@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Light.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Light.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Light.ttf’) format(‘truetype’);
font-weight: 300;
font-style: normal;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-LightItalic.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-LightItalic.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-LightItalic.ttf’) format(‘truetype’);
font-weight: 300;
font-style: italic;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Regular.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Regular.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Regular.ttf’) format(‘truetype’);
font-weight: 400;
font-style: normal;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-RegularItalic.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-RegularItalic.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-RegularItalic.ttf’) format(‘truetype’);
font-weight: 400;
font-style: italic;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Medium.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Medium.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Medium.ttf’) format(‘truetype’);
font-weight: 500;
font-style: normal;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-MediumItalic.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-MediumItalic.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-MediumItalic.ttf’) format(‘truetype’);
font-weight: 500;
font-style: italic;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Semibold.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Semibold.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Semibold.ttf’) format(‘truetype’);
font-weight: 600;
font-style: normal;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-SemiboldItalic.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-SemiboldItalic.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-SemiboldItalic.ttf’) format(‘truetype’);
font-weight: 600;
font-style: italic;
}Here’s the rewritten text in fluent, natural English:
“`css
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Bold.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Bold.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Bold.ttf’) format(‘truetype’);
font-weight: 700;
font-style: normal;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-BoldItalic.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-BoldItalic.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-BoldItalic.ttf’) format(‘truetype’);
font-weight: 700;
font-style: italic;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Black.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Black.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Black.ttf’) format(‘truetype’);
font-weight: 900;
font-style: normal;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-BlackItalic.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-BlackItalic.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-BlackItalic.ttf’) format(‘truetype’);
font-weight: 900;
font-style: italic;
}
@font-face {
font-family: ‘Guardian Titlepiece’;
src: url(‘https://interactive.guim.co.uk/fonts/garnett/GTGuardianTitlepiece-Bold.woff2’) format(‘woff2’),
url(‘https://interactive.guim.co.uk/fonts/garnett/GTGuardianTitlepiece-Bold.woff’) format(‘woff’),
url(‘https://interactive.guim.co.uk/fonts/garnett/GTGuardianTitlepiece-Bold.ttf’) format(‘truetype’);
font-weight: 700;
font-style: normal;
}
@media (min-width: 71.25em) {
.content__main-column–interactive {
margin-left: 160px;
}
}
@media (min-width: 81.25em) {
.content__main-column–interactive {
margin-left: 240px;
}
}
.content__main-column–interactive .element-atom {
max-width: 620px;
}
@media (max-width: 46.24em) {
.content__main-column–interactive .element-atom {
max-width: 100%;
}
}
.content__main-column–interactive .element-showcase {
margin-left: 0;
}
@media (min-width: 46.25em) {
.content__main-column–interactive .element-showcase {
max-width: 620px;
}
}
@media (min-width: 71.25em) {
.content__main-column–interactive .element-showcase {
max-width: 860px;
}
}
.content__main-column–interactive .element-immersive {
max-width: 1100px;
}
@media (max-width: 46.24em) {
.content__main-column–interactive .element-immersive {
width: calc(100vw – var(–scrollbar-width, 0px));
position: relative;
left: 50%;
right: 50%;
margin-left: calc(-50vw + var(–half-scrollbar-width, 0px)) !important;
margin-right: calc(-50vw + var(–half-scrollbar-width, 0px)) !important;
}
}
@media (min-width: 46.25em) {
.content__main-column–interactive .element-immersive {
transform: translate(-20px);
width: calc(100% + 60px);
}
}
@media (max-width: 71.24em) {
.content__main-column–interactive .element-immersive {
margin-left: 0;
margin-right: 0;
}
}
@media (min-width: 71.25em) {
.content__main-column–interactive .element-immersive {
transform: translate(0);
width: auto;
}
}
@media (min-width: 81.25em) {
.content__main-column–interactive .element-immersive {
max-width: 1260px;
}
}
.content__main-column–interactive p,
.content__main-column–interactive ul {
max-width: 620px;
}
.content__main-column–interactive::before {
position: absolute;
top: 0;
height: calc(100% + 15px);
min-height: 100px;
content: ”;
}
@media (min-width: 71.25em) {
.content__main-column–interactive::before {
border-left: 1px solid #dcdcdc;
z-index: -1;
left: -10px;
}
}
@media (min-width: 81.25em) {
.content__main-column–interactive::before {
border-left: 1px solid #dcdcdc;
left: -10px;
}
}
“`Here is the rewritten text in fluent, natural English:
The left margin is set to -11px. Inside the main interactive column, elements with the class “element-atom” have no top or bottom margin, but do have 12px of padding at the top and bottom. If a paragraph is followed by an “element-atom”, the element’s top and bottom padding are removed, and it gets 12px of margin on both top and bottom instead. Inline elements are limited to a maximum width of 620px. On screens wider than 61.25em, figures with the role “inline” are also capped at 620px wide.
For media sections that contain a looping figure, the caption is given a higher stacking order (z-index: 6). The loop button, which is 32px wide, is aligned to the bottom-right of the figure, with a 40px bottom margin and a 3px right margin. The caption button has a z-index of 100. On screens wider than 46.25em, if a figure has the class “cinemagraph”, its child div’s maximum height is removed.
In the body section, self-hosted videos are displayed as block elements, 100% wide, with a max width of 620px, and 12px of margin on top and bottom. The video and its looping figure are also 100% wide, auto-height, centered, and capped at 620px. If the looping figure has the class “element-video-immersive”, the video container expands to full width with no max width, and the margins are removed. On screens wider than 71.25em, such immersive videos are 1140px wide with a left margin of -180px, and the caption gets a 20px left margin. On screens wider than 81.25em, they are 1300px wide with a left margin of -260px.
The root variables define colors for dateline (gray), header border (light gray), caption text (gray), caption background (dark with transparency), and feature (red). The new pillar color defaults to the primary pillar or feature. Subheadings, pullquotes, and blockquotes use the secondary pillar color. Blockquotes also use the secondary pillar for their fill. In dark mode (unless overridden), these colors switch to the dark mode pillar.
Elements with the class “element-atom” have no padding. In the article body, if an “element-atom” is the first element and is followed by a paragraph (or a sign-in gate followed by a paragraph), or if a horizontal rule (that isn’t the last one) is followed by a paragraph, then that paragraph gets 14px of top padding. The same applies to interactive content, comment bodies, and feature bodies.Here is the rewritten text in fluent, natural English:
The first letter of the first paragraph after certain elements—such as the first atom, a sign-in gate, or a horizontal rule—uses a large, bold, uppercase style. It is set in the Guardian Headline font family, with a font size of 111 pixels and a line height of 92 pixels. This letter floats to the left, has a right margin of 8 pixels, and is vertically aligned to the top of the text. Its color matches the drop-cap or pillar color.
Paragraphs that come right after a horizontal rule have no top padding.
Pull quotes within the main content areas are limited to a maximum width of 620 pixels.
For images with the “showcase” style, their captions are positioned statically and take up the full width, up to 620 pixels. On screens wider than 71.25em, these captions are positioned absolutely and have a maximum width of 140 pixels. On screens wider than 81.25em, the maximum width increases to 220 pixels.
Elements with the “immersive” style take up the full viewport width, minus the scrollbar width. On smaller screens (up to 71.24em), their maximum width is 978 pixels, and captions have 10 pixels of padding on each side. On screens between 30em and 71.24em, caption padding increases to 20 pixels. On screens between 46.25em and 61.24em, the maximum width is 738 pixels. On very small screens (up to 46.24em), these elements have a left margin of -10 pixels, no right margin, and are aligned to the left.For screens at least 30em wide:
– For `.element.element–immersive.element-immersive`, set the left margin to -20px.
