Silicon Valley – An Operator's Blog

The “Mission-Pitch”

To break through AI noise in the Bay Area right now, figure out your “why should anyone care?” pitch.

Important tip for international founders who have recently relocated to SF and are looking to build their networks here for customers & fundraising:

As you meet new people, it’s important to have an abstracted-out, 10-20 second mission pitch that clearly outlines “why should anyone care?”.

More than market analysis, facts & data, this pitch should have a strong underlying emotion that can immediately connect with someone who might have an overlapping world view.

There is immense noise in the Valley right now, and every space/ vertical has tens of startups going after it. All pitches sound similar, most founders have similar backgrounds, and all content looks the same.

Breaking through this clutter is hard, especially for folks who don’t have a high-signal, prior track record in the Bay Area.

In these cases, dialing up the personal authenticity quotient big time, and having a clear “Mission-Pitch” with a strong emotional pull can be extremely helpful in winning over new relationships.

In an ecosystem where every decent startup is flush with capital and early traction, founders need to 1) go deep, 2) go sharp, and 3) manage the psychology of market participants in order to stand out.

AI Ecosystems – Silicon Valley vs India

During my recent India trip, a question I got asked repeatedly by both founders & investors was, “What are you seeing as the main differences between the AI ecosystem in the Valley vs India?”.

I currently see 2 main differences:

1/ Exposure (& therefore, Ambition)

AI founders in the Valley seem to have significantly more direct exposure to the work happening at the frontier. And not just in terms of the foundational technology, but also what battles the incumbents are taking on, how workflows are being iterated on, what lean, full-stack startup teams are doing to be able to generate significant product velocity & revenue, and how customers are thinking & allocating resources.

Essentially, they have the advantage of directly drinking from the Bay Area fountain of knowledge & information, spread primarily via networks.

A direct consequence of more exposure is that it uplevels the ambition of Valley AI founders and organically pushes them to raise the bar for execution within the company. Thus leading to sharper thinking, more courageous bets, and faster execution that all put together, improves the odds of a large outcome.

2/ Story-telling

I see that while AI founders in both the Valley and India are picking very similar problem statements to work on, the storytelling around the same use cases in the Valley is significantly superior.

I guess one reason is that operating directly in the target market (vs being a few degrees of freedom away from it) makes it much easier to get higher-quality early validation signals, making the story much more believable.

Also, AI founders in the Valley tend to emerge from the leading-edge companies of the last mobile/ cloud/ SaaS cycles. So they have a much better intuitive understanding of how to position & message the company in the early days to customers, investors & key hires.

Story-telling becomes even more important as how the AI landscape will evolve in specific market segments & verticals remains highly fuzzy.

So, what can India-based AI founders do to bridge these 2 gaps? Here are a few actionable things:

1/ Do extended sprints in the Bay Area regularly to drink from the same fountain.

2/ Surround yourself with Bay Area-based operators, angels & advisors (even remote is ok to begin with) who can regularly feed this knowledge & intel and, more importantly, help uplevel your thinking & ambition.

3/ Follow a conscious 0-to-1 strategy of only building for US design partners, so your product is held to the same bar as those from Valley startups.

4/ Specific suggestion for VCs – mine your network of LPs, Advisors & Portcos to hold regular AI knowledge sharing sessions with leaders of marquee AI-native companies that are building on the frontier in the Bay Area.

AI Musings #7 – latest on AI in the Valley

Given the exponential rate of change in AI, Dev Tools appear to have the most risk of use case durability, compared to Infra and Applications.

Earlier this week, I attended the AGI Builders Meetup in San Francisco. The event had 5 product demos ranging from new AI features by Twilio to a YC S24 startup building an AI agent to handle calls on behalf of users.

These demo-based events are always helpful for me to track the latest in AI. Also ended up chatting with a few engineers and founders attending the event, to get their thoughts on what they are seeing in their respective domains within AI.

One key insight I got from this event was that LLMs aren’t the real future of AI. No one really knows what’s going on inside them. They hallucinate much more than desirable (especially for accuracy-driven enterprise use cases). They are prone to prompt injection hacking.

In fact, the presenting Twilio PM said that “working with LLMs is like getting toddlers to do something”. They don’t take instructions promptly. You don’t know what’s going on inside their heads. You have to proactively ensure their safety as they are doing a task. I found this framing really interesting.

Building on this further, I have an updated (but still working) POV on the 3 buckets of AI – infra, applications, and dev tools.

#1 Infra

Irrespective of where AI goes, hardware infra like GPUs will always be needed. Hence, it makes sense that Nvidia is doing so much capex.

