Talking to Machines – The Rise of Conversational Interfaces and NLP

Depending on where you’ve lived you’re probably using at least one of  WhatsApp, WeChat, LINE, or Kakaotalk. At first glance, these apps look like communication tools. They allow you talk to your friends, family, coworkers and business partners. Sometimes all at once. But messenger apps are just the beginning of a much larger trend. Pretty soon, you’ll use such apps not to talk to your friends, but to talk to machines. Chat applications will become a new interface used to consume information and services. In fact, many companies have started to move into this direction. We’ll get to that later, but bear with me for a little while.

Let me clarify what I mean by interface first. For the purpose of this post, an interface is a layer of technology that facilitates the exchange of information and services between humans and machines. The perhaps most ubiquitous interface today is the web browser. We’ve invented several layers of complex technologies: HTTP, HTML, CSS, Javascript, and many others, to make the web browser interface work well.  The same is true for mobile apps, which share a lot of the same principles. Some people argue that a browsers and mobile apps are fundamentally different, but I disagree. There are subtle differences, but you’re essentially looking at a screen and clicking or touching stuff. They’re quite similar. In both cases, we use them to communicate with other people (chat apps), find information (Google), and consume services (Amazon).

By design, all interfaces impose constraints on their users. Some constraints are good – they force us to focus on the essential, making us more efficient. But Some are bottlenecks to our productivity, preventing us from doing what feels natural to us. Take search as an example. Humans are quite good at asking questions. We’ve been doing that for thousands of years. But can you have a conversation with Google to get the information you need? No, you can’t. (Okay, except for a tiny fraction of very simple and commonly asked questions). Instead, we’ve adjusted to the interface that Google is providing us. We’re entering keywords that we think Google will be able to efficiently utilize to give us what we want. Most of us are doing this unconsciously already because we’ve been conditioned to do so. But if watch your mom, who didn’t grow up with Google, you’ll see that she has a hard time wrapping her head around what “good” keywords are, and why she can’t find what she needs. She also can’t tell spam for non-spam. Keyword search doesn’t come naturally to her.

Now, there’s an alternative interface that we use to communicate, get information and consume services almost every day: Natural Language. Natural Language is nothing more than the transmission of information, or meaning, through a medium such as sound or script. That’s also why I won’t make a distinction between speech and text-based solution in this post. They both have their place, but they embody the same thing. Language is an interface that, right now, mostly facilitates communication between humans. But you could imagine using this same interface to facilitate communication between humans and machines. Instead of putting keywords into Google you’d ask a question, potentially have a quick conversation, and get a response. Instead of clicking through a dozen app screens you’d just say what food you want to order, or tell your car where you want to go.

Just like other interfaces, the natural language interface has constraints. It isn’t suited for everything. Obviously it’s bad for visual tasks – shopping clothes for example. It also doesn’t seem so great for discovery, like browsing items on Amazon without a clear goal. But you can certainly imagine use cases where natural language excels. Asking questions to get information or conveying tasks with a clear intention – ordering a specific food item, cab, or giving directions for example. Natural Language places a low cognitive load on our brains because we’re so used to it. Hence, it feels more natural and effortless than using an app or browser.

Of course, Facebook (M), Google (Now), Apple (Siri), Microsoft (Cortana) and Amazon (Echo) have been working on natural languages interface for a while now. They’re calling it personal assistants (PAs). However, there’s another group of companies who are attacking the same problem from a different angle, but we’ve been ignoring that part of their business.  Messenger apps. These platforms are uniquely positioned to enable communication with machines. The leader of the pack is probably WeChat, which allows you to order everything from food to taxis from within the app, using a messaging as the interface. Slack is moving into the same direction by encouraging the creation of various botsLINE also has a range of robotic accounts, ranging from translation services to erotic entertainment. 

Why do I think these two groups of companies are working on the same problem? PAs follow what I would call a top-down approach. They’re trying to solve a challenging problem with a long history in AI: Understanding your intentions and acting on them. They are trying to build general purpose software that is smart enough to also fulfill specific tasks. It’s a research area with lots of stuff left to figure out. Messenger apps follow a bottom-up approach. They are starting with simple bots (“Hi, translate this sentence for me”), solving very specific problems, and are gradually moving towards more sophisticated AI. Over time, these two approaches will converge.