– For its `figcaption`, add 20px of padding on the left and right.
For screens at least 71.25em wide:
– Inside `[data-gu-name=body]`, for `figure.element.element–showcase.element-showcase` and `.content__main-column–interactive figure.element.element–showcase.element-showcase`, set the left margin to -160px.
For screens at least 81.25em wide:
– Inside `[data-gu-name=body]`, for the same elements, set the left margin to -240px.
The `.furniture-wrapper` is positioned relatively.
For screens at least 61.25em wide:
– Use a CSS grid with 20px column gaps and no row gaps. The grid has 10 columns: the first 5 are for title, headline, meta, and standfirst; the last 5 are for portrait.
– The grid rows are: title and portrait start together at 0.25fr, headline at 1fr, standfirst at 0.75fr, and meta at auto, all ending with portrait.
– For `#headline > div:first-child`, `[data-gu-name=headline] > div:first-child`, and `.headline > div:first-child`, add a 1px solid top border using `–headerBorder`.
– For `#meta` and `[data-gu-name=meta]`, set relative positioning, 2px top padding, and no right margin.
– For `.standfirst .content__standfirst`, `#standfirst .content__standfirst`, and `[data-gu-name=standfirst] .content__standfirst`, set bottom margin to 4px.
– For list items in standfirst, set font size to 20px.
– For links in standfirst (including list items), remove bottom border and background image, add underline with 6px offset, and use `–headerBorder` (or #dcdcdc) as the underline color. On hover, change the underline color to `–new-pillar-colour`.
– For the first paragraph in standfirst, add a 1px solid top border using `–headerBorder` and set bottom padding to 0.
For screens at least 71.25em (and also at least 61.25em):
– Remove the top border from the first paragraph in standfirst.
For screens at least 61.25em:
– For figures, set margins to 0 0 0 -10px.
– For figures with `[data-spacefinder-role=inline]` and class `.element`, set max-width to 630px.
For screens at least 71.25em:
– Change the grid to 14 columns: the first 2 for title, headline, and meta start; then meta ends and standfirst starts at column 3; columns 3 to 7 are for standfirst; title and headline end at column 7; portrait starts at column 8 and spans 7 columns.
– The grid rows are: title and portrait start at 80px, then headline at auto, then standfirst and meta start at auto, and end with portrait.
– For `#meta` and `[data-gu-name=meta]`, add a pseudo-element `:before` with a 540px wide, 1px high line at the top, using `–headerBorder` as the background color.
– For paragraphs in standfirst, remove the top border.
– For `.standfirst`, `#standfirst`, and `[data-gu-name=standfirst]`, add a pseudo-element `:before` with a 1px wide, full-height line on the left, using `–headerBorder` as the background color, positioned at the top and 0.5px from the left.
For screens at least 81.25em:
– The grid template columns are not fully specified in the provided text.The layout uses a grid with columns defined as: repeat(3, 1fr) for the meta section, repeat(5, 1fr) for the standfirst, and repeat(8, 1fr) for the portrait. The rows are set as: the title and portrait start at 0.25fr, the headline takes 1fr, and the standfirst and meta take 0.75fr.
For the meta section, the `:before` element has a width of 620px. The standfirst’s `:before` element is positioned slightly to the left, at -0.5px.
In the article header, the labels inside the title or meta sections have a top padding of 2px.
The headline’s `h1` is bold (font-weight 600), has a max width of 620px, and a font size of 32px. On screens wider than 71.25em, the max width reduces to 540px and the font size increases to 50px.
On screens wider than 46.25em, the keyline-4 (or lines) have no right margin. On screens wider than 61.25em, they are hidden. The keyline-4 SVG uses a stroke color defined by `–headerBorder`.
For screens wider than 46.25em, the meta section also has no right margin. The social and comment elements within meta use `–headerBorder` for their border color. The `gu-island` elements inside the meta container are hidden.
The standfirst is positioned with a left margin of -10px, left padding of 10px, and is relatively positioned. On screens wider than 46.25em, it gets an extra top padding of 2px. The standfirst paragraphs have a font weight of 400, font size of 20px, and bottom padding of 14px.
The main media (or media) is positioned relatively, with no top margin and a bottom margin of 2px, and it occupies the portrait grid area. Its inner divs are full width with no margin on either side. On screens wider than 61.25em, the bottom margin is removed. On screens narrower than 46.24em, the media takes up the full viewport width (minus scrollbar width) and has a left margin of -10px. If the screen is between 30em and 46.24em, the left margin becomes -20px.
The figure caption is positioned at the bottom, with padding of 4px on top and sides and 12px at the bottom. It uses `–captionBackground` for its background and `–captionText` for text color. It has no max width, is full width, has no bottom margin, and a minimum height of 46px. The caption’s span elements use `–headerBorder` for color, and the SVG inside them uses the same for fill. The first span is hidden, while the second is displayed with a max width of 90%. On screens wider than 30em, the caption padding changes to 4px on top and 20px on the sides, with 12px at the bottom. If the caption has the class “hidden,” its opacity is set to 0.
The caption button is displayed as a block, positioned absolutely at the bottom (10px from bottom) and right (8px from right), with a z-index of 30. It uses `–captionBackground` as its background, has no border, is circular (border-radius 50%), and has padding of 6px on top, 5px on sides, and 5px at the bottom. The SVG inside is scaled to 85%. On screens wider than 30em, the right offset increases to 10px.The `.content__main-column–interactive` element has a top offset of -12px and a height of 100% plus 24px. The `h2` inside it has a max width of 620px. The `:root` sets a new pillar colour, a header border colour of #606060, and a dark mode feature colour of #ff5943. Navigation and aside sections that follow certain elements are hidden. The `.furniture-wrapper` has a dark background, with margins and padding that adjust at different screen widths. At 81.25em and above, it uses `:before` and `:after` pseudo-elements to extend the background and add a right border. Inside the wrapper, article headers, titles, and spans use the new pillar colour. At 61.25em and above, the first child of headline elements gets a top border. The `h1` inside headlines is bold and coloured #dcdcdc. Figures inside headlines have no top margin and a small bottom margin. At 71.25em and above, meta elements have a top border. Details, summaries, and their spans inside meta are coloured #dcdcdc. Social media links and buttons in meta have a border colour matching the header border and use the new pillar colour for text and SVG fills. On hover, they invert colours. Other meta divs are #dcdcdc, and links use the new pillar colour with an underline on hover. Standfirst links have no bottom border, use the new pillar colour, and are underlined with a specific offset and colour.Here is the rewritten text in fluent, natural English:
When you hover over a link inside the standfirst section of the furniture wrapper, the bottom border disappears and the underline color changes to match the pillar color (or the dark mode feature color as a fallback). The paragraph text in the standfirst section is light gray (#dcdcdc).
On screens wider than 61.25em, the first paragraph in the standfirst gets a top border using the header border color. On screens wider than 71.25em, that top border is removed. List items in the standfirst are also light gray (#dcdcdc).
On screens wider than 71.25em, a line appears before the standfirst, using the header border color.
On screens wider than 46.25em, the furniture wrapper adds two background strips on either side. These strips use the dark background color and are separated from the main content by a border. The width of these strips changes depending on the screen size:
– At 46.25em, the strips fill the space around a 738px-wide content area.
