Mapping
the Open
Territory

A dessert with computers in itA dessert with computers in it
A dessert with computers in it

An open future
V1.0 4/2025

Tenstorrent

Part 1

How We Got Here

AI is changing the laws that once governed computing.

Until recently, Bell’s Law gave us an accurate frame for understanding computing revolutions, stating that each decade a new class of computing emerges, resulting in a fundamental shift in access.

We went from mainframes in the 1950s, to minicomputers in the 1960s, to super computers in the 1970s, to personal computers in the 1980s, to the world-wide web in the 1990s, and mobile in the 2000s.

These revolutions allowed us to make computers that were much more accessible – simultaneously driving performance up 10x while also driving cost down 10x. In 1981, a fully loaded IBM PC cost $4500. Today, an iPhone, which is many millions of times faster, retails for $1,129. Through this process we got very good at building very powerful computers with very small chips.

AI is changing the laws that once governed computing.

Every shift created new leaders, sidelined old ones, and required adaptation. From a social perspective, these innovations gave many more people access to compute.

However, prices aren’t dropping with the advent of Artificial Intelligence. While cost per math operation is going down, the actual cost of inference per token is still climbing as models are getting larger (e.g. GPT4.5), doing more work (e.g. “reasoning models”), and doing work that is more intensive (e.g. new image generation). AI datacenters are orders of magnitude more powerful than previous generations with spending rising by tens of billions year-over-year. Even if we eventually see some cost reductions, it will take time before they reach affordability, leaving everyone besides a few in the dust of the AI revolution.

Why is this computer class more expensive? AI is extremely physically intensive – requiring more silicon, more energy, more resources. From shifting the physics of compute at the transistor level to building out the global infrastructure of AI data centers, this revolution is pushing against the physical limitations of human industry.

Timeline of OPEN

univac

The first commercial mainframe computer, released in 1948 by the Eckert-Mauchly Computer Corporation (EMCC).

Mainframe

closed

icchip

The silicon chip invented by Robert Noyce at Fairchild Semiconductor, kicking off Moore's law.

Silicon

closed

12bitpdp8

The first popular minicomputer released by Digital Equipment Corporation's (DEC).

Minicomp

closed

dram

Invented in 1966 by Robert Dennard at IBM, later standardized by JEDEC.

Open Architecture

open

antitrustlawsuit

IBM forced to unbundled software from hardware, effectively creating the commercial software industry.

Open Source

open

unix

Ken Thompson and Dennis Ritchie developed UNIX, a lightweight operating system designed for research computers.

Software

closed

ethernet

Invented in 1973 at Xerox PARC by Robert Metcalfe, with the first open standard published in 1980.

Open Architecture

open

intel4004

Faggin later used silicon-gate MOS technology to develop the first single-chip microprocessor.

Silicon

closed

minitel

A closed precursor to the internet launched in France by France Télécom, eventually phased out.

Internet

closed

www

The Web was invented by English computer scientist Tim Berners-Lee while at CERN in 1989 and opened to the public in 1993.

Internet

open

linux

Linus Torvalds, while still a student, releases Linux, beginning the era of open OSes.

Software

open

opensource

The term "open source" was formalized, distinguishing it from "free software" (more ideological).

Open Source

open

mozilla

Netscape open-sourced its browser, leading to Firefox.

Internet

open

redhatipo

Red Hat (1993) proved that support-based open-source businesses could be profitable.

Open Source

open

pcie

An open standard for peripheral PC components, spearheaded by Intel and formalized by industry consortium PCI-SIG.

Open Architecture

open

iphone

Apple's first iPhone, launched with a tightly integrated ecosystem, including exclusivity deals with carriers, and no third-party apps.

Mobile

closed

android

First announced by Google in November 2007 as part of the launch of the Open Handset Alliance (OHA), a consortium developing open standards for mobile devices.

Mobile

open

riscv

The first version of the RISC-V Instruction Set Manual was published.

Open Architecture

open

redhatsellstoibm

IBM acquires Red Hat for $34 billion, further demonstrating the commercial viability of open models.

Open Source

open

chatgpt

ChatGPT3 becomes the first consumer breakthrough AI product.

AI

closed

deepseek

Engineers at High Flyer, a Chinese quant fund, release a competitive open alternative to proprietary LLMs.

AI

open

1950

1960

1970

1980

1990

2000

2010

2020

Mainframes

Minicomp

PC

Browser

Mobile

Cloud

AI

Univac

12-bit PDP-8

Intel 4004

iPhone

ChatGPT3

IC Chip

DRAM

Minitel

WWW

Android

DeepSeek

IBM Anti-trust Lawsuit

Linux

“Open Source”

RISC-V

Unix

Mozilla

Ethernet

Red Hat IPO

PCIe

Red Hat Sells
To IBM

AI is valuable enough to warrant this kind of investment. It is literally, as Andrej Karpathy said, “Software 2.0”.