When it comes to the adoption of natural language interfaces, messengers apps may actually have a leg up. Here’s why. Talking to Siri on a crowded train still feels awkward, even in SF. It’s also annoying because you have to open yet another app. However, many of us are spending our time in chat apps anyway, so it feels completely natural to just add conversation with a bot. Ditto for Slack. The transition for consumers seems so much smoother. As conversational interfaces penetrate our world, voice interfaces will start feeling just as natural as taking selfies (and hopefully more than selfie sticks). But another reason for why messenger apps are well positioned is data. These companies have collected huge amounts of conversational data that can be used to train better Natural Language models. Facebook has that too, both with their messenger and with WhatsApp. Microsoft doesn’t. Amazon doesn’t. Apple may or may not, depending on how secure iMessage really is.

If you’ve actually used any of the PAs you may be skeptical. Siri still barely understands what you want, and Facebook has put hordes of human workers behind M to get it to do anything useful. How will these things ever replace all the complex tasks we’re doing apps and browsers? That’s another reason for why the bottom-up approach seems promising. It yields immediate benefits and practical applications.

But I also believe that over the coming years we will see rapid improvements in conversational interfaces. There are clear enabling technologies for this trend: Natural Language Processing (NLP) and Deep Learning. Deep Learning techniques have led to breakthroughs in Computer Vision, and they are now penetrating natural language research. Whether or not Deep Learning itself will lead to breakthroughs in NLP is hard to say, but one thing is clearly happening: Many smart people who didn’t previously focus on NLP are now seeing the potential and are starting working on NLP problems, and we can surely expect something to come out of this.

 

Deep Learning Startups, Applications and Acquisitions – A Summary

Most major tech companies are use Deep Learning techniques in one way or another, and many have new initiatives on the way. Self-driving cars use Deep Learning to model their environment. Siri, Cortana and Google Now use it for speech recognition, Facebook for facial recognition, and Skype for real-time translation.

Naturally there are a lot of startups doing cool things in the space. I tried to do my best to categorize the companies below based on where their main focus seems to be. If you’re a Deep Learning company and I forgot you, please do let me know!

General / Infrastructure

Because Deep Learning is such a generic approach, some companies are focusing on creating infrastructure, algorithms, and tools that can be applied across a variety of domains.

DeepMind, which was acquired by Google for more than $500M in 2014, is working on general-purpose AI algorithms using a combination of Deep Learning and Reinforcement Learning. DeepMind is the company behind an algorithm that learns to play Atari games better than humans. It is a largely a research company and does not provide products for use by businesses or consumers.

MetaMind focuses on providing cutting-edge performance for image and natural language classification tasks. Richard Socher, the founder of Metamind, is very active in the academic community and teaches Stanford’s Deep Learning for Natural Language Processing class. The company offers a cloud service to train Deep Learning classifiers.

Nervana is the company behind the open source Python-based neon framework, a GPU-optimized library to build Deep Learning architectures. Nervana also provides a cloud services where it runs algorithms on proprietary hardware specifically designed for Deep Learning. Nervana raised $20.5M in a June 2015 round led by Data Collective.

Skymind is the company behind the Deeplearning4j framework. Deeplearning4j makes efficient use of GPUs and integrates with distributed systems such as Hadoop and Spark to scale to large data sets. Skymind sells an enterprise editions of its software together with training and support.

Ersatz Labs offers a cloud service to manage data and train Deep Learning models through a web interface (video) or an API. Pricing is based on minutes of GPU time used.

Computer Vision

It would be fair to say that Deep Learning gained most of its popularity through excellent performance on a variety of computer visions tasks: Recognizing objects in images, understanding scenes, and finding semantically similar images. Convolutional Neural Networks (CNNs), a popular type of Deep Learning architecture, are now considered the standard for most of the above. The rapid success of Deep Learning in Computer Vision has spurred a lot of startup activity.