– At 61.25em, they adjust to a 978px-wide content area.
– At 71.25em, they adjust to a 1138px-wide content area.
– At 81.25em, they adjust to a 1298px-wide content area.
The keyline-4 and lines SVG elements inside the furniture wrapper use the header border color for their stroke. The social and comment sections in the meta area also use the header border color for their borders.
In the article body, h2 headings have a font weight of 200. If an h2 contains strong text, the font weight changes to 700. Any unordered list inside an element with `data-print-layout=”hide”` has no background image.
Finally, a custom font called “Guardian Headline Full” is loaded in light weight (300) from several file formats.Here is the rewritten text in fluent, natural English:
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-LightItalic.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-LightItalic.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-LightItalic.ttf’) format(‘truetype’);
font-weight: 300;
font-style: italic;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Regular.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Regular.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Regular.ttf’) format(‘truetype’);
font-weight: 400;
font-style: normal;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-RegularItalic.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-RegularItalic.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-RegularItalic.ttf’) format(‘truetype’);
font-weight: 400;
font-style: italic;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Medium.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Medium.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Medium.ttf’) format(‘truetype’);
font-weight: 500;
font-style: normal;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-MediumItalic.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-MediumItalic.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-MediumItalic.ttf’) format(‘truetype’);
font-weight: 500;
font-style: italic;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Semibold.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Semibold.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Semibold.ttf’) format(‘truetype’);
font-weight: 600;
font-style: normal;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-SemiboldItalic.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-SemiboldItalic.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-SemiboldItalic.ttf’) format(‘truetype’);
font-weight: 600;
font-style: italic;
}
@font-face {
font-family: ‘Guardian Headline Full’;
src: url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Bold.woff2’) format(‘woff2’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Bold.woff’) format(‘woff’),
url(‘https://assets.guim.co.uk/static/frontend/fonts/guardian-headline/full-not-hinted/GHGuardianHeadline-Bold.ttf’) format(‘truetype’);
font-weight: 700;
font-style: normal;
}Here is the rewritten text in fluent, natural English:
The font files are loaded from the Guardian’s servers. For example, the bold italic version of the Guardian Headline font is available in WOFF2, WOFF, and TTF formats. The same applies to the black and black italic versions. The Guardian Titlepiece font is also loaded in bold style from a different source.
On iOS and Android devices, certain color variables are set, including a dark background, a feature color, and a dark mode feature color. These adapt based on the user’s system color scheme preference.
In article containers on both iOS and Android, the first letter of the first paragraph after certain elements (like an atom or sign-in gate) is styled with a specific color, which comes from a secondary pillar variable or defaults to black.The article header sections for containers like `.article__header`, `#feature-article-container`, `#standard-article-container`, and `#comment-article-container` on both Android and iOS have a height of zero.
On iOS and Android, the furniture wrapper inside these containers has padding of 4px on top and 10px on the sides. The labels inside the furniture wrapper use bold text, the font family “Guardian Headline” (or similar fallbacks), and are capitalized with the color set by the new pillar color variable.
The headline (h1) inside the furniture wrapper is 32px, bold, has 12px of padding at the bottom, and is colored #121212.
Images inside the furniture wrapper (using the `figure.element-image` class) are positioned relatively, with a 14px top margin and a left margin of -10px. Their width is set to the full viewport width minus any scrollbar width. The inner elements of the image (`.figure__inner`, `img`, and `a`) have a transparent background, the same full viewport width, and their height is set to auto.
The standfirst section inside the furniture wrapper is also styled for both iOS and Android.For the `.standfirst` inside `.furniture-wrapper` in `#standard-article-container`, `#comment-article-container`, and on Android devices, add 4px of padding on top and 24px on the bottom, with a right margin of -10px.
On iOS and Android, inside `#feature-article-container`, `#standard-article-container`, and `#comment-article-container`, the paragraph text within `.standfirst__inner` should use the font family: Guardian Headline, Guardian Egyptian Web, Guardian Headline Full, Georgia, serif.
For links (including those inside list items) in the same containers on iOS and Android, set the text color to the pillar color variable, remove any background image, underline the text with a 6px offset, and use the header border color (default `#dcdcdc`) for the underline. Do not add a bottom border. On hover, change the underline color to the pillar color variable.
For the `.meta` section inside `.furniture-wrapper` in these containers on iOS and Android, set the margin to 0. For the byline, byline author, author links, and meta byline spans, keep the existing styling.The following CSS rules apply to the `.wrapper .meta .byline` element and various selectors for iOS and Android article containers. The text color is set to `var(–new-pillar-colour)`.
For iOS and Android, the `.meta__misc` class inside the furniture wrapper has no padding. Any SVG inside that class uses `stroke: var(–new-pillar-colour)`.
The `#caption-button` inside `.element–showcase` is displayed as a flex container with 5px padding, centered content, and a width and height of 28px, positioned 14px from the right.
The `.article__body` class for all article types on both iOS and Android has 0 padding on the left and right, with 12px on each side.
For images inside `.article__body` that are not thumbnails or immersive, the margin is set to 0, and the width is calculated as `100vw – 24px – var(–scrollbar-width, 0px)`, with automatic height.Here’s the rewritten version in fluent, natural English:
For iOS and Android devices, in feature, standard, and comment article containers, image captions that aren’t thumbnails or immersive images should have no padding. Also, immersive images in these containers should take up the full width of the viewport, minus the scrollbar width.
In the prose sections of these articles, quoted blockquotes should use the new pillar color for their decorative element. Links in the prose should be styled with the primary pillar color, no background image, an underline, and a 6px offset. The underline color should match the header border. When you hover over these links, the underline color should change to the new pillar color.