Also, Big Tech software infra players (Microsoft, Google, Meta, AWS, etc.), as well as AI-native hyperscalers (OpenAI, Anthropic, etc.), will continue playing a key role in defining where AI goes from here.

Sadly, as a micro VC, I can’t play much in this bucket (except personally investing in the public markets).

#2 Applications

Application layer founders that are starting up today are leveraging the capabilities of AI from Day 0 to solve customer problems. Their core focus still remains commercial-first – using the best-available software capabilities to solve customer problems, rather than getting overly enamored by the research aspects of AI and where it’s headed.

In a sense, these startups are centered on customer problems, not AI per se. Wherever AI ends up going, LLMs and beyond, these founders will leverage whatever capabilities they can get their hands on, and modify their architectures accordingly.

As an investor, the key is to back founders who are starting up now with an AI-first mindset and therefore, are fresh enough and agile enough to keep evolving their software as the underlying AI capabilities evolve.

Therefore, it’s reasonable to expect that these AI-native application startups should be fairly resilient to changes in the overall AI landscape. Hence, I feel reasonably comfortable in backing them (eg. portcos like Confido Health, Loop, and Soulside).

#3 Dev tools

This is the bucket I am most confused (and concerned) about. From seeing these demos, it seems like dev tools startups are essentially using the mental models of the previous cloud & mobile waves to make assumptions on use cases.

Further, I have observed that many of them are solving short-term, immediate pain points that could easily become irrelevant due to where AI goes from here, and/ or from the competition (eg. open source alternatives, AWS quickly launching it as a feature, etc.).

As I was seeing these demos, I looked up how much capital some of these companies had raised. Many of them have raised anywhere from $10-35Mn. The capitalization of these companies seems out of sync with the durability of their underlying use cases and revenue.

Essentially, what all this means is that I have a macro “Why Now?” question around the AI dev tools bucket. A top Bay Area engineer who recently left a cushy Big Tech job to start up was recently saying – “Given how things are changing every month, I am really not sure what to build right now”. I feel this is an intellectually honest view, rather than a FOMO-based approach that many VCs are taking.

Based on what AI practitioners like this person are saying about the exponential rate of change in AI, I fear that a majority of these dev tool use cases won’t endure.

Again, this is just my working POV. Would love to hear your views on what you are seeing.

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How Silicon Valley startup narratives fool us

One big realization I have had as a founder over last year or so — all Silicon Valley startup narrative is post-facto. Both founders and media conveniently don’t include the real “initial phases” of the company. These include things like the 2nd co-founder getting “recruited” much later, an old services biz revamped to appear like a fresh startup, an advisor/ angel joining & getting co-founder status, taking on a product that was in reality, built by other devs who didn’t see value in it etc. These inconvenient and scrappy realities are glossed over, to paint the narrative of a smooth curve — 2 co-founders, one engg. and one biz, met in Ivy league or top tech co., fell in love with same idea, launched, raised, scaled…done deal.

Till very recently, I had no idea that 1) Travis isn’t the original founder of Uber, but was an advisor to the original devs who created it and then later, saw the potential and hopped on, or 2) Elon Musk isn’t the original founder of Tesla, but had led the Series A round.

A bad side-effect of this managed PR is that new founders take all these narratives as playbooks. So, either they try and forcibly recreate it, or give up altogether once they don’t see a similar narrative coming together. Established founders & investors also don’t call this out.

Going forward, we should always try and peel the onion on startup PR narratives. Actively look for bias by asking critical questions like who is writing the story or Medium post, and what incentives are at play. Talk to operating people to get the real execution insights on these companies.

In today’s age of rapid news cycles, planted news, overzealous investors and internal PR teams, it’s foolish for founders to base our strategies and critical biz/ life decisions on what the media is telling us. In most cases (based on what I see), startup media stories are biased to tell the “curated truth”, with a specific end-objective in mind. As founders, let’s be smarter in digesting & acting on them.

Understanding Fixed vs Variable Costs as a Founder

To be capital-efficient as a founder (also applicable to life, in general), when evaluating various cost line items or taking on a new cost, have a clear understanding of “Fixed” vs “Variable”. Variable Costs are driven by your intended “velocity” and therefore, can be controlled during tough times via a frugal approach (cut variable marketing spend, let go of expensive contractors etc.). Fixed Costs don’t care about your velocity and will keep eating you up (housing rent/mortgage, office space, full-time salaries etc.). They are much harder to control, given they reflect a certain baseline you have up-leveled your startup (or life) to. Paring down Fixed Costs will require more drastic down-leveling, including completely letting go of certain assets or experiences.