Madbits was acquired by Twitter in 2014  before it got a chance to launch publicly. In its own words, it “built visual intelligence technology that automatically understands, organizes and extracts relevant information from raw media (images)”.

Perceptio was acquired by Apple In October 2015 while still in stealth mode. The website was shut down after the acquisition, but Perceptio seems to have been developing technology to run image-classifications algorithms on smartphones.

Lookflow was acquired by Yahoo/Flickr in October 2013. It’s unclear what exactly Lookflow was offering, but it was using Deep Learning algorithms for image classifications to help organize photos.

HyperVerge builds technology for a range of visual recognition tasks, including facial recognition, scene recognition, and image similarity search. HyperVerge is also working on a smart photo organization app called Silver. The company came out of  IIT and raised a $1M seed round from NEA in August 2015.

Deepomatic builds object recognition technology to identify products (e.g. shoes) in images, which can then be monetized through e-commerce links. It focuses on the fashion vertical and has raised $1.4M from Alven Capital (a French VC) and Angels in September 2015.

Descartes Labs focuses on understanding large datasets of images, such as satellite images. An example use case is tracking agriculture development across the country. Descartes Labs came out of the Los Alamos National Laboratory and has raised $3.3M of funding to date.

Clarifai uses CNNs to provide an API for image and video tagging. In April 2015, Clarifai raised a $10M Series A led by USV.

Tractable trains image classifiers to automate inspection tasks currently done by humans, for example detecting cracks on industrial pipes or inspecting cars.

Affectiva classifies emotional reactions based on facial images. It raised $12 million in Series C funding Horizon Ventures and Mary Meeker and Kleiner Perkins in 2012.

Alpaca is the company behind Labellio, a cloud service to build your own deep learning image classifier using a graphical interface.

Orbital Insight uses Deep Learning to analyze satellite imagery and understand global and national trends.

Natural Language

After the rapid success in Computer Vision, researchers were quick in adopting Deep Learning techniques for Natural Language Processing (NLP) tasks. In fact, the exact same algorithm that categorizes images can be used to analyze text. Since then, new Deep Learning techniques specifically for NLP have been developed, and are being applied to tasks such as categorizing text, finding content themes, analyzing sentiment, recognizing entities, or answering free-form questions.

AlchemyAPI was acquired by IBM (Watson group) in March 2015. It provides a range of  Natural Language Processing APIs, including Sentiment Analysis, Entity Extraction and Concept Tagging.  (AlchemyAPI also provides computer vision APIs, but their primary product seems to be language-related so I decided to put them in this category).

VocalIQ was working on a conversational voice-dialog system before being acquired by Apple in October 2015.

Idibon develops general-purpose NLP algorithms that can be applied to any language. Idibon’s public API does Sentiment Analysis for English, but more languages, and  support for Named Entity Recognition are coming soon. Idibon raised a $5.5M Series A led by Altpoin, Khosla, and Morningside Ventures in October 2014.

Indico provides a variety of Natural Language APIs based on Deep Learning models. APIs include Text Tagging, Sentiment Analysis, Language Prediction, and Political Alignment Prediction.

Semantria provides APIs and Excel plugins to perform various NLP tasks in 10+ languages. Pricing starts at $1,000/month for both Excel plugins and API access. Lexalytics, an on-premise NLP platform, acquired Semantria in 2014.

ParallelDots provides APIs for Semantic Proximity, Entity Extraction, Taxonomy Classification and Sentiment Analysis, as well as tools for social media analytics and automated timeline construction. 

Xyggy  is a search engine for all data types (text and non-text) represented by deep-learning vectors. With text for example, a search can be with keywords, snippets or entire documents to find documents with similar meaning.

Vertical-Specific

Instead of focusing on general-purpose vision or language applications, some companies are applying Deep Learning techniques to specific verticals. My research surfaced mostly Healthcare companies, but It’s likely that many others are using Deep Learning without explicitly mentioning in on their website.

Enlitic applies deep learning techniques to medical diagnostics. By classifying x-rays, MRIs and CT scans, Enlitic can recognize early signs of cancer more accurately than humans. The company raised $3M from undisclosed investors in February 2015.