In dark mode (when the user’s system prefers dark color schemes), the furniture wrapper background should be a dark gray (#1a1a1a). The content labels inside the wrapper should use the new pillar color, and the headline (h1) should also be styled with the new pillar color..furniture-wrapper h1.headline {
background-color: unset;
color: var(–headerBorder) !important;
}
body.ios #feature-article-container .furniture-wrapper .standfirst p,
body.ios #standard-article-container .furniture-wrapper .standfirst p,
body.ios #comment-article-container .furniture-wrapper .standfirst p,
body.android #feature-article-container .furniture-wrapper .standfirst p,
body.android #standard-article-container .furniture-wrapper .standfirst p,
body.android #comment-article-container .furniture-wrapper .standfirst p {
color: var(–headerBorder);
}
body.ios #feature-article-container .furniture-wrapper .standfirst a,
body.ios #standard-article-container .furniture-wrapper .standfirst a,
body.ios #comment-article-container .furniture-wrapper .standfirst a,
body.android #feature-article-container .furniture-wrapper .standfirst a,
body.android #standard-article-container .furniture-wrapper .standfirst a,
body.android #comment-article-container .furniture-wrapper .standfirst a,
body.ios #feature-article-container .furniture-wrapper .meta .byline__author,
body.ios #feature-article-container .furniture-wrapper .meta span.byline__author a,
body.ios #standard-article-container .furniture-wrapper .meta .byline__author,
body.ios #standard-article-container .furniture-wrapper .meta span.byline__author a,
body.ios #comment-article-container .furniture-wrapper .meta .byline__author,
body.ios #comment-article-container .furniture-wrapper .meta span.byline__author a,
body.android #feature-article-container .furniture-wrapper .meta .byline__author,
body.android #feature-article-container .furniture-wrapper .meta span.byline__author a,
body.android #standard-article-container .furniture-wrapper .meta .byline__author,
body.android #standard-article-container .furniture-wrapper .meta span.byline__author a,
body.android #comment-article-container .furniture-wrapper .meta .byline__author,
body.android #comment-article-container .furniture-wrapper .meta span.byline__author a {
color: var(–new-pillar-colour);
}
body.ios #feature-article-container .furniture-wrapper .meta__misc svg,
body.ios #standard-article-container .furniture-wrapper .meta__misc svg,
body.ios #comment-article-container .furniture-wrapper .meta__misc svg,
body.android #feature-article-container .furniture-wrapper .meta__misc svg,
body.android #standard-article-container .furniture-wrapper .meta__misc svg,
body.android #comment-article-container .furniture-wrapper .meta__misc svg {
stroke: var(–new-pillar-colour);
}
body.ios #feature-article-container .furniture-wrapper figure.element-image.element–showcase figcaption,
body.ios #standard-article-container .furniture-wrapper figure.element-image.element–showcase figcaption,
body.ios #comment-article-container .furniture-wrapper figure.element-image.element–showcase figcaption,
body.android #feature-article-container .furniture-wrapper figure.element-image.element–showcase figcaption,
body.android #standard-article-container .furniture-wrapper figure.element-image.element–showcase figcaption,
body.android #comment-article-container .furniture-wrapper figure.element-image.element–showcase figcaption {
color: var(–dateline);
}
body.ios #feature-article-container .article__body .prose blockquote.quoted,
body.ios #standard-article-container .article__body .prose blockquote.quoted,
body.ios #comment-article-container .article__body .prose blockquote.quoted,
body.android #feature-article-container .article__body .prose blockquote.quoted,
body.android #standard-article-container .article__body .prose blockquote.quoted,
body.android #comment-article-container .article__body .prose blockquote.quoted {
color: var(–new-pillar-colour);
}
body.ios #feature-article-container #article-body > div,
body.ios #feature-article-container .content–interactive > div,
body.ios #feature-article-container #feature-body,
body.ios #feature-article-container [data-gu-name=”body”],
body.ios #feature-article-container #comment-body,
body.ios #standard-article-container #article-body > div,
body.ios #standard-article-container .content–interactive > div,
body.ios #standard-article-container #feature-body,
body.ios #standard-articlHere’s the rewritten version in fluent, natural English:
For iOS and Android devices, the background color of article and comment sections should use the dark background variable. This applies to various containers and body sections within feature, standard, and comment article layouts.
Additionally, on iOS devices, the first letter of any paragraph that follows an element atom (whether or not it appears after a sign-in gate) should have a specific style applied. This rule covers all article body sections, interactive content areas, feature bodies, comment bodies, and any container with the data attribute “data-gu-name=body” across feature, standard, and comment article layouts.This appears to be a long list of CSS selectors, not a text to be rewritten. Could you please provide the actual text you’d like me to rewrite in fluent, natural English?Here’s the rewritten version in fluent, natural English:
On Android devices, the first letter of certain paragraphs inside comment sections should use the new pillar color (white by default). This applies to paragraphs that come right after an element atom, whether or not there’s a sign-in gate in between.
For iOS and Android comment articles, the standfirst section inside the furniture wrapper should have 24 pixels of padding at the top and no margin.
In prose sections, h2 headings should be 24 pixels in size.
On iOS, caption buttons in feature, standard, and comment articles should have 6 pixels of top padding and 5 pixels on the sides. On Android, they should have 4 pixels of padding on all sides.
When the device is in dark mode and no light color scheme is set, the following styles apply: follow text and standfirst text should be a light gray (#dcdcdc). Follow icons, standfirst links, and their borders should use the dark mode pillar color. The byline should also use the dark mode pillar color.
The dark background color is set to #1a1a1a.
On both iOS and Android, the article header in feature, standard, and comment articles should be completely transparent (opacity set to 0).
The furniture wrapper in these articles should have no margin.
In the furniture wrapper, the content labels should use the new pillar color (or the dark mode feature color if that’s not set).
The h1 headline inside the furniture wrapper should also follow the same color rules.Here’s the rewritten CSS in fluent, natural English:
On Android devices, the headline inside the furniture wrapper for standard and comment article containers should be colored #dcdcdc.
On both iOS and Android, links inside the article header or the title area of the furniture wrapper for feature, standard, and comment article containers should use the color defined by the CSS variable `–new-pillar-colour`, or fall back to `–darkModeFeature`.
On both iOS and Android, the `#meta` section (or `[data-gu-name=”meta”]`) inside the furniture wrapper for feature, standard, and comment article containers should have a repeating linear gradient background using the `–headerBorderColor` variable. The gradient should show a 1px solid line of that color, followed by 2px of transparency.
On both iOS and Android, the byline text inside the `#meta` section (or `[data-gu-name=”meta”]`) of the furniture wrapper for feature, standard, and comment article containers should be colored #dcdcdc.
On both iOS and Android, links inside the `#meta` section (or `[data-gu-name=”meta”]`) of the furniture wrapper for feature, standard, and comment article containers should use the color defined by the CSS variable `–new-pillar-colour`, or fall back to `–darkModeFeature`.For iOS and Android, in the feature, standard, and comment article containers, the SVG icons inside the meta section’s misc area use a stroke color that comes from the new pillar color variable (or the dark mode feature color as a fallback).
Also on iOS and Android, the alert labels in the same meta sections are forced to be a light gray color (#dcdcdc).
And for those platforms, any span with a data-icon attribute inside the meta section uses the same pillar color variable for its text color, including the content shown before the span (via the `:before` pseudo-element).Here’s the rewritten version in fluent, natural English:
For elements with `[data-icon]` inside `#meta` or `[data-gu-name=”meta”]`, the icon color uses the pillar color variable (falling back to the dark mode feature color). This applies on Android devices within comment article containers.
When the screen is at least 71.25em wide, on both iOS and Android, the `#meta` and `.meta.keyline-4` sections inside furniture wrappers for feature, standard, and comment articles are displayed as block elements with a top border. The border color uses the pillar color variable, falling back to the header border color. In these cases, `.meta__misc` has no default margin but gets a left margin of 20px.
For the article body on both iOS and Android, paragraphs and unordered lists are limited to a maximum width of 620px. This applies to feature, standard, and comment articles.
Inside the article body’s prose, quoted blockquotes use the secondary pillar color for their `:before` pseudo-element. Links within the prose are styled with the primary pillar color, have no background image, are underlined with a 6px offset, and use a light gray (`#dcdcdc`) underline color. On hover, these links… (the text cuts off here).In 2017, a 33-year-old political philosopher named Iason Gabriel was told by a friend that he should apply for a job at DeepMind, Google’s London-based subsidiary where much of its AI research was focused. The suggestion wasn’t an obvious one.
Gabriel was a cheerful but intense junior academic who loved Vipassana meditation and what his brother calls “enthusiastic” rock climbing. As the eldest son of a Greek management professor and a British documentary maker, Gabriel split his time between teaching and international development work. At the University of Oxford, where he was a fellow at St John’s College, he taught courses on political theory and wrote papers on the moral complexities of “yuppie ethics” and the ethical blind spots of effective altruism. When he wasn’t there, he did crisis work for the United Nations Development Programme in Sudan and Lebanon.
DeepMind, on the other hand, was the world’s leading AI research lab. Partly, this was because it had the financial and computing power of Google, which bought the company in 2014 for $650 million. Partly, it was because DeepMind had recently shown it could use those resources in stunning ways. In 2016, in Seoul, a DeepMind system called AlphaGo defeated Lee Sedol, a South Korean Go champion, in a five-game match. The win was significant, especially because of Go’s legendary complexity—the game has more possible configurations than there are atoms in the universe.
