Month: February 2026

Software is Dead, Long Live Software

Is software dead?

Today, writing software is no more difficult than pressing a button. You describe what you want. In a few minutes, not a mockup but a fully functional application is ready to use.

I can testify to this personally. In 15 minutes, using AI, I have “written” more software than I did in a full year when I was writing software professionally. Although my old skill is now obsolete, it is wonderful because I can build faster than I ever could. This is the best of times!

So yes, in a narrow sense, the old software opportunity is dead.

The writing has been on the wall for a while. Shallow tech software has been democratized and, in many cases, is not investable. Public markets have finally figured out that a new wave of software is coming. They just do not really know what it is yet, so they sell indiscriminately. Generic business and financial skills do not work during a paradigm shift because disruption does not show up neatly on a spreadsheet full of ARR, EBITDA, and CAGR. Those are the wrong questions to ask when the underlying rules are being rewritten.

At the same time, the early phase of a paradigm shift is often the best time to invest. The people who have new specific knowledge and the courage to build for an AI native world will have a clear edge and, if they are right, capture outsized returns.

Now here is the twist.

When the cost of X collapses, the world does not get less of the thing. It gets flooded with it. That is Jevons Paradox in action. Make something cheaper and easier, and overall demand goes up significantly, often faster than the drop in price. We have seen versions of this before as humanity adopted electricity, personal computers, the internet, and now intelligence.

So software is not dead. We are about to have 10x, 100x, maybe 1000x more software than we have today.

We have seen a similar movie in content. Thanks to the internet and mobile devices, as the cost of content creation and distribution dropped, the amount of content exploded. That created giants that seized the opportunity. Fun fact, I co-founded a business two decades ago on that thesis and rode that wave myself, so yes, I have been there and done that.

Back to software.

The question now is how to capture the opportunity when the world has 1000x more software and the cost of creating software is approaching zero. Inevitably, the business model shifts because we move to a different part of the price elasticity curve when software becomes abundant. When code becomes cheap, value migrates to what stays scarce.

Shallow tech, run-of-the-mill software companies, including a lot of AI wrappers, are generally not investable from a VC perspective because they are so easy to build, copy, and replace. I have been saying this for many years, even before ChatGPT came out. If you still need more evidence, you are already behind. The button is not coming. The button is here.

This does not mean these companies cannot make money. Some will. But “can generate cash when bootstrapping” and “can return a venture fund” are not the same statement.

In contrast, deep tech software is a fantastic opportunity. There is a reason TSF shifted to deep tech investments years ago. That was not an accident. When the cost curve of intelligence collapses, businesses whose primary moat is “we can write this software” or “we spent 100 engineer years building it” need a rethink.

This is why we are unapologetically investing in deep tech.

Deep tech software is a completely different sport. In many cases, the moat is not in the software. The moat is the unique technology embedded in the software, plus the data and the system it connects to. The software is the container. The defensibility sits underneath.

People often ask how to draw the line between deep tech software and everything else. We have a definition, and it is more true than ever in this “software is abundant” era. More importantly, making that call takes specialized skill. That is why deep tech investing is reserved for trained eyes, as it requires engineering judgment, product instinct, operating experience, and recognition of a market gap that comes from building and commercializing disruptive opportunities. We can do deep tech because we are equipped to do so. Been there. Done that.

To be clear, of course, I am not suggesting the only software opportunity is deep tech. There is also a massive opportunity in bespoke software and disposable software.

For decades, companies bought off-the-shelf software because that was the only option that made economic sense, even when the software was not a perfect fit for their workflow. You ended up customizing your workflow around the software. Bespoke-built software was too expensive, too slow, and too hard to maintain.

Now the economics are changing.

We can now build software for problems that were previously too small to matter economically. We can now create personal tools designed for an audience of one. We will ship internal workflows the way we send emails. We can now generate software that lives for a week, does its job, and disappears.

