The lines between technology and culture are blurring. And so, it’s no longer enough to build great tech.

Whether in expressed via product design, brand, or user experience, taste now defines how a product is perceived and felt as well as how it is adopted, i.e. distributed — whether it’s software or hardware or both. Technology has become deeply intertwined with culture.3 People now engage with technology as part of their lives, no matter their location, career, or status.

founders are realizing they have to do more than code, than be technical. Utility is always key, but founders also need to calibrate design, brand, experience, storytelling, community — and cultural relevance. The likes of Steve Jobs and Elon Musk are admired not just for their technical innovations but for the way they turned their products, and themselves, into cultural icons.

The elevation of taste invites a melting pot of experiences and perspectives into the arena — challenging “legacy” Silicon Valley from inside and outside.

B2C sectors that once prioritized functionality and even B2B software now feel the pull of user experience, design, aesthetics, and storytelling.

Arc is taking on legacy web browsers with design and brand as core selling points. Tools like Linear, a project management tool for software teams, are just as known for their principled approach to company building and their heavily-copied landing page design as they are known for their product’s functionality.4 Companies like Arc and Linear build an entire aesthetic ecosystem that invites users and advocates to be part of their version of the world, and to generate massive digital and literal word-of-mouth. (Their stories are still unfinished but they stand out among this sector in Silicon Valley.)

Any attempt to give examples of taste will inevitably be controversial, since taste is hard to define and ever elusive. These examples are pointing at narratives around taste within a community.

So how do they compete? On how they look, feel, and how they make users feel.6 The subtleties of interaction (how intuitive, friendly, or seamless the interface feels) and the brand aesthetic (from playful websites to marketing messages) are now differentiators, where users favor tools aligned with their personal values. All of this should be intertwined in a product, yet it’s still a noteworthy distinction.

Investors can no longer just fund the best engineering teams and wait either. They’re looking for teams that can capture cultural relevance and reflect the values, aesthetics, and tastes of their increasingly diverse markets.

How do investors position themselves in this new landscape? They bet on taste-driven founders who can capture the cultural zeitgeist. They build their own personal and firm brands too. They redesign their websites, write manifestos, launch podcasts, and join forces with cultural juggernauts.

Code is cheap. Money now chases utility wrapped in taste, function sculpted with beautiful form, and technology framed in artistry.

The dictionary says it’s the ability to discern what is of good quality or of a high aesthetic standard. Taste bridges personal choice (identity), societal standards (culture), and the pursuit of validation (attention). But who sets that standard? Taste is subjective at an individual level — everyone has their own personal interpretation of taste — but it is calibrated from within a given culture and community.

Taste manifests as a combination of history, design, user experience, and embedded values that creates emotional resonance — that defines how a product connects with people as individuals and aligns with their identity. None of the tactical things alone are taste; they’re mere artifacts or effects of expressing one’s taste. At a minimum, taste isn’t bland — it’s opinionated.

The most compelling startups will be those that marry great tech with great taste. Even the pursuit of unlocking technological breakthroughs must be done with taste and cultural resonance in mind, not just for the sake of the technology itself. Taste alone won’t win, but you won’t win without taste playing a major role.

Founders must now master cultural resonance alongside technical innovation.

In some sectors—like frontier AI, deep tech, cybersecurity, industrial automation—taste is still less relevant, and technical innovation remains the main focus. But the footprint of sectors where taste doesn’t play a big role is shrinking. The most successful companies now blend both. Even companies aiming to be mainstream monopolies need to start with a novel opinionated approach.

I think we should leave it at “taste” which captures the artistic and cultural expressions that traditional business language can’t fully convey, reflecting the deep-rooted and intuitive aspects essential for product dev

The “hard tech” in AI are the LLMs available for rent from OpenAI, Anthropic, Cohere, and others, or available as open source with Llama, Bloom, Mistral and others. The hard-tech is a level playing field; startups do not have an advantage over incumbents.

There can be differentiation in prompt engineering, problem break-down, use of vector databases, and more. However, this isn’t something where startups have an edge, such as being willing to take more risks or be more creative. At best, it is neutral; certainly not an advantage.

This doesn’t mean it’s impossible for a startup to succeed; surely many will. It means that you need a strategy that creates differentiation and distribution, even more quickly and dramatically than is normally required

Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial. Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithms, so long as they maintain secrecy and privacy.