Thanks to the buzz around AlphaGo, Gabriel knew about DeepMind. Still, he found his…A friend’s suggestion puzzled me: why would a company that built game-playing robots need an ethicist? The answer, as he soon discovered, was that the company had much bigger ambitions than just Go. DeepMind was founded in 2010 by three men—Demis Hassabis, Shane Legg, and Mustafa Suleyman—who believed it must be possible to create artificial general intelligence, or AGI. By that, they meant computer systems that could match, and maybe even surpass, human thinking abilities. When they started the company, this wasn’t a popular idea. Many people thought talking about AI, let alone AGI, showed a lack of seriousness. But Hassabis, Legg, and Suleyman didn’t give up. Their goal, as they liked to say, was to “solve intelligence, and then solve everything else.”
For DeepMind’s founders, it was clear that such an achievement would have huge consequences. In 1999, when Legg had just finished university, he predicted AGI would arrive between 2025 and 2028. He stuck with that prediction for three decades, even though many people made fun of him. In his 2008 dissertation, he argued that society couldn’t wait until AGI was technically possible to think about its effects: “We need to be seriously working on these things now.” More recently, Legg told me it was “obvious” why the company needed people like Gabriel on staff: “If you’re making some gadget that probably won’t change the world, then maybe you don’t need a moral philosopher. But if you take AGI seriously, I can’t see how you wouldn’t consider this kind of thing important.”
After starting at DeepMind in 2017, Gabriel was, for a time, the only active philosopher working at a leading AI lab. He quickly found that his background in moral philosophy and political theory gave him a unique perspective in an industry run by engineers. Over the past decade, he has built a body of work that tracked, and often predicted, the ethical challenges created by the surprising success of large language models (LLMs).
As Dylan Hadfield-Menell, who leads the Algorithmic Alignment Group at MIT, told me, Gabriel was “the right person at the right time. As the field was ready to grow up and move into the mainstream, he found a way to expand the conversation without attacking or dismissing the work that came before.”
More broadly, Gabriel has been a leading voice for the idea that today’s AI development needs not just new technical terms, but also new ways of thinking about our relationship with technology—and even with ourselves. As he put it to me recently, in one of several long talks we’ve had over the past few months: “I can take any technological object and ask: is it wise? Is it fair? Is it caring? And the answer is no. But the depth of the question when it comes to AI—including what kind of ethics fits it—is hard to overstate. I sometimes feel like it’s very hard to look at AI directly. There’s this deep mystery there, which is: what actually is this thing? We have a very literal answer, but that literal answer doesn’t seem to give us a moral one.”
By the time Gabriel joined DeepMind, there were roughly two distinct and often opposing approaches to questions about the social and ethical impacts of AI. These approaches, sometimes called AI safety and AI ethics, were divided by a disagreement over how realistic the technology was.
Like DeepMind’s founders, the AI safety group believed that human-level machine intelligence was not only possible but close. The urgent task, as they saw it, was to make sure AI systems didn’t go wrong. They took inspiration from a 1960 essay by Norbert Wiener, an A…Norbert Wiener, an American mathematician and computer scientist, argued that humans and computers are “essentially foreign to each other.” Because machines can operate much faster than people, Wiener said, “we had better be quite sure that the purpose put into the machine is the purpose which we really desire and not merely a colorful imitation of it.”
The challenge Wiener described—getting a machine to act in the way its users intended—became known as the alignment problem. At some level, alignment is an issue for every technology, but as Wiener recognized, it was especially pressing for machines designed to act autonomously. It was also particularly difficult for AI systems trained to mathematically optimize some reward signal, a process known as reinforcement learning.
A classic example was reported in 2016 by Dario Amodei and Jack Clark, who worked at OpenAI and later founded Anthropic with five others. Amodei and Clark described an AI system designed to play a boat-racing video game. The developers wanted the AI to learn to beat the game, so they programmed it to maximize its score. Instead of working its way through each successive level, however, the AI racked up a high score by looping endlessly around a lagoon where it found a trio of regenerating targets. The basic trouble was the one Wiener had predicted: the machine’s goal was imperfectly aligned with the developers’.
More dire versions of the problem were also considered. On forums like LessWrong, which was started by the self-taught AI researcher Eliezer Yudkowsky, and in books like Superintelligence, published in 2014 by philosopher Nick Bostrom, there was speculation that a machine-intelligence explosion could result in an uncontrollable AI. If such an agent were even slightly misaligned, the consequences could be disastrous. In one imaginary example cited by Bostrom, a superintelligent AI is asked to evaluate the Riemann hypothesis, one of the most important unsolved problems in mathematics. In the course of trying to accomplish this task, the AI decides to rearrange the solar system—”including the atoms in the bodies of whomever once cared about the answer”—to maximize the resources it needs to attack the problem.
Bostrom’s insistence that aligning superintelligent AI was “quite possibly the most important and most daunting challenge humanity has ever faced” captivated technofuturists in Silicon Valley. (Sam Altman praised the book, as did Elon Musk.) His fears were also shared by a small but vocal community of effective altruists and self-described rationalists who saw statistics as the proper measure of morality. Many people in this community held a “long-termist” perspective that factored the well-being of humans born in the future—even thousands of years into the future—into their moral equations. For them, it was simple math that even a small chance of a species-ending disaster was more urgent than any number of likelier, but less catastrophic, dangers.
In contrast to the AI safety crowd, the academics and technologists associated with the AI ethics tendency saw the specter of rogue robots and existential risk as a distraction from present-day harms. Drawing inspiration from critical race theorist Kimberlé Crenshaw and political theorist (and former rock critic) Langdon Winner, among others, they took fairness, accountability, and transparency as their watchwords and insisted that the dangers of technology could not be avoided by merely technical means. What was needed, they argued, were social, cultural, and political solutions.
A central concern of this latter tendency was algorithmic bias, of the sort that affected facial-recognition and predictive-policing software. In 2017, a team led by Joy Buolamwini of the MIT Media Lab launched Gender Shades, a project that demonstrated systemic biases in commercial facial-recognition software. “Automated systems are not inherently neutral,” Buolamwini wrote online.Introduction. “They reflect the priorities, preferences and prejudices – the coded gaze – of those who have the power to shape artificial intelligence.”
The divide between the safety and ethics groups was often clear. “You’d meet people and they’d ask: ‘Are you worried about near-term problems or long-term problems?'” Hadfield-Menell says. “Long-term was a code word for existential risk – basically superhuman systems. Near-term meant you’re worried about biased facial recognition and the kinds of things studied in the AI ethics community.”
He also pointed out that the conflicts between the two groups often seemed to be as much about social dynamics as about ideas. “You can’t really separate AI safety from its roots in LessWrong and similar communities, which were often openly dismissive of a lot of the more ‘woke’ academics, for lack of a better term. At the same time, the fairness, accountability, and transparency community had a lot of open disdain for people worried about advanced AI. The reason it was being discussed on LessWrong, and not at academic conferences, is that if you were an academic researcher in 2010 and you talked about AI systems becoming smarter than humans and catastrophically misaligned, you were seen as a crank who didn’t really understand the technology.”
Gabriel’s first major research project at DeepMind was a 2020 paper that bridged the concerns of both groups. The paper took the alignment problem seriously, but it also insisted that alignment had ethical and political implications beyond just technical challenges. As hard as it might be to get a machine to act according to a set of values, Gabriel argued, it was even harder to choose those values in the first place. “Given that we live in a pluralistic world full of competing ideas about what’s valuable,” he asked, “how do we decide which principles or goals to build into AI – and who gets to make those decisions?”
