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AI takes over code authorship at major tech firms

AI takes over code authorship at major tech firms

New Capabilities
By Newzino Staff |

Google's CEO says three-quarters of new code is now machine-written, marking a rapid shift in how software is built.

2 days ago: Pichai announces 75% at Cloud Next

Overview

For most of computing's history, every line of production code at a major software company was typed by a human. Google CEO Sundar Pichai told an audience at Google Cloud Next 2026 on April 24 that 75 percent of new code at the company is now generated by artificial intelligence and reviewed by engineers afterward. That share was 25 percent eighteen months earlier.

Why it matters

If most code at top firms is machine-written, the entry-level path into software engineering — the highest-paid mass profession in America — is being rewritten in real time.

Key Indicators

75%
Google's new code that is AI-generated
Up from 50 percent in fall 2025 and 25 percent in October 2024.
6x
Speedup on a recent code migration
Engineers paired with AI agents versus engineers alone a year earlier.
30%
Microsoft's reported AI-written share
Figure cited by CEO Satya Nadella in April 2025, with some internal repos exceeding 50 percent.
750M
Monthly Gemini users
Reported at Cloud Next 2026, alongside a $240B Google Cloud backlog.
$175-185B
Google's planned 2026 capital expenditure
Largely directed at AI data centers, chips, and energy capacity.
67,000
U.S. software engineer job listings in early 2026
A three-year high, even as AI-attributed tech layoffs continue.

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People Involved

Sundar Pichai
Sundar Pichai
Leading Google's AI repositioning around Gemini and agentic tools
 Satya Nadella
Satya Nadella
Set the public benchmark Google has now passed
BC
Boris Cherny
Public face of the most aggressive internal-AI-coding claims

Organizations Involved

Google
Google
Public technology company
Leading public benchmark for AI-generated code share

The dominant search and advertising company, now repositioning its engineering organization around Gemini and agentic developer tools.
Microsoft
Microsoft
Public technology company
Owns GitHub Copilot; 30% AI-written code internally

The largest enterprise software vendor, and through GitHub the operator of the most widely used AI coding assistant.
Anthropic
Anthropic
AI research company
Reports near-100% internal AI code share via Claude Code

The maker of the Claude model family and Claude Code, which competes head-to-head with Google's Gemini and OpenAI's Codex inside developer workflows.

Timeline

  1. Pichai announces 75% at Cloud Next

    Statement

    Pichai opens Google Cloud Next 2026 saying three-quarters of new code is AI-generated, alongside a $240B cloud backlog and 750M monthly Gemini users.

  2. Anthropic and OpenAI engineers report ~100%

    Statement

    Senior engineers at the leading AI labs say they have stopped writing code by hand, delegating it almost entirely to in-house models.

  3. Google launches Antigravity and Gemini 3

    Product Launch

    Google releases an agent-first IDE bundled with Gemini 3 Pro, designed to orchestrate multiple autonomous coding agents.

  4. Google's share crosses 50%

    Statement

    Pichai updates the AI-generated code figure to about half of new code at the company.

  5. Microsoft puts a public number on it

    Statement

    At Meta's LlamaCon, Satya Nadella says 20 to 30 percent of Microsoft's repository code is now AI-written.

  6. Google's first 25% disclosure

    Statement

    Pichai tells investors that more than a quarter of new code at Google is being generated by AI assistants.

  7. GitHub launches Copilot

    Product Launch

    Microsoft's GitHub releases the first mainstream AI pair programmer, suggesting code completions inside the IDE.

Scenarios

1

Engineering headcount stagnates as productivity absorbs growth

Discussed by: Semafor, Fortune, and labor economists tracking BLS occupational data

Big tech firms keep total engineer counts flat or slightly down, using AI to handle the marginal ticket. Junior hiring contracts hardest because review-and-orchestrate roles favor experienced engineers. Software remains lucrative but the field stops absorbing new graduates at the rate of the last decade.