Quantified Skin uses selfies to track and analyze a person’s skin and recommends beneficial products and activities. The company raised a total of $280k in 3 rounds.

Deep Genomics uses Deep Learning to classify and interpret genetic variants. Its first product is SPIDEX, a dataset of genetic variants and their predicted effects.

StocksNeural uses Recurrent Neural Networks to predict stock prices based on historical time-series data.

Analytical Flavor Systems use Deep Learning to understand what people taste and optimize food and beverage production.

Artelnics builds open source libraries and graphical users interfaces to train Deep Learning models for a variety of industries.

 

Are there any Deep Learning startups I missed? I’d love to hear about them in the comments.

 

The Unbundling of AWS

(I’m using AWS as an example, but this post applies just as much to other cloud providers)

Over the years AWS has grown to dozens of different services,  providing virtual machines,  databases, monitoring and  deployment tools on-demand. Today, it would be considered foolish to manage your own Postgres/MySQL server when you can set up an RDS instance with excellent scalability and availability characteristics in a matter of minutes.

But that’s changing.

Container infrastructure is starting to provide similar abstractions and benefits: One-click deployments, load balancing, auto-scaling, rolling deploys, recovery from failures, data migration, resource usage monitoring, and more. Increasingly, I see companies moving away from cloud provider services in favor of containers and container orchestration platforms. Core services like EC2 and S3 aren’t easily replaced, but others are, and there are good reasons to do so:

  • Costs. AWS prides itself on pay-per-use pricing, but many services aren’t fulfilling that promise. For example, an Elastic Load Balancer costs a fixed $25/month, even if it receives only a few requests. A database that runs only few queries per day (like this blog) also comes with a fixed price tag. Containers are almost free – instead of paying with dollars you pay with the CPU/network resources actually used by the service. Often, that turns out to be cheaper.
  • Features. Many AWS services are based either directly or indirectly on open source projects. But these open source projects are typically more feature-complete than their AWS counterparts. An Elastic Load Balancer has a limited set of features compared to a HAProxy instance; so does AWS Kinesis compared to Apache Kafka. Even services that are running open source software under the hood (such as EMR with Hadoop/Spark) don’t typically support the latest versions.
  • No cloud lock-in: Most container orchestration solutions work across clouds out of the box. This means you can host parts of your infrastructure on AWS, Google Cloud, Azure, DigitalOcean, you’re private cloud, or whatever else is the best fit.
  • Full control: When things don’t work as expected you’re relying on AWS support for help. That can be convenient, but usually it’s faster to debug a problem yourself. That’s only possible with access to the internals of a service, something you don’t have with hosted solutions. What if there’s a simple feature that’d take a small configuration change or a few lines of code to implement? AWS support can’t do that for you. With containers and open source software you can.

Just like Craigslist has been unbundled by purpose-built websites, it seems natural to (not only) me that cloud providers like AWS will be unbundled by purpose-built open source software. In a way that’s ironic because the value proposition of AWS is the exact opposite – bundling  open source software in a centralized place and giving them a consistent look and feel. Up until now we didn’t have the right technology to enable unbundling of PaaS solutions. It’s only recently that container infrastructure and orchestration are becoming mature enough to make this possible.

 

A Few Tips To Make Distributed Teams Work Well

An increasing number of startups seem to be building distributed teams, particularly in engineering. But making a distributed team work well isn’t easy. From what I’ve seen most distributed teams are less productive than their centralized counterparts. Here are a few things from my own experience that I think are crucial to the effectiveness such teams. If I had to summarize all of the below I would do it as: Remove synchronous communication, and when in doubt, over-communicate.

Hire people who thrive in remote environments

An otherwise excellent hire may underperform in a distributed environment. This has nothing to do with skill, it’s mostly a result of past experience and personality. Some of us enjoy the social atmosphere and tighter supervision of an office environment. Others thrive with little or no supervision away from all distractions. I found that the latter type of people tend to have experience with starting or managing their own projects, e.g. an open source project, side project, or startup. Explain the company’s goals and they will figure out the little details themselves, without the need for a lot of meetings. Look for these people.