It isn’t just an efficiency gain, like previous revolutions. AI creates knowledge that we didn’t have before. It is unprecedented how quickly AI can navigate nearly inconceivable amounts of data and complexity. It will ask questions we didn’t even know to ask. It will destroy previous industries and create new ones. Those that know how to leverage it, and can afford to, will reap the rewards.

But we can’t assume that we’ll return to the historical trend of falling costs and broadening access. We're at a critical juncture. As companies build out their AI stack, they are making a choice today that will determine the future. Companies can invest in closed systems, further concentrating leverage in the hands of a few players, or they can retain agency by investing in open systems, which are affordable, transparent, and modifiable.

But we can’t assume that we’ll return to the historical trend of falling costs and broadening access. We're at a critical juncture. As companies build out their AI stack, they are making a choice today that will determine the future.

Today, many companies are forced into choosing closed systems because they don’t know of, or can’t imagine, an alternative. Industry leaders see the sector as a tight competition between a few established incumbents and a handful of well-funded startups. We’re seeing consolidation in the market, accompanied by a huge increase in total market value.

If Bell’s Law breaks fully, AI will be the first computing revolution that doesn’t increase access, but instead concentrates it. We saw hints of this concentration effect with the previous computer class. Jonathan Zittrain argues that the cloud has put accessibility at risk leaving “new gatekeepers in place, with us and them prisoner to their limited business plans and to regulators who fear things that are new and disruptive.” Unlike hyperscalers before it, AI threatens to tip consolidation into full enclosure.

If AI eats everything, like software has eaten everything, this means that open versus closed is a referendum on the future shape of society as a whole.

A handful of companies will own the means of intelligence production, and everyone else will purchase access at whatever price they set. As many have warned, this will represent a new form of social stratification.

It is clear to us that open is existential.

A dessert with computers in it

Part 2

A Closed World

This isn’t the first time we’ve been presented with a choice between a closed or open future. In fact, we’re living in a closed world today because of choices made for us 40+ years ago. Early minicomputer and PC culture was dominated by a hacker ethos defined by “access to computers… and the Hands-On Imperative.” By the late 90s and early 00s, PC development became dominated by Windows and Intel at the cost of limiting innovation while hamstringingcompetitors and partners alike.

How do closed worlds form? One word: swamps. A swamp is a moat gone stagnant from incumbents who have forgotten how to innovate.

Just look at WinTel’s OEM partners, like Compaq, which struggled to hit 5% operating margins in the late 90s, according to SEC filings. Dell, during the same time period, absolutely revolutionized supply chains, and typically enjoyed margins around 10%. Compare this to Microsoft and Intel, which often tripled or quadrupled those figures in the same period, with Microsoft hitting 50.2% margins in 1999. Some have jokingly referred to this as drug dealer margins. In 2001, Windows had >90% market share, and almost 25 years later, it still has >70% market share.

Closed World > WHO IS able to innovate?

Innovation Ownership

Closed

Vertical Owner

Figure 1. Closed

No leverage or choice in dealings.

Proprietary

Customer

Customer

Customer

Proprietary Owner

Customer

Customer

Customer

Figure 2. Proprietary

No control of roadmap or features while incurring higher development and product costs.

Open

Customer

Customer

Customer

Open Foundation

Customer

Customer

Customer

Figure 3. Open

You drive and control the future.

The writing is on the wall for AI. We are veering towards a closed world where the constellation of technology companies are fighting over scraps. Competition, innovation, and sustainable business can’t thrive in this low-oxygen environment.

How do closed worlds form? One word: swamps. A swamp is a moat gone stagnant from incumbents who have forgotten how to innovate.

There are many ways to produce a swamp. They can protect a product by overcomplicating it, adding unnecessary proprietary systems and layers of abstraction. They can charge rents, in the form of license fees. They can pile on features just enough to justify an upgrade to customers, while staying disconnected from what they actually need. And if they want to get really clever, they can offer something “for free” as an inseparable part of a bundled service in order to lock out competition.

However it happens, what started as innovation becomes just an extra tax on the product, erecting monopolies instead of creating real value. These companies become incentivized to preserve the status quo, rather than changing.

But, as we’ve seen before, the world always changes.

A dessert with computers in it

Part 3

An Open World

Open source has a way of infiltrating crucial computing applications. The internet runs on it. The entire AI research stack uses open source frameworks. Even proprietary tech relies on it with 90% of Fortune 500 companies using open source software. There wouldn’t be macOS without BSD Unix, Azure without Linux, or Netflix without FFmpeg.