2

Software demand expands and engineer counts grow with it

Discussed by: Sundar Pichai, GitHub's Octoverse report, and the 'compiler analogy' camp

Cheaper, faster code creation triggers a wave of new software that wasn't economical before — internal tools, custom enterprise apps, indie products. Demand grows faster than per-engineer productivity, mirroring what compilers did to assembly programmers. Hiring continues but the role shifts toward systems design and review.

3

Quality and security debt forces a partial reversal

Discussed by: Security researchers, Stack Overflow developer survey commentary

AI-written code passes review but accumulates subtle defects, supply-chain vulnerabilities, and architectural drift. A high-profile incident — a major breach traced to an AI-introduced bug, or a regulatory finding — forces firms to dial back the share or impose stricter human gates. The 75% number plateaus or retreats.

4

Numbers like '75%' get exposed as mostly marketing

Discussed by: Independent developers writing on dev.to, Hacker News commentary, academic researchers

Critics note that 'AI-generated' covers everything from a one-line autocomplete to a fully agent-written module, with no public methodology. Closer audits show the headline figure overstates substantive contribution. The share remains real and rising, but the public framing loses credibility.

Historical Context

FORTRAN and the compiler revolution (1957)

April 1957

What Happened

IBM shipped FORTRAN, the first widely used high-level language, with John Backus's optimizing compiler. A routine that had taken 1,000 lines of assembly could now be written in roughly 50 lines of FORTRAN, cutting programming effort by a factor of five to ten.

Outcome

Short Term

Programming stopped being 'hand-to-hand combat with the machine' and opened to scientists and engineers outside the small priesthood of assembly coders.

Long Term

Demand for programmers exploded rather than collapsed. Each productivity gain enabled software in domains that had been economically out of reach, and the profession grew tenfold over the following decades.

Why It's Relevant Today

The closest historical analogue: a tool that drastically cuts the per-line cost of code. The compiler precedent is why Pichai and others argue AI coding will expand engineering employment, not shrink it — though the transition reshaped which skills mattered.

Spreadsheets replace bookkeeping clerks (1979-1990s)

1979 onward

What Happened

VisiCalc, then Lotus 1-2-3 and Excel, automated work that armies of bookkeepers and junior accountants had done by hand on green ledger paper. Economist James Bessen later documented that roughly 400,000 bookkeeping jobs disappeared while accounting and analyst roles grew by more than 600,000.

Outcome

Short Term

The most routine, entry-level financial roles contracted sharply through the 1980s and 1990s.

Long Term

The profession reorganized upward: fewer pure data-entry jobs, more financial analysts and modelers doing work that wasn't feasible without spreadsheets.

Why It's Relevant Today

Spreadsheets did to bookkeeping what AI coding may do to junior software engineering — eliminate the routine production layer while expanding the analytical and design layer above it.

Power loom and the Luddite revolt (1811-1816)

1811-1816

What Happened

Mechanized weaving displaced English handloom weavers, whose wages collapsed from around 21 shillings a week in 1797 to about 6 shillings by the 1830s. Organized weavers smashed machines across Yorkshire and Nottinghamshire; the British government deployed troops and made frame-breaking a capital offense in 1812.

Outcome

Short Term

A generation of skilled weavers was destroyed economically, with widespread poverty and migration.

Long Term

Textile output and overall employment in the sector grew enormously, but the gains accrued to a different set of workers — factory operatives, not the displaced craftsmen.

Why It's Relevant Today

The cautionary parallel: aggregate productivity gains from automation do not automatically benefit the workers whose specific skills are being automated. Whether AI coding follows the compiler path or the power-loom path will depend on how quickly displaced engineers can move into the new orchestrator and reviewer roles.

Sources

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+3
Google Blog Human Progress Semafor Fast Company CNBC Fortune Google Developers Blog Tom's Hardware The Next Web