Be proactive, not reactive, in giving access to resources

Communication in a remote environment is asynchronous, so your goal should be to minimize the need for synchronous communication. For example, a developer may be blocked by not having access to a code repository or SaaS service the company uses. In an office environment this isn’t a big deal, she can just walk over to a manager or teammate and get access. In a remote environment she may have to wait several hours to get it. If this happens frequently it adds up to a lot of lost time. Be proactive in giving everyone the resources they need to make their own decisions.

Don’t be a hybrid

It’s not uncommon to simultaneously run a distributed team and a “core” team in a centralized location. This is appealing, but it’s also difficult to get right. It only works if everyone follows the same procedures. It’s tempting for members of the core team to make quick decisions through in-person meetings and neglect discussing them with people who aren’t there. If everyone talks in Slack then the core team should do the same. Not doing so will result in an imbalance of information, or worse, frustration of people who are not “in the loop”.

Focus on process, not outcome

As long as a centralized team is small you can run it without a lot of formal procedures. Not so with distributed teams. An example for engineering teams would be creating formal procedures for Pull Requests. What should be in the title/description and acceptance criteria? Who should review/merge, and when? Defining all of this formally seems like overkill, but in a remote environment it ensures that everyone is on the same page and knows what to expect. More generally, your goal shouldn’t be to ship the next version of your product as quickly as possible (though that’s a nice side effect). It should be to build a scalable process , get out of the way, and make your team productive without requiring a lot of supervision. Formal processes help do that.

Have the right technology stack in place

There’s lots of software that makes working in a distributed team easier. It would be foolish not to use it. Use Slack for team communication. Blossom or Pivotal Tracker for task tracking . Screenhero for screen sharing. Google Hangouts for meetings. And so on. Of course the above are just examples, use any software that meets your needs. Again, make sure that you have processes in place that define how to use your software stack (e.g. how to manage and reviews tasks).

Get everyone on the same page

I found that many inefficiencies in distributed teams stem from the fact that team members aren’t on the same page about company priorities, or that they don’t know what everyone else is working on. This is absolutely crucial. Processes that I’ve found effective include using company and team OKRs, regular standups (either in Slack or via video chat), and doing weekly retrospectives of what has been accomplished, what didn’t go so well, and what the goals for next week are.

Consider Transparency

Buffer is probably the best example of a company that’s incredibly transparent. Transparency works well with distributed teams because it removes the need for communication. If something is open to everyone, employees don’t need ask around for access. You don’t need to become as transparent as Buffer is, but it’s worth considering what you could be transparent about both publicly and internally.

 

Why Startups Really Succeed: Strings of Luck

Unicorn Cafe

Luck plays a huge role in everything we do, and where we’re born is the perhaps biggest lottery of our lives. But acknowledging luck makes us feel uncomfortable. Our brain seeks causal stories, and tries to create them from whatever information is currently available. This helps us maintain the illusion that the world is an orderly place we have control over.

Startups are no exception. The stories of those that succeeded, and the post-mortems of those that failed, are always causal stories. In the case of success they are typically stories about visionary founders in a fast-growing market pursuing an idea at just the right time. That’s exactly the kind of story that appeals to our brains (and the press). There’s no mention of luck. Surely, if we could turn back time, and those founders were to start the business again under the same circumstances, it would also succeed, right?

That’s an illusion.

We tend to overestimate the influence that founders, or any element we can control, have on the outcome. I am not discrediting the hard work of startup founders. The intelligence, resilience, resourcefulness, and optimism of the founders certainly play a big role in the success of a startup. But I believe that it’s a required and not a sufficient condition. Let’s take Airbnb as an example. Paul Graham writes:

Airbnb now seems like an unstoppable juggernaut, but early on it was so fragile that about 30 days of going out and engaging in person with users made the difference between success and failure.

There are an infinite number of events, from family problems to legal issues, that did not happen but would have resulted in Airbnb going out of business at some time during its inception. A chance encounter with someone offering an attractive job to the founders would probably have been enough (the founders started renting out mattresses because they couldn’t afford rent in SF). It was lucky that none of this happened.

