Engi...

Interview process: 1-2 one-hour technical phone screens, 1 day on-site (now virtual) with 3 one-hour technical questions. All these include extra time to chat, answer your questions about Genesis, and meet us. Can go from first email to offer in a week or two

Tech Stack: python, C++, pytorch, postgres, docker, kubernetes, various computational chemistry libraries + tools

Please apply online, or email me your resume: ben@genesistherapeutics.ai

Genesis Therapeutics | South San Francisco, CA | Onsite (Flexible WFH) | Full-time | https://www.genesistherapeutics.ai

We’re a hybrid AI + biotech start-up, developing novel neural networks to predict molecular properties and using them to accelerate the development of new medicines.

Looking for great software engineers and ML researchers with an interest in drug discovery -- no biology or chemistry experience required. We all learn from each other here.

- We currently have a small team of excellent software engineers: graduates from Stanford, UC Berkeley, MIT. Previously worked at Facebook, Google, Memsql, Jane Street

- Core deep learning tech was invented by co-founder + CEO Evan Feinberg during his PhD at Stanford’s Pande lab (the lab that did Folding@Home). See the peer-reviewed PotentialNet paper: https://pubs.acs.org/doi/10.1021/acscentsci.8b00507

- $4.1m seed round led by Andreessen Horowitz, and we currently have lots of runway

- Our platform was validated in collaboration with a top-five pharma company, in a public paper: https://arxiv.org/abs/1903.11789

- In addition to strong software + AI talent, our small team has top drug discovery chemists who have collectively discovered several FDA-approved drugs before

Here are our open roles:

- Software Engineer: https://jobs.genesistherapeutics.ai/apply/ILdjypU87G/Softwar...

- AI Engineer: https://jobs.genesistherapeutics.ai/apply/NAtx0CsIIL/AI-Engi...

Interview process: 1-2 one-hour technical phone screens, 1 day on-site (now virtual) with 3 one-hour technical questions. All these include extra time to chat, answer your questions about Genesis, and meet us. Can go from first email to offer in a week or two

Tech Stack: python, C++, pytorch, postgres, docker, kubernetes, various computational chemistry libraries + tools

Please apply online, or email me your resume: ben@genesistherapeutics.ai

Genesis Therapeutics | South San Francisco, CA | Onsite (Flexible WFH) | Full-time | https://www.genesistherapeutics.ai

We’re a hybrid AI + biotech start-up, developing novel neural networks to predict molecular properties and using them to accelerate the development of new medicines.

Looking for great software engineers and ML researchers with an interest in drug discovery -- no biology or chemistry experience required. We all learn from each other here.

- We currently have a small team of excellent software engineers: graduates from Stanford, UC Berkeley, MIT. Previously worked at Facebook, Google, Memsql, Jane Street

- Core deep learning tech was invented by co-founder + CEO Evan Feinberg during his PhD at Stanford’s Pande lab (the lab that did Folding@Home). See the peer-reviewed PotentialNet paper: https://pubs.acs.org/doi/10.1021/acscentsci.8b00507

- $4.1m seed round led by Andreessen Horowitz

- Our platform was validated in collaboration with a top-five pharma company, in a public paper: https://arxiv.org/abs/1903.11789

- In addition to strong software + AI talent, our small team has top drug discovery chemists who have collectively discovered several FDA-approved drugs before

Here are our open roles:

- Software Engineer: https://jobs.genesistherapeutics.ai/apply/ILdjypU87G/Softwar...

- AI Engineer: https://jobs.genesistherapeutics.ai/apply/NAtx0CsIIL/AI-Engi...

Interview process: 1-2 one-hour technical phone screens, 1 day on-site (now virtual) with 3 one-hour technical questions. All these include extra time to chat, answer your questions about Genesis, and meet us. Can go from first email to offer in less than a week

Tech Stack: python, pytorch, postgres, docker, kubernetes, various computational chemistry libraries + tools

Please apply online, or email me your resume: ben@genesistherapeutics.ai

Modway Articulate is what we have for everyone in our office: https://www.amazon.com/Modway-Articulate-Ergonomic-Office-Ch...