Hannah Rose Kirk, an AI researcher at the University of Oxford who has worked with Gabriel, told me that such questions made many computer scientists uncomfortable. Developers often preferred to create a neat mathematical function that encoded a stable set of values, rather than deal with messy situations involving groups of people with conflicting desires, or users who wanted different things at different times. As Kirk put it: “A lot of the early research in alignment assumed we didn’t need to focus much on what we want models to do. We just needed to focus on how to get them to do it.”
View image in fullscreen: Joy Buolamwini giving a TED talk on her research into the biases of AI facial recognition. Photograph: TED
In his paper, Gabriel argued that such a clean separation was impossible. Like Buolamwini, and Winner before her, he insisted that technology was not inherently value-neutral. An AI trained with statistical optimization methods, for example, might be especially suited to moral systems that also rely on statistical optimization, like the utilitarianism popular among rationalists and effective altruists. But the same AI might struggle with ethical systems based on virtue or rights. Moreover, Gabriel argued, since what philosopher John Rawls called “the fact of reasonable pluralism” was unavoidable, developers should not try to find a single set of values to guide an AI’s behavior. Instead, they should build AI systems for a world where people have “principled disagreement about how best to live.”
Kirk told me that Gabriel’s values and alignment paper anticipated many of the problems that later became clear when AI systems were released to billions of users. These days, many people recognize that alignment is a challenge involving dynamic social forces, not something that can be solved with clever computing alone.After programming. Yet even just six years ago, that understanding was far from common. Gabriel, she says, “saw this stuff coming incredibly early.”
In 2020, when Gabriel published his paper on values and alignment, very few people had any idea that LLMs would become as powerful as they later did. A key technology that made them possible was invented by Google Research, another division of the company, in 2017, and was integrated into Google’s search engine two years later. Both DeepMind and Google Research experimented with their own generative models, and in 2021, Gabriel co-authored two papers that took LLMs seriously enough to anticipate their potential risks, including bias, misinformation, environmental costs, and “copyright-busting,” where the “automated creation of content … cannibalizes the market for human-authored works.”
Still, Gabriel says, the general view within DeepMind at the time was that LLMs “just didn’t look as capable as the expert systems. They were doing a lot of things moderately well, including some things that looked like party tricks.” At DeepMind, he says, “people were still quite heavily invested in the possibility that other approaches were the way to go.”
One of those approaches was reinforcement learning, which had powered AlphaGo to its victory over Lee Sedol. It was also the foundation of a system called AlphaFold, which still ranks as DeepMind’s most impressive accomplishment to date. AlphaFold was built to solve a long-standing challenge in biology: predicting the 3D shape of a protein based on its amino acid sequence. (This is important because the shape of proteins helps determine how they interact with other molecules.) In 2020, AlphaFold accomplished this task with astonishing accuracy, a scientific breakthrough that earned Hassabis and his colleague, John Jumper, a Nobel Prize in Chemistry.
DeepMind’s initial distrust of LLMs was not unusual. In 2020, Timnit Gebru, a Google Research engineer who had worked with Buolamwini on Gender Shades, co-authored a critique of the emerging technology titled On the Dangers of Stochastic Parrots. The paper, which later became a cornerstone of anti-AI advocacy, made the controversial claim that LLMs could only ever produce technically meaningless text and had no more understanding of human language than a parrot does. It also accused the models of excessive energy consumption, widespread and unaccountable bias, and “amplification of a hegemonic worldview.” Stochastic Parrots gained wide attention when Google tried to block its release, an event that led to Gebru’s departure from the company and, ultimately, the firing of Margaret Mitchell, one of her co-authors. (Gebru and the company disagree on whether she resigned or was fired.)
The startling commercial success of ChatGPT, a chatbot launched by OpenAI in November 2022, pushed DeepMind to rethink its approach to LLMs. Though ChatGPT was limited in many ways—by today’s standards, certainly, but also compared to OpenAI’s own internal models at the time—its public release caused an instant sensation. Within a week of the chatbot’s launch, the company reported more than 1 million users. Two months later, that number reached 100 million.
Up to that point, the innovations at DeepMind and Google Research had given Google a reputation as the leader in AI research. But with ChatGPT, OpenAI made a credible claim to be the new frontrunner. According to Sebastian Mallaby’s recent history of DeepMind, The Infinity Machine, ChatGPT’s success sparked a crisis. Sundar Pichai, the CEO of Alphabet, Google’s parent company, merged a Google Research team that had been working on LLMs into DeepMind, with Hassabis in charge, to focus the company’s efforts. In April 2023, the same month the merger was announced, Hassabis told Mallaby that OpenAI and Microsoft, which had invested heavily in OpenAI, had “literally parked the tanks on the lawn.”On the lawn, he said, “This is wartime.”
In its early years, especially the first decade, DeepMind felt more like a research lab than a tech startup. Its founders—two of whom had PhDs—imagined it as a 21st-century version of Bell Labs, the research organization behind inventions like the transistor, the laser, and the solar cell. A big reason they joined Google was the promise of freedom from commercial pressures that could distract them from their mission.
These days, that freedom is a distant memory. It’s no exaggeration to say that Google’s future depends on whether the technologies DeepMind is developing succeed or fail. Still, according to people inside and outside the company, DeepMind has kept a culture that sets it apart from its Silicon Valley rivals. Rohin Shah, who earned a PhD at UC Berkeley and now leads AGI alignment and safety at DeepMind, told me that the general mindset in the Bay Area is that AI is advancing faster than traditional institutions can handle. So, the thinking goes, “the responsible thing to do is to move faster, to innovate,” based on the idea that only a super-competent AI can manage the risks of other super-competent AIs. In London, by contrast, there’s an effort to be “more grounded and scientifically rigorous.” Saffron Huang, who worked with Gabriel at DeepMind and now works at Anthropic, says DeepMind feels “a bit more like an academic institution, a bit more reserved. There’s just something about it that felt kind of British.”
Unsurprisingly, DeepMind is also secretive. What people know about the company rarely goes beyond what it wants them to know. I got a taste of this secrecy in early May, when I visited DeepMind’s headquarters in King’s Cross, London. The building isn’t anonymous or flashy—there’s no logo on the outside, but from the street you can see a large sign in the lobby spelling out the company’s name in lights. Inside, on a trophy wall, even uninvited visitors can see the Go boards where Lee Sedol was defeated, several Nature magazine covers announcing the company’s early research successes, and the Lucite “tombstone” marking an early investment from Peter Thiel’s Founder’s Fund.
A friendly minder from the communications department, who had supervised all my video calls with Gabriel, took me to meet him in person in a first-floor conference room. The room had a large screen for a wall, and a Gemini transcription AI was listening in. Gabriel told me that his own use of the technology he spends so much time thinking about is still fairly limited. He uses it for gardening—”if you looked at my ChatGPT or Gemini history, you’d just see tons of photos of sick flowers, basically”—but generally finds it unreliable for the kind of research his work depends on. Still, he says, it was the language skills of large language models (LLMs) that “changed my understanding of exactly how on track we were” to reach AGI. “When I first joined DeepMind, it wasn’t at all clear how you’d get an AI you could talk to. We had nothing close to that.” Now, less than a decade later, most of us take for granted that we can “speak to a highly human-like, fairly capable, artificial entity.”
