Former Intel CEO on What Went Wrong, What's Next + Lovable CEO on the Real Promise of Vibe Coding

Routing tasks to the model best suited for each job and prioritizing observed errors for fine‑tuning lowers cost and raises accuracy because diverse models bring different strengths and focused retraining fixes the platform's real failure modes.

Modern fabs are extremely fragile because they require continuous power and tightly controlled environments, and a shutdown or brownout forces a months‑long restart that removes capacity for an extended period.

Parallel, independent prototypes improve discovery because isolated experiments avoid local minima, produce diverse solutions quickly, and let teams merge or A/B test the best elements instead of committing to a single monolithic path.

Various qubit modalities and demonstrated error correction mean the remaining barrier for useful quantum computing is engineering scale, so practical results before 2030 are plausible if teams can industrialize more qubits and reduce noise.

No-code and AI platforms make bespoke internal software practical because they provide opinionated architecture, integrations, and tooling that let non‑engineers build, secure, and operate production apps far faster and cheaper than traditional custom development.

Open, iterative engineering plus an accessible platform invites outside communities to find novel uses because steady improvements and low friction let hackers and researchers discover high‑value applications the original designers did not predict.

Apple built its own silicon to optimize the whole product system because relying on commodity Intel chips forced compromises in power, size, and integration that could not meet mobile and iOS device requirements.

Global AI and data‑center expansion hits a hard ceiling because data centers need large, steady power and regional energy capacity grows only a few percent per year, so compute fleets cannot scale indefinitely regardless of investment.