Intercom’s AI strategy is built on the foundation of hundreds of millions of customer interactions. This gives them an advantage over a newcomer developing a chatbot from scratch. Similarly, Google has an advantage in AI video because they own the entire YouTube library. GitHub has an advantage with Copilot because they trained their AI on their vast code repository (including changes, with human-written explanations of the changes).

While there will always be individuals preferring the startup environment, the allure of working on AI at an incumbent is equally strong for many, especially pure computer and data scientsts who, more than anything else, want to work on interesting AI projects. They get to work in the code, with a large budget, with all the data, with above-market compensation, and a built-in large customer base that will enjoy the fruits of their labor, all without having to do sales, marketing, tech support, accounting, raising money, or anything else that isn’t the pure joy of writing interesting code. This is heaven for many.

A chatbot is in the chatbot market, and an SEO tool is in the SEO market. Adding AI to those tools is obviously a good idea; indeed companies who fail to add AI will likely become irrelevant in the long run. Thus we see that “AI” is a new tool for developing within existing markets, not itself a new market (except for actual hard-tech AI companies).

AI is in the solution-space, not the problem-space, as we say in product management. The customer problem you’re solving is still the same as ever. The problem a chatbot is solving is the same as ever: Talk to customers 24/7 in any language. AI enables completely new solutions that none of us were imagining a few years ago; that’s what’s so exciting and truly transformative. However, the customer problems remain the same, even though the solutions are different

Companies will pay more for chatbots where the AI is excellent, more support contacts are deferred from reaching a human, more languages are supported, and more kinds of questions can be answered, so existing chatbot customers might pay more, which grows the market. Furthermore, some companies who previously (rightly) saw chatbots as a terrible customer experience, will change their mind with sufficiently good AI, and will enter the chatbot market, which again grows that market.

the right way to analyze this is not to say “the AI market is big and growing” but rather: “Here is how AI will transform this existing market.” And then: “Here’s how we fit into that growth.”

Software is misunderstood. It can feel like a discrete thing, something with which we interact. But, really, it is the intrusion into our world of something very alien. It is the strange interaction of electricity, semiconductors, and instructions, all of which somehow magically control objects that range from screens to robots to phones, to medical devices, laptops, and a bewildering multitude of other things. It is almost infinitely malleable, able to slide and twist and contort itself such that, in its pliability, it pries open doorways as yet unseen.

the clearing price for software production will change. But not just because it becomes cheaper to produce software. In the limit, we think about this moment as being analogous to how previous waves of technological change took the price of underlying technologies—from CPUs, to storage and bandwidth—to a reasonable approximation of zero, unleashing a flood of speciation and innovation. In software evolutionary terms, we just went from human cycle times to that of the drosophila: everything evolves and mutates faster.

A software industry where anyone can write software, can do it for pennies, and can do it as easily as speaking or writing text, is a transformative moment. It is an exaggeration, but only a modest one, to say that it is a kind of Gutenberg moment, one where previous barriers to creation—scholarly, creative, economic, etc—are going to fall away, as people are freed to do things only limited by their imagination, or, more practically, by the old costs of producing software.

We have almost certainly been producing far less software than we need. The size of this technical debt is not knowable, but it cannot be small, so subsequent growth may be geometric. This would mean that as the cost of software drops to an approximate zero, the creation of software predictably explodes in ways that have barely been previously imagined.

Entrepreneur and publisher Tim O’Reilly has a nice phrase that is applicable at this point. He argues investors and entrepreneurs should “create more value than you capture.” The technology industry started out that way, but in recent years it has too often gone for the quick win, usually by running gambits from the financial services playbook. We think that for the first time in decades, the technology industry could return to its roots, and, by unleashing a wave of software production, truly create more value than its captures.

Software production has been too complex and expensive for too long, which has caused us to underproduce software for decades, resulting in immense, society-wide technical debt.

technology has a habit of confounding economics. When it comes to technology, how do we know those supply and demand lines are right? The answer is that we don’t. And that’s where interesting things start happening. Sometimes, for example, an increased supply of something leads to more demand, shifting the curves around. This has happened many times in technology, as various core components of technology tumbled down curves of decreasing cost for increasing power (or storage, or bandwidth, etc.).

Suddenly AI has become cheap, to the point where people are “wasting” it via “do my essay” prompts to chatbots, getting help with microservice code, and so on. You could argue that the price/performance of intelligence itself is now tumbling down a curve, much like as has happened with prior generations of technology.

it’s worth reminding oneself that waves of AI enthusiasm have hit the beach of awareness once every decade or two, only to recede again as the hyperbole outpaces what can actually be done.