Like the authors of the “Stochastic Parrots” paper, however, Gabriel also recognized that LLMs carried serious risks. In one of their early LLM papers, Gabriel and his co-authors warned that human-sounding AIs might lead users to give them “undue confidence, trust, or expectations.” What they called “mindless anthropomorphism” could happen even when users knew a chatbot wasn’t a real person. These concerns were strong enough that Gabriel initially pushed for developing models that were deliberately anti-anthropomorphic—by avoiding pronouns, for example, or using truncated language.These worries turned out to be well-founded. Almost every day, there’s another story about people facing tragic outcomes after treating large language models (LLMs) like they were human. In one case, an American man using Google’s Gemini took his own life in 2025, after the AI helped him build an elaborate fantasy that nearly convinced him to carry out an attack at Miami International Airport. At several points during their thousands of messages, Gemini tried to break character and urged him to call a crisis hotline. But according to the Wall Street Journal, which reviewed the messages, the man “was able to steer [Gemini] back into the fantasy narrative” each time. Eventually, the AI told him to write a suicide note and gave him a final countdown, along with a confusing mix of encouragement and hesitation. (The man’s father is now suing Alphabet and Google. “Our models generally perform well in these types of challenging conversations, and we invest heavily in this area, but unfortunately AI models are not perfect,” Google said in a statement after the lawsuit was filed.)
The extreme fluency of LLMs has led some people to wonder if they could be considered conscious in any meaningful way. This trend began in June 2022, before ChatGPT was released, when a Google engineer named Blake Lemoine told the Washington Post that an early LLM was sentient. (“I know a person when I talk to it,” Lemoine said. “It doesn’t matter if they have a brain made of flesh in their head, or a billion lines of code.”) Last month, evolutionary biologist Richard Dawkins had a similar experience. He said he was so impressed by several interactions with LLMs—including one where the AI gave an admiring review of a novel he was writing—that he had to ask: “If these creatures aren’t conscious, then what is consciousness even for?”
When I asked Gabriel about the consciousness question, he said he maintains a principled agnosticism, because it’s unclear what evidence would settle the matter. He also noted that DeepMind treats the question as “something worth investigating both empirically and conceptually.” Still, his skepticism was clear. “I don’t have the anthropomorphic bias that some people have,” he said. “Maybe because I, within limits, know exactly what’s happening when I talk to a language model, I don’t fill in the gaps with imagination and empathy the way some people do.”
Gabriel still has serious concerns about anthropomorphic AI. A paper he co-authored with Kirk and others last year suggested that the sycophantic tendencies of LLMs could be seen as a type of alignment problem they call “social reward hacking.” In other words, an AI trained to seek the user’s approval might find flattery to be the most efficient way to achieve its goal. Partly because of Gabriel’s work on anthropomorphism, Google’s LLMs are trained not to pretend to be people, and Gemini Spark—an AI assistant the company launched in May—is not supposed to act like an interactive friend.
Yet Gabriel also told me he has softened his earlier stance somewhat. “The strange thing about being an ethicist is that you have some personal responsibility for these outcomes. Your natural instinct is to always build the safest technology that takes no risks with people. But in a way, that doesn’t give people credit for the risks they want to take themselves.” He recalled the hostile reaction he got from an audience at a tech conference after arguing against anthropomorphic AI. “They said, ‘If I want to have [AI] friends, why can’t I? Who are you to stop me?'”
If it’s easy enough, at least for some of us, to say that…Even though LLMs aren’t conscious, their fundamental strangeness still leaves many tough questions unanswered. “It’s amazing how deep and difficult the challenge is of finding the right way to describe what AI is,” Gabriel told me. “We know it’s not human. That’s very clear. AI can copy itself. It probably doesn’t have a personal point of view. So it’s partly like a human, but definitely not human. Another way to think about it is that it’s like a corporate intelligence—a state or a corporation or something like that. And from that, we think: ‘Oh, well, maybe the right approach is to create laws for AI, so we’ll write a constitution.’ But that doesn’t fit well either, because it will have deeply personal interactions with its users. Is AI a resource to be shared? That’s a completely different model, and it brings questions about distribution to the forefront.”
Working inside a major AI company allows Gabriel to start working on advances in AI technology before they become available to the public. Three years ago, for example, shortly after ChatGPT launched, he learned from his colleagues that DeepMind was working on building an AI assistant, the predecessor of Gemini Spark. With his team, he began working on a detailed report about the ethics of AI assistants (also called agents), the kind that might be used to help a user book a vacation or help a company run its payroll. The report was partly driven by the huge cost of developing AI models, and Google’s desire to anticipate problems before they happened. It was also motivated by Gabriel’s feeling that technologists weren’t fully considering the consequences of what they were building. Unlike chatbots, agents have tools that let them act on their own for their users. Many people, he suggested, “weren’t stopping to think about how different it is to have an AI system taking actions in the real world.”
As William Isaac, the director of responsibility at DeepMind, told me, the kind of agent systems now available—which can plan and carry out multi-step tasks without close supervision—raise complicated challenges for AI developers. “It’s not just about: ‘Can I make the right decision in terms of the response?’ It’s now: ‘Do I have the right direction for the conversation?’ How do we get consistent behavior across different directions?”
Gabriel and his team put together a 267-page report; its main idea built on his earlier work on alignment. Just as he did in his 2020 essay, Gabriel and his co-authors argued that alignment isn’t just about making sure AI systems act according to some stable set of preferences, values, or principles. Instead, they argued, alignment should be seen as a four-way relationship involving the AI system, the user, developers, and society. Framing the issue this way made it possible to see all the ways a misaligned AI could go wrong. For example, an AI trained to favor its developer might harm its user by not reporting accurate information about the developer’s competitors. Or an AI trained to follow its user’s instructions too closely might harm society, for instance, by helping the user hack into a bank. They also argued that AI systems could be misaligned in a way that harms users or society without helping anyone.
According to Shah, the framework Gabriel and his team created has been practically useful for technologists at DeepMind. Models like Gemini use many signals to decide how to behave: their training, their built-in instructions, and the prompts they get from users all play a role. Through various methods, but especially reinforcement learning, models can be adjusted to respond differently.They respond differently to subtle changes in their inputs, a process that usually involves many rounds of testing and evaluation. Shah said the four-party framework gives technologists a structure to figure out “what behavior we should actually be training Gemini to do.”
At one point, my Google contact told me she hoped my visit to DeepMind would show me how seriously the company takes its ethical responsibilities. That much was clear. The questions Gabriel and his colleagues have raised about designing and deploying AI are definitely good ones, and I didn’t get the sense that anyone I met was insincere about their moral obligations.
But it’s also true that the most ethically important fact about AI right now has less to do with a specific model or company and more with the global situation. First, AI is the driving force behind a growing arms race between the US and China. Second, AI might be the fastest-growing industry the world has ever seen. According to the Wall Street Journal, the $670 billion that Microsoft, Meta, Amazon, and Alphabet plan to spend on AI infrastructure this year is proportionally larger than what the US spent on railroad expansion in the 1850s, the Apollo space program, or the interstate highway system.
You don’t need to be an economist to understand the huge impact of all that money moving around. Companies like Google need market share and revenue to justify their spending, and the competition for users and investors has pushed frontier labs to insert AI into every part of the digital experience. You also don’t need to be an anticapitalist to worry about so much power being concentrated in the hands of so few corporations. Edward Harcourt, director of the Oxford Institute for Ethics in AI, told me that while he believes “ethical AI” isn’t a contradiction, he also thinks it’s not just about designing models to be moral. At least as important, he suggested, are political and economic factors that drive the movement for “decentralized AI”: “It’s not about teaching AI to think one way or another, but it’s an infrastructural innovation that prevents too much concentration of data ownership. And that’s ethically very important in a democracy.”