That is a massive opportunity. Much of it will look like a low-tech, large-scale service business. Some of it will become platforms and infrastructure for software generation itself. Some of it will become entirely new categories we do not have names for yet. Some of it will help make deep tech software even more defensible.

But the direction is clear. Software is becoming abundant, and the economics of software will be drastically different.

So, is software dead?

Yes, software as a scarce craft is dying.

Software-as-a-moat because “we spent 100-engineer-years building it” is dying.

But software as leverage is exploding. Software as the fabric of everything is exploding. The world is not losing software. The world is getting more of it than we can possibly imagine.

Back to the movie analogy. It is like the theatre business. The movie is not the only product. The experience is the product. The popcorn is the product. The atmosphere is the product. The movie is what gets you in the door.

The winning recipe has changed forever.

Software is dead. Long live software!

Portfolio Highlight: Zinite. Speed and Energy, Two Birds, One Stone

For most of semiconductor history, progress was a simple loop. Shrink transistors. Fit more into the same area. Get faster compute as a byproduct.

That loop had a name. Moore’s Law. It traces back to Intel co-founder Gordon Moore. He observed in the 1960s that the number of transistors on a chip, and hence its capabilities, tended to double every two years. The industry turned that observation into a roadmap. It was never guaranteed to run forever. Now shrinking is harder because we are starting to hit many limits in physics and economics, and the cost of pushing the frontier keeps rising.

So if the curve is going to keep bending upward, the industry needs new scaling vectors beyond making everything smaller in two dimensions.

This is why Two Small Fish invested in Zinite in 2021 at the company’s inception. The thesis was simple then, and it is still simple now. Scale in the third dimension, using proprietary technology protected by patents to enable true 3D chips.

Zinite stayed deliberately stealth early on, focused on building the core and protecting it properly before saying too much. Five years after we invested, we can finally talk about it more openly.

The company is led by its CEO, Dr. Gem Shoute. Fun fact. Her breakthrough was strong enough that her professors and industry veterans (who helped create fundamental IP used in all chips since 2008) joined her as co-founders, Dr. Doug Barlage and Dr. Ken Cadien.

The Distance Tax

In a recent blog post, I used a factory analogy to explain why speed, latency, and energy are often bottlenecked by movement, not necessarily arithmetic. 

In short, systems don’t lose because they can’t do math. GPUs are already very good at that. Systems lose speed because they can’t feed the math with data fast enough. 

In many systems, moving data costs far more than doing the arithmetic. When movement is expensive, speed and energy efficiency get worse together.

AI inference exacerbates the problem because the computational characteristics of AI inference workloads put a premium on memory behaviour. In many cases, the limiting factor is not arithmetic. It is how efficiently the system can move data. Bringing memory closer to logic matters because it directly reduces that movement.

Sensing fits in the same frame as logic and memory. Sensors generate raw data at high volume. If the system’s first step is to ship raw data far away before anything useful happens, it pays in bandwidth, latency, and power. The more intelligence that can happen closer to where data is produced, the less the system wastes just transporting information.

So the distance tax is one big problem showing up in three places at once. Logic. Memory. Sensing.

Why 3D Matters for Speed and Energy

When people hear 3D chips, they think density. More transistors per area. That matters. The bigger lever is proximity. Current 3D approaches to deliver more performance per area rely on advanced packaging, which is hindered by cost and the distance tax. 

If memory can live closer to logic, the system avoids transfers that dominate both performance and power. If compute and memory can sit closer to sensing, the system avoids hauling raw streams around before doing anything intelligent.

Every avoided transfer is a double win. Speed improves because stalls go down and effective bandwidth goes up. Energy improves because fewer joules are burned moving bits instead of doing work.

That is the two birds, one stone result.

Five years after we invested, Zinite is far from just a concept. The company is doing exceptionally well, and it represents the kind of platform that can extend performance gains into the post-Moore era by reducing the distance tax, not by asking physics for more shrink, but by making data travel less.