At $150 I find it comfortable enough to sit in all day without issue, which is rare even among more expensive chairs I’ve tried. One of our employees has ergonomic issues and loves it. I really like having the ability to adjust seat tilt and back tilt separately, as well as initial back height. The only downside is the armrests don’t go low enough to fit under your desk easily, so we generally leave the armrests off when building them

Genesis Therapeutics | South San Francisco, CA | Onsite (Flexible WFH) | Full-time | https://www.genesistherapeutics.ai

We’re a hybrid AI + biotech company, developing novel neural networks to predict molecular properties and using them to accelerate the development of new medicines.

Looking for great software engineers and ML researchers with an interest in drug discovery -- no biology or chemistry experience required. We all learn from each other here.

- We currently have a small team of excellent software engineers: graduates from Stanford, UC Berkeley, MIT. Previously worked at Facebook, Google, Memsql, Jane Street

- Core deep learning tech was invented by co-founder + CEO Evan Feinberg during his PhD at Stanford’s Pande lab (the lab that did Folding@Home). See the peer-reviewed PotentialNet paper: https://pubs.acs.org/doi/10.1021/acscentsci.8b00507

- $4.1m seed round led by Andreessen Horowitz

- Our platform was validated in collaboration with a top-five pharma company, in a public paper: https://arxiv.org/abs/1903.11789

- In addition to strong software + AI talent, our small team has top drug discovery chemists who have collectively discovered several FDA-approved drugs before

Here are our open roles:

- Software Engineer: https://jobs.genesistherapeutics.ai/apply/ILdjypU87G/Softwar...

- AI Engineer: https://jobs.genesistherapeutics.ai/apply/NAtx0CsIIL/AI-Engi...

Tech Stack: python, pytorch, postgres, docker, kubernetes, various computational chemistry libraries + tools

Please apply online, or email me your resume: ben@genesistherapeutics.ai

Oops, can’t believe I mistyped this, thanks. Should have had coffee today

Yep, I almost never drink coffee more than 3 days in a row because it loses its affect. Luckily, intolerance comes back after only a day or two off.

100% this. In some I think I can see recognizable images (a car? A bird? A human face?) bizarrely decomposed and distorted. Combined with the manically meticulous, not-quite-repetitive patterns and discolorations, the pieces have a distinctly tortured quality to them.

Genesis Therapeutics | South San Francisco, CA | Onsite | Full-time | https://www.genesistherapeutics.ai

We’re a hybrid AI + biotech company, developing novel neural networks to predict molecular properties and using them to accelerate the development of new medicines.

- Small team of excellent software engineers: graduates from Stanford, UC Berkeley, MIT. Previously worked at Facebook, Google, Memsql, Jane Street

- Core deep learning tech was invented by co-founder + CEO Evan Feinberg during his PhD at Stanford’s Pande lab (the lab that did Folding@Home). See the peer-reviewed PotentialNet paper: https://pubs.acs.org/doi/10.1021/acscentsci.8b00507

- Recently raised a $4.1m seed round led by Andreessen Horowitz

- Our platform was validated in collaboration with a top-five pharma company, in a public paper: https://arxiv.org/abs/1903.11789

- In addition to strong software + AI talent, our small team has top drug discovery chemists who have collectively discovered several FDA-approved drugs before

Here are our open roles:

- Software Engineer: https://jobs.genesistherapeutics.ai/apply/ILdjypU87G/Softwar...

- AI Engineer: https://jobs.genesistherapeutics.ai/apply/NAtx0CsIIL/AI-Engi...

Tech Stack: python, pytorch, postgres, docker, kubernetes, various computational chemistry libraries + tools

Please apply online, or email me your resume: ben@genesistherapeutics.ai

Yes, I agree the language should be compiled for performance, types enable compiler optimizations and it would be silly to abandon that benefit. By “scripting language” I mean the syntax will be similar to existing interpreted languages and it will be easy to write small scripts without specifying types since they are inferred.