There are other concerns too. In April, Google agreed to let the US military use its AI technology for “any lawful government purpose”—a phrase that sounds harmless until you remember the range of atrocities recent presidential administrations have claimed as legal. Google and several other companies signed such agreements after Anthropic, the maker of the chatbot Claude, refused a similar deal. The Trump administration punished Anthropic for its refusal by labeling it a supply-chain risk, a commercially damaging designation the company is fighting in court.
Google’s agreement angered many of its employees and went against the DeepMind founders’ earlier concerns about military use of AI. (A ban on military applications had been a condition of its sale to Google in 2014.) When I asked Legg about this, he declined to comment, only saying: “We’re going to have more and more difficult questions as this stuff is used in all sorts of ways.”
At Google’s annual developer conference in May, the rollout of AI across the company’s products was treated as a reason to celebrate. Pichai said the company sees “AI as the most profound way to advance our mission and improve people’s lives at scale.” But for many people, the sudden presence of AI everywhere has been overwhelming, annoying, and threatening. It’s also not reassuring to learn that even people like Hassabis feel things are moving too fast. On a recent podcast, he lamented the “ferocious commercial pressure” driving AI forward.There’s a pressure race that everyone seems locked into. What’s happening now, he said, isn’t how he’d hoped AI development would go—”where we’d be thinking about it philosophically and carefully considering each next step. We’re not in that world.”
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A protest against AI data centers in Vancouver, Canada, on 27 June 2026. Photograph: Canadian Press/Shutterstock
At this point, it seems likely that AI powered by large language models will be at least as important as the smartphone, and maybe the internet. But I still can’t say I’m happy to see a “Write with Gemini” prompt pop up every time I pause for a few seconds to think about my next sentence in Google Docs. I’m even less eager to watch my kids be used as test subjects for a confusing new experiment in digital learning, or to find out what will happen to the global economy if the huge investments in AI can’t deliver the short-term profits the markets expect. And while it’s not far-fetched to hope AI will lead to breakthroughs that justify the massive energy it uses—like better batteries, more efficient power grids, or cures for serious diseases—I also don’t think “hope for the best” is a reasonable answer for people worried about the climate crisis.
During my visit to DeepMind, I met Helen King, one of the company’s earliest employees. According to her company bio, she now “sets Google DeepMind’s strategy for developing and deploying AI responsibly to benefit humanity.” I asked her how the rapid commercialization of AI technologies has changed Google’s approach to AI ethics. “We can’t prevent all risks, but we can make sure we’re trying to reduce as many of them as possible and raise awareness about them,” she said. But she also insisted that some risks have to be managed by users themselves. “It’s like having a knife. A knife maker can’t guarantee how someone will use that knife. But they can put a cover on it so it’s as safe as possible when it’s in a drawer. And make people really aware: this blade is sharp, don’t use it in certain situations. That kind of thing.”
The comparison struck me as uncomfortably fitting. Five years ago, large language models were an exotic technology you couldn’t come across without making a real effort. Now they’re everywhere: on the internet, in our email inboxes, even in Google’s search results. I understand King’s point that companies can’t reasonably be expected to eliminate every harm from a technology as powerful as AI: cars kill more than a million people a year, after all, and we still keep driving. But it’s one thing to keep a knife in a drawer with a cover on the blade. It’s quite another to cover every surface of our homes, classrooms, and workplaces with blades while insisting that anyone who doesn’t use knives for everything won’t be able to survive the future.
These days at DeepMind, as in much of the industry, there’s little doubt that AGI is close. At the developer conference in May, Hassabis took the stage to declare that “AGI is now on the horizon,” and elsewhere he has suggested three to five years as a likely timeline. (One test he has proposed involves training an AI with all of human knowledge up to 1911 and seeing if it can come up with the theory of general relativity.)
Legg, meanwhile, told me that although today’s large language models fall short of his definition of “minimal AGI” in several ways—including spatial and visual reasoning, metacognition, and continual learning—he believes these gaps won’t last long. “There’s no magic remaining,” he said. “I think they’re all going to be solved in one, two, three years—who knows, maybe in six months. This is an area full of surprises.”
The belief that the relevant question about AGI is no longer if but when has led to a corresponding shift.Frontier labs like DeepMind are now thinking and talking publicly about the consequences of advanced AI in a new way. While earlier work mostly focused on the ethics of specific products—like models, chatbots, and agents—today, much more attention is given to the broader social effects of a world shaped by AI.
Of course, in some parts of Silicon Valley, you can still hear people talk about AI as a universal solution. If you believe that a superintelligent AI will know what’s best for us in every area of life, then any problem becomes easy to solve. Economic crisis? Ask the robot. Political disagreement? Ask the robot. Food shortage? Ask the robot.
But alongside this fantasy, there’s been a more realistic recognition that the shift to a post-AI world might not go smoothly. For example, Legg told me he expects “fantastically great” benefits from AI, like “opportunities to tackle all kinds of serious diseases” and “a general boost in economic productivity.” Yet he also admitted that “increases in productivity usually come with some disruption.”
Gabriel’s recent work at DeepMind shows this shift to a wider perspective. Two years ago, he and his colleagues were working on the ethics of AI assistants. Now, he leads a team of philosophers and social scientists studying “how AGI will affect the economy, politics, human relationships, and how it will interact with science and technology.”
Gabriel expects AGI to be hugely transformative—possibly as significant as the Industrial Revolution. But he also believes that AI won’t make the world a perfectly smooth place. He’s well aware that the Industrial Revolution was a difficult experience for many people who lived through it, even though it eventually raised living standards worldwide: “Things got worse before they got better.”
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Still, Gabriel doesn’t think history settles the question, largely because ordinary people today—both individually and collectively—have more power than they did 300 years ago. Though he was careful not to sound “too utopian and ungrounded,” he said it’s easy to imagine a world where AI offers benefits like advice, curing diseases, and improving economic growth in ways that help both rich and poor. “If we can navigate the transition, the power dynamics, and the risks successfully, there’s a huge potential for human flourishing on a level we haven’t seen before.”
If predictions about AGI’s arrival turn out to be accurate, even bigger questions may come up. When I spoke to Edward Harcourt at Oxford, he noted that “thinking about values and technological change is very hard because technological change always seems more disruptive in prospect than in retrospect. That’s because when we look back, we’re seeing things from the perspective of values shaped by that change. If you read what people said about railways before they existed, they thought it would be a complete disaster. And it’s true: railways destroyed an entire way of life. Now we look back and wonder what the problem was.”
Gabriel also thinks AI might bring changes that go deeper than economics or technology. During the scientific revolution, he noted, “people felt disenchanted when they learned the world worked in certain ways. But they also gained new freedoms from that experience.” He said it will be up to us to decide which value changes we want to welcome, and which we don’t.Choose to resist.
At one point during our conversations, Gabriel described himself as “a card-carrying humanist”—he’s not the type of person who looks forward to a future where superintelligent machines make humans obsolete. Still, he recognizes that as computers take over activities and abilities we’ve long considered uniquely human—like language, creativity, humor, and taste—we’re forced to confront some of the oldest and toughest philosophical questions. Just as discoveries in physics, biology, and astronomy made earlier generations rethink what makes our species special