The combined absence of all events that would’ve resulted in the founders shutting down Airbnb was very unlikely. Similarly, there were a few crucial (lucky) events that had a large impact on Airbnb. What if the initial 2 customers had never seen the website? What if nobody ever recommended that the founders take prettier pictures of the listed places? You can come up with similar examples for most other billion-dollar startups. Google almost sold their company for $750k in 1999 and just barely escaped death.  All companies are fickle in their early days, and it’s usually a stroke of random events that leads the founders to continue instead of shutting down or prematurely selling the business.

In Thinking Fast and Slow, nobel-winning Daniel Kahneman puts it well:

 Narrative fallacies arise inevitably from our continuous attempt to make sense of the world. The explanatory stories that people find compelling are simple; are concrete rather than abstract; assign a larger role to talent, stupidity, and intentions than to luck; and focus on a few striking events that happened rather than on the countless events that failed to happen.

This also gives us the top reason startups fail: Because it’s the default action. In the absence of continuous random events that keep a startup alive there are just too many things that can go wrong, and too many seemingly better opportunities the founders could choose to pursue. Statistically, it is more likely that something leads to the (voluntary or involuntary) shutdown of a startup than it is that everything goes just according to plan. That’s the reason VCs don’t focus on “Will this startup succeed?”, but on “If this startup succeeds, how big could it be?” Some have recognized that there are just too many variables to consider, and that it’s impossible to predict the future of a startup.

The reason so many successful startups come out of Silicon Valley is because it’s a numbers game. SV has the highest concentration of startups anywhere in the world (maybe even more than the rest of the world combined). People move to SV to start risky companies. Statistically it should come as no surprise that most successes start here. To avoid sounding like a hopeless pessimist I want to clarify that  I am not saying that all the other factors (culture, available of talent, etc) are irrelevant. It’s just that we tend to overvalue them because they make for good stories.

Optimism, or blissful ignorance, could be called the secret sauce of startup founders. Being relentlessly optimistic leads the founder to make the (irrational) decision of continuing with their startup when they could be pursuing an opportunity with a higher expected value. And given the large number of samples, this works out just fine in Silicon Valley.

 

The human side of technical debt

When we talk about technical debt we typically talk about its business impact. It allows us to gain short-term efficiency at the expense of long-term productivity. Technical debt isn’t always bad. Sometimes it’s the right business decision. When running experiments that may or may not become part of a final product taking on technical debt is often a good idea. If the experiment doesn’t work out we can throw away the piece the incurred the debt and there’s no need to “pay it back”.

But technical debt has side effects that we often forget about. Developers hate working with technical debt that isn’t their own. Nobody likes cleaning up somebody else’s mess. Whenever a developer touches code or infrastructure plagued by technical debt she is likely to feel frustrated and demotivated, and that feeling will spill over to other aspects of her work. This effect on human motivation is hard to quantify, but extremely important to consider.

Then there’s the cascading effect. The presence technical debt increases the probability of developers adding more technical debt in adjacent components. It’s messed up anyway, so adding a bit more won’t hurt, right? Individual developers often make such decisions on the spot, and the result can get out of hand rather quickly.

The price of technical grows with number of people and the number of components that touch it.  We need to think of its impact in terms of people, not just code or infrastructure. As much as possible debt should stay confined to isolated components that are “owned” by individuals or teams who are responsible for managing and paying back the debt over time.

 

Being in Silicon Valley as a Founder – Pros and Cons

Many founders working on early-stage startups seem to want to move to Silicon Valley. The biggest hurdles are usually visa and money. But is moving to SV actually worth it? I’m not a SV veteran, I’ve only been here since 2008, but over the past years I’ve spent a fair amount of time living outside of SV. I believe this helped me gain some perspective on what Silicon Valley can and cannot provide to founders.

Common Misconceptions

Let’s first look at things that people believe to be advantages of being in SV, but which in my experience also have a flip side. I see these as not inherently positive or negative, but depending on your situation they may be worth considering.