The issue with Bay Area startup environment today is extremely high Fixed Costs (housing, child-care, salaries etc.). These are uncorrelated to the actual state or momentum in your startup so founders have no choice but to live with them. You can’t be frugal with Fixed Costs beyond a point, as they are driven by the external environment, not the choices you make. This, in a nutshell, is the real challenge facing Silicon Valley founders.

Here are some ways to proactively manage your startup’s Fixed Costs at early stages of the Company:

Explore building a non-Bay Area distributed team — to balance output with salary costs, at least until you see the business momentum required to support Bay Area salaries.
Be generous with equity, (relatively) tight with cash — I know this is a hard one, especially while hiring engineers in today’s market. But as founders, we need to be disciplined about this. I would rather wait out for the right candidate who believes in aligning incentives with the real situation of the startup. For instance, if someone is asking for high cash compensation in a pre-PMF startup, this means they are not the right fit for this stage. I am all for doubling-down on higher equity, even higher than market standards, for early risk-taking hires. But every $ of cash being paid out needs to have a solid justification. Anyone who seriously wants to join a really early stage startup, needs to understand and appreciate this viewpoint.
Try converting Fixed Costs into Variable Costs — some ideas could be paying sales people more on % of sales commissions and less on fixed; going for an “on-demand” co-working space with elasticity to quickly scale up/ down; keeping specific functions eg. designers, content writers etc. (these functions need to be chosen really carefully) on contract per “as-needed” basis, instead of full-time etc.
Be frugal on G&A — optimize costs on office space, service providers, vendors, food etc. In particular, Bay Area startups have a tendency to splurge beyond their means on fancy office spaces, lavish off-sites, dinners at marquee restaurants, expensive swag etc. These non-core costs tend to add up and hit your budget more than you might realize.
Leverage free ways of brand-building — instead of spending tons of $$ on brand marketing to drive early awareness (eg. conference sponsorships, which are essentially Fixed Costs), leverage free channels such as blogging, building a community on social media (Twitter, LinkedIn, Quora etc.), podcasts, creating a compelling website, white-papers, research articles, invited speaker slots etc. Early stages of a startup are all about cost-efficient marketing. This can only happen when founders focus on the above channels to build their startup’s brand, their personal brands as well as communities around their product. Austen Allred, Co-founder and CEO of Lambda School, is doing this very smartly.

Would love to hear what ways of Fixed Cost management have worked well for your startup.

More investors than operators in Silicon Valley?

Recently, Brian Armstrong (Co-founder and CEO of Coinbase) did a tweetstorm on how there are way too many investors, vs builders/ operators, in Silicon Valley. And how founders in their prime-age are opting to become full-time investors, rather than starting-up again, even after a relatively small exit.

Brian makes some good points about a trend, though short-term in my view, that even I am seeing in the Valley. Here are my thoughts on why this is happening and how it will eventually get corrected (I did my own tweetstorm with these views).

I think this is a side-effect of the last decade of over-liquidity across markets. Companies got over-funded, assets got over-paid for, specific skillsets (mostly engineers) have gotten astronomical salaries relative to their skills & experience. Also, there is no inherent entry-barrier to becoming an angel/ seed investor, provided you have “some” liquidity, especially as cost of starting businesses has come down a lot and early rounds have gotten increasingly syndicated/ fragmented across multiple small investors.

Given excess liquidity, a person who ordinarily would have been an individual angel, is now getting a shot at raising a small fund. While institutions would still keep a high bar, there are enough friends/ colleagues/ relatives willing to commit funds to ride the tech gravy train.

So am not surprised that many are jumping on the professional investing bandwagon, instead of starting-up/ operating companies. What many wouldn’t realize is:

This asset class has a 10 year feedback loop. You might end up concluding after a decade, that you aren’t really that good as an investor.
While raising a “small” first fund from personal well-wishers is relatively easy, scaling up to 2nd fund and beyond, esp. getting institutions to buy-in, is much harder.
Once you raise other people’s money, you are locked-in for many, many years. Not easy to switch career tracks.
Unlike a startup, it’s really hard to “pivot” or “reset” a fund. If your thesis/ strategy turns out to be faulty, or your Partner team chemistry doesn’t work out for some reason, you are still going to be stuck with these mistakes for a relatively long period of time.
In professional investing, showing commitment & consistency over a long period of time is critical. Yet, this is really hard to do, especially when you have decades of your career ahead of you.

In the end, market forces will weed out short-term players and restore balance. This could start once the current boom cycle turns around and liquidity becomes tight.

My personal philosophy — the world is shaped by “Builders”, not “Investors”. Why would you want to be a full-time cheerleader, when you can play the actual game?

Tag: Silicon Valley