I think languages which bolt some trait / generic / role system onto an object-oriented system are generally a bit too bloated, non-opinionated, and confusing. Typeclasses not only encourage an elegant separation between data and functions, they allow you to design guarantees about how your functions interact with each other into your program — basically, not only data inheritance but also functional inheritance. Object oriented syntactic sugar is an easy add-on to a typeclassed language, but not the other way around.

Beyond immediately reducing bugs, I think widespread use of this sort of language designed with a good type system from first principles would allow many more programmers to write programs with some provable security guarantees.

A programming language with Python-like syntax and a Hindley-Milner type system plus typeclasses. Basically, we bring Haskell’s type system an imperative programming language. An easy-to-learn scripting language with a strong, sound type system with reliable type inference will reduce bugs in codebases of every size across the industry.

Most of the cyclists I’m referring to have been cycling since childhood, so 15-20 years of experience, a lot of it in cities. Taking up the lane at the intersection is unrealistic in a lot of city scenarios, nobody I know advocates for getting in front of cars in the crosswalk or waiting in the middle of a long line of stopped cars at a red light.

Many veteran cyclists I know treat red lights as stop signs. It can be safer and better for the flow of traffic to get a head start on the cars stopped at the red light rather than jockeying with them for road space out of the gate when the light turns green.

Genesis Therapeutics | South San Francisco, CA | Onsite | Full-time | https://www.genesistherapeutics.ai

We’re a hybrid AI + biotech company, developing novel neural networks to predict molecular properties and using them to accelerate the development of new medicines.

- Core deep learning tech was invented by co-founder + CEO Evan Feinberg during his PhD at Stanford’s Pande lab (the lab that did Folding@Home). See the peer-reviewed PotentialNet paper: https://pubs.acs.org/doi/10.1021/acscentsci.8b00507

- Recently raised a $4.1m seed round led by Andreessen Horowitz

- Our platform was validated in collaboration with a top-five pharma company, in a public paper: https://arxiv.org/abs/1903.11789

- In addition to strong software + AI talent, our small team has top drug discovery chemists who have collectively discovered several FDA-approved drugs before

Here are our open roles:

- Software Engineer: https://jobs.genesistherapeutics.ai/apply/ILdjypU87G/Softwar...

- AI Engineer: https://jobs.genesistherapeutics.ai/apply/NAtx0CsIIL/AI-Engi...

Tech Stack: python, pytorch, postgres, docker, kubernetes, various computational chemistry libraries + tools

Please apply online, or email me your resume: ben@genesistherapeutics.ai

To clarify, I believe in allocating work based on competence, and even incentivizing such behavior when necessary with some monetary reward, which our current markets do an okay job at.

By “meritocracy” I mean the idea that it is morally just for people to be rewarded in money or social status proportional to their economic usefulness — an idea most people implicitly hold to some degree and which some libertarians trumpet as a core tenet. I think the implied inverse to this belief — in the extreme, that lack of economic usefulness deserves poverty — is morally repugnant and dangerous.

Great article that exposes many college-educated blind spots, in particular that education is not a panacea for inequality. But the proposed solution of more democratic workplaces — somehow convincing capital-holders that investing in human labor rather than automation is in their own self-interest — feels like swimming against the tide of history.

Meritocracy is a dangerous ideal for humanity to continue to hold so dearly. IQ is partially genetic, and with a low ceiling — sooner than we’re ready to accept, nobody’s brain will hold a candle to AI. Equating our economic value with moral worth will only lead to further cultural division, despair, and anarcho-primitivism. Instead, let’s try to build a culture, society, and government that takes the intrinsic value of human life as an axiom, before it’s too late.

1. LEO satellites will provide Internet access worldwide. About 90% of the world's population will be online by 2030.

2. Drastic climate engineering will become a mainstream research area and popular controversial talking point.

3. Many more people will get blood tests regularly, for early disease detection and fitness monitoring. Both a Theranos-like fingerprick device and a tampon-based screening company will IPO.

4. AI will be a crucial component in discovering multiple clinical trial-stage drugs.

5. A fully AI musician will go on tour, perhaps with its own lyrics and deepfaked vocals. It will be particularly good at improvisation.