Raising (seed) money is easy. It’s true that the amount of capital available in SV is huge, but raising money is not as easy as you may think (or as the press makes it out to be). One reason for this is that lots of startups are competing for the money. Expectations of what companies are required to show are constantly shifting. Manu Kumar has an excellent post on how seed rounds are looking more like series the A rounds a couple years ago. To have a shot at raising a seed round in Silicon Valley you better have a post-MVP product with traction and promising metrics. Having a pitch deck or an MVP without customers doesn’t usually cut it. So, SV is a great place to raise money only If you’re at the right stage.

Lots of events and communities. You can attend startup mixers, meetups, hackathons and pitch competitions all day long. You’ll have beers with founders who are usually looking for co-founders or trying to sell their product. In my experience going to these events is mostly a waste of time. The time is better spent building your product and talking to users. Also, don’t expect to meet any influential people at such events. Most of them are too busy to attend and have realized that there is little value to be gained. 

The best talent is in SV. Silicon Valley companies and famous bay area universities attract people from all over the world. True, but that doesn’t immediately benefit you or your startup. Sought-after people have dozens of excellent opportunities and you would need to offer something pretty special to attract someone to your company over the other gazillion startups out there. Expect to pay salaries or hourly rates that are 3-5x above the rates in other parts of the country (or world). You may actually be able to attract better talent in areas where people don’t have as many options as they do in SV.

Positives

Karma really exists in SV. I’m constantly amazed at how helpful people are, particularly those that I expected would be too busy to reply to me. I’ve asked favors of influential people who had absolutely zero reason to talk to me, but they still did. This is something that newcomers, in my experience especially those from a sales background, seem to have a hard time getting used to. They try to strike a deal whenever someone asks them for a favor. I think this culture is pretty unique to SV and I haven’t seen anything comparable in other cities or countries.

Selling to startups is easy. I often hear people discouraging startups from selling to other startups. I disagree. It’s bad if your total available market consists of startups only, but there is nothing wrong with having startups as early adopters and then moving upmarket. In accelerators like YC it’s extremely common that startups start out selling to the other cohort and alumni companies. There’s no better place to sell to startups than SV. Getting meetings is relatively easy, and companies are open to trying out and implementing new solutions, helping you to iterate quickly on your product.

It’s easy to relate to people. Living in other parts of the world I found it very difficult to explain what exactly I am doing to others. In some places I got tired of explaining and settled with “I’m an engineer and work for a software company” or something similar. Most people just aren’t familiar with the concept of a startup. In Silicon Valley you could probably strike up a conversation about churn rates with your barista (not that I’ve tried…) and she’ll tell you that she’s working on her own mobile app during break time. Knowing that people understand what you are doing is a very comfortable feeling. Not being able to talk to anyone about your problems can be quite depressing.

Negatives

High burn rates. Medium rent for a 1BR apartment in San Francisco is about $2,500 to $3,500 (source) and increasing. There are cheaper options, such as living in the east bay, but since almost all meetings happen in SF you would be commuting all the time. That time/money may be better spent on your product and commuting is among the most significant factors of work and life dissatisfaction. If you’re in the early stages and haven’t raised a big chunk of money then these are serious expenses. Startups are a marathon, and lowering your burn rate may just make the difference between making or not. The Airbnb story is a good example of this.

An influx of people who want to make a quick buck. Even in the relatively short time I’ve been in SV I’ve seen the crowd in SV change. Many of those who used to go into Finance are now going into startups hoping to make a quick buck. I’m all for more people getting into the startup ecosystem and bringing positive change, but it also seems like startups in SV have become a more hostile environment as a result. Legal disputes between founders and people trying to trick each other with strange non-standard equity clauses seem to become more common. As startups are entering mainstream media the situation will likely worsen, and SV is the place that will feel it the most.

High Pressure. At Stanford we have something called the “duck syndrome”. On the surface it looks like you are gliding along effortlessly, but in reality you’re vehemently paddling underneath. Talk to any startup founder in Silicon Valley and they’ll tell you how they’re “crushing it”.  In reality most founders work crazy hours and things aren’t going nearly as well as it may seem. But you’ll never find out about that. As humans we tend to compare ourselves to those around us. In SV this can lead to extreme stress and pressure. Founder depression is common and not to be underestimated.

For anyone who has made the move, I’d love to hear your experience.