AI Native Software and Hardware is Here
This Week AI Broke Our Software—and Hardware—Assumptions
AI Native is Here
This Week AI Broke Our Software - and Hardware - Assumptions
This advancement establishes a new dimension beyond raw knowledge, allowing AI to move from advice-giving to direct action-taking within complex workflows.
Thus spoke Tomasz Tunguz in his essay From Knowledge to Action. The same theme is echoed in GPT-5 Hands-On: Welcome to the Stone Age on Latent Space":
The Stone Age marked the dawn of human intelligence, but what exactly made it so significant? What marked the beginning? Did humans win a critical chess battle? Perhaps we proved a very fundamental theorem, that made our intelligence clear to an otherwise quiet universe? Recited more digits of pi?
No. The beginning of the stone age is clearly demarcated by one thing, and one thing only: humans learned how to use tools.
What if the defining assumption of modern tech - that software is something humans write, and other humans use, and hardware is something we buy and use - just became wrong?
This week's stories say exactly that. OpenAI's GPT‑5 "just does stuff," routes work to tools, and slashes latency and energy via a new router; Anthropic keeps shipping pragmatic upgrades that turn models into working colleagues. The net: software is becoming an actor, not an app, and hardware is going to have that embedded - imagine a child talking to a toy and getting answers back. I can already do that in my Tesla Model 3 using Grok. Or telling the lawn mower to go mow the lawn and it not only does it but reports back to you.
1) As Tomasz Tunguz states - Software has moved from knowledge to action - and budgets will follow
GPT‑5 is not a smarter autocomplete; it's a workflow engine. Its router policy picks specialized modules, calls tools, and self‑verifies work, yielding ~4x lower latency and half the energy per token. Pair that with hands‑on reports of GPT‑5 proactively spinning up entire apps and collateral ("it just does stuff"), and Tom Tunguz's framing is here: from advice to execution.
Anthropic's tack is quieter but consequential. Claude Opus 4.1 isn't splashy; it's better at code refactors, reasoning, and "agentic" tasks - exactly where enterprises live. As Dario Amodei told John Collison (via Om Malik), code is the early indicator of what's coming everywhere else.
The result hits pricing and procurement first. Jason Lemkin put it bluntly:
"Every developer... is going to get $10,000 a month of AI credits... Shopify is already there for some of its top developers."
McKinsey's 12,000 agents and Box's "enhance, don't replace" posture show where Fortune 500s are headed. Meanwhile, the ground truth: non‑coders are building internal tools in hours, ditching pricey SaaS (Every's $50k‑in‑three‑hours story) as "vibe analysis" lets teams talk to their data and compress cycles 2x to 100x.
2) Hardware is the constraint - and it's reorganizing the stack
Azure captured ~43% of net new cloud run‑rate, but all three hyperscalers say demand exceeds capacity. Power and chips - not sales - are the gating factor. GPT‑5's router is thus not just clever; it's a power policy.
The hardware response is national and local. Apple's additional $100B U.S. investment includes a Houston facility to build AI servers - a reshoring bet that compute sovereignty becomes strategy.
Edge is back. OpenAI's open‑weight "gpt‑oss" models run on a single 80GB GPU - or even a laptop (20B on 16GB). If your agents can act locally with near‑o4‑mini capability, you don't just save cost; you change privacy, latency, and vendor risk. ElevenLabs' licensed music generator hints at the parallel content supply chain: on‑device generation backed by explicit rights.
3) How can we help AI and also enable revenue streams?
Cloudflare's charge that Perplexity used stealth, undeclared crawlers to evade robots.txt (Perplexity disputes it) is more than drama; it's the fault line for an AI‑era web. ChatGPT, Cloudflare notes, respected robots.txt; the market will punish those who stall AI and reward those who help it but it will also reward AI for figuring out how to channel money to content producers. My 2c is that this needs more than training fees policed by robots.txt.
Capital is concentrating around those who can shoulder the capex. Carta shows valuations up 15-25% even as deals fall; Crunchbase highlights $70B flowing to just 11 companies. OpenAI's prospective $500B secondary, 700M weekly users, and even my $10T hot‑take underscore the winner‑take‑most stakes.
Meanwhile, Meta's "copy button" culture and Substack vs. Ghost reveal distribution power shifting from brands to platforms and - importantly - back to open infrastructure. China's push for a global AI governance plan says the rules of this game won't be written in one capital.
What others are missing: The software story isn't "apps get AI." That would simply plug AI use into existing user interfaces.
The real story is that agents plus tool‑calling invert enterprise design.
What to watch next
Will enterprises appoint "agent managers" and codify routing, spend caps, and human‑in‑the‑loop by function? (Compliance, claims, tax are already drawing legal lines.)
Do open‑weight models at the edge erode proprietary moats - or expand them via proprietary data and deep workflow integration?
Does the Cloudflare‑Perplexity fight catalyze a paid, auditable standard for AI access to the web - and who enforces it?
Can clouds expand power faster than AI demand? If not, expect more Apple‑style reshoring and product designs that privilege energy‑aware routing.
If last year was about demos, this week made it operational. Software does the work. Hardware rations the power. Our assumptions should update accordingly.
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