Open source and its hardware equivalent, open standards, have repeatedly catalyzed mass adoption by reducing friction and enabling interoperability. Robert Metcalf says the openness of ethernet allowed it to beat rival standards. DRAM enabled the mass adoption of PCs with high-capacity, low-cost memory, while PCIe enabled high-speed interoperability of PC components. Similarly, Open Compute Project specs, used by Meta and Microsoft among others, standardized rack and server design, so components could be modular and vendor-agnostic.

RISC-V is the hardware equivalent of Linux for AI hardware.

RISC-V is the hardware equivalent of Linux for AI hardware. It launched in 2010 at UC Berkeley as a free, open standard alternative to proprietary architectures like Intel’s x86 and ARM. Its open nature allows it to be deeply customized, making it especially desirable for AI and edge computing applications, and it is royalty-free. RISC-V’s ISA is gaining incredible adoption, with companies from Google to us at Tenstorrent adopting it for custom silicon.

Open systems also attract a global talent pool. Linux itself is the shining example of this, constructed by thousands of engineers, with significant contributions coming both from independent outsiders and employees of major players like Intel and Google.

We believe open is the default state – what remains when artificial boundaries fall away. The only question is how long those boundaries hold, and how much progress will be delayed in the meantime.

The AI Stack > Closed Today

The AI Stack > Open Future

End User

Application

Model

ML Toolchain

ML Frameworks

Optimization

Data Retrieval

Silicon & Data Centers

Data Centers

Compilers

Low Level Software

Networking

Hardware

Closed

Open

Closed

Today, parts of the AI stack are open, parts are closed, and parts have yet to be decided. Let’s look at a few of the layers:  

Hardware

CLOSED

Most hardware today is a black box, literally. You’re reliant on a company to fix, optimize, and, at times, even implement your workloads.

Low Level software

CLOSED

Most parallelization software is proprietary causing unnecessary lock-in and massive switching costs.

Models

MIXED

Models are mixed, but most of the leading ones are closed. The models that are open share limited data, with little to no support, and have no promises of staying open in the future.

Applications

CLOSED

Even if an application is using an open source model, most are built using cloud platform APIs. This means your data is being pooled to train the next gen models.

The current stack tells a story of closed engulfing open, stopping innovation in its tracks – a classic swamp.

Opening up AI hardware, with open standards like RISC-V, and its associated software would trigger a domino effect upstream. It would enable “a world where mainstream technology can be influenced, even revolutionized, out of left field.” This means a richer future with more experimentation and more breakthroughs we can barely imagine today, like personalized cancer vaccines, natural disaster prediction, and abundant energy. And this world gets here a lot faster outside of a swamp.

There’s an old Silicon Valley adage – if you aren’t paying you are the product. In AI, we’ve been paying steeply for the product, but we still are the product. We have collectively generated the information being used to train AI, and are feeding it more every day.

In a closed world, AI owns everything, and that AI is owned by a few. Opening up hardware and software means a future where AI doesn’t own you.

A dessert with computers in it

Part 4

Building an Open Future

At Tenstorrent, we're committed to building an open future for AI.

Open can mean a lot of things. For us, open means affordable, transparent, and modifiable.

Affordable

AI hardware shouldn’t be a luxury product. Universal access to intelligence requires reasonable costs. The future deserves a proliferation of AI applications, not just a few businesses capable of surviving on tiny margins thinned by monopoly rents.

Transparent

You don’t really own it unless you understand what you own, which is why we don’t sell black boxes. Our hardware is built on open standards, with each layer of the stack built from first principles for complete navigability resulting in transparency.

Modifiable

You should be able to choose what you want and what you don’t want. Open shouldn’t be another form of control. It should empower you to create your own tech stack that suits your specific needs.

It will not be easy to achieve this open future. Hardware resists openness, and software isn’t exempt either. Most developers rely on copyright law, which is automatic and offers the same protection for paintings and songs. Change a few lines of code, or a shape in a drawing, and it’s a new work. Software patents muddy the waters, locking down broad concepts with vague claims. And hardware’s worse where patents are the default. Surmounting the burden of patent law means we need to create a full-stack hardware and software company, or create a consortium of companies.

We, at Tenstorrent, are doing both.

To that end, we’re building up organizational excellence across multiple verticals from hardware to software because if we don’t, then closed systems will continue to block innovation. It’s necessary that the entire stack be open, otherwise we’ll remain in the swamp we’re in today.

We are also opening up our technology. Our IP is transparent, our architectures are open, and our software is open source so you can edit, select, fork, and own your silicon future.

Join us.

A dessert with computers in it

Own
your
silicon
future

Tenstorrent Github