6. To combat deepfakes, public ledger-based verification of photos and videos will become common but not ubiquitous. May or may not be on blockchain.

7. Cryptocurrency-based financial instruments will become a minor part of the global financial system, widely accepted and taken seriously by traditional investors.

8. Metal 3D-printers will start to seriously compete with CNC machining and expendable casting, they will be relatively common in machine shops and factories. Consumer 3D-printing will remain a niche hobby.

9. Flying cars won't really take off.

Genesis Therapeutics | South San Francisco, CA | Onsite | Full-time | https://www.genesistherapeutics.ai

We’re a hybrid AI + biotech company, developing novel neural networks to predict molecular properties and using them to accelerate the development of new medicines.

- Core deep learning tech was invented by co-founder + CEO Evan Feinberg during his PhD at Stanford’s Pande lab (the lab that did Folding@Home). See the peer-reviewed PotentialNet paper: https://pubs.acs.org/doi/10.1021/acscentsci.8b00507

- Recently raised a $4.1m seed round led by Andreessen Horowitz

- Our platform was validated in collaboration with a top-five pharma company, in a public paper: https://arxiv.org/abs/1903.11789

- In addition to strong software + AI talent, our small team has top drug discovery chemists who have collectively discovered several FDA-approved drugs before

Here are our open roles:

- Software Engineer: https://jobs.genesistherapeutics.ai/apply/ILdjypU87G/Softwar...

- AI Engineer: https://jobs.genesistherapeutics.ai/apply/NAtx0CsIIL/AI-Engi...

Tech Stack: python, pytorch, postgres, docker, kubernetes, various computational chemistry libraries + tools

Please apply online, or email your resume to: ben@genesistherapeutics.ai

Genesis Therapeutics | South San Francisco, CA | Onsite | Full-time | https://www.genesistherapeutics.ai

We’re a hybrid AI + biotech company, developing novel neural networks to predict molecular properties and using them to accelerate the development of new medicines.

- Core deep learning tech was invented by co-founder + CEO Evan Feinberg during his PhD at Stanford’s acclaimed Pande lab (the lab that did Folding@Home). See the peer-reviewed PotentialNet paper: https://pubs.acs.org/doi/10.1021/acscentsci.8b00507

- Recently raised a $4.1m seed round led by Andreessen Horowitz

- Our platform was validated in collaboration with a top-five pharma company, in a public paper: https://arxiv.org/abs/1903.11789

- In addition to strong software + AI talent, our small team has top drug discovery chemists who have collectively discovered several FDA-approved drugs before

Here are our open roles:

- Software Engineer: https://jobs.genesistherapeutics.ai/apply/ILdjypU87G/Softwar...

- AI Engineer: https://jobs.genesistherapeutics.ai/apply/NAtx0CsIIL/AI-Engi...

Tech Stack: python, pytorch, postgres, docker, kubernetes, various computational chemistry libraries + tools

Please apply online, or email your resume to: ben@genesistherapeutics.ai

In general, computers are stateful (they use RAM) and execute instructions one-by-one, in order. Even when programming in a higher level imperative language like C++ or Python, we can guess with reasonable accuracy about our program’s time and space efficiency because a sequence of commands and variable assignments maps fairly well to instructions and memory updates the computer is actually executing.

In functional languages this is inherently more difficult, especially when features such as lazy execution (Haskell) are introduced. A functional programmer has to reason about how the compiler will translate their unordered, pure statements into imperative machine instructions and stateful memory accesses in order to know how efficiently their program is going to run.

The above is of course an oversimplification - CPUs are complicated, and do all sorts of tricks to run sequential instructions out of order or in parallel when possible. Still, I’ve yet to see a functional language that makes reasoning about program efficiency as easy as in imperative languages.

I personally love Haskell, just not for most large-scale applications. As others have mentioned, because functional concepts can make code more succinct and more obviously correct in some cases, we’re seeing adoption of these concepts in imperative languages. I think this is good, pragmatic progress toward programming language nirvana, and I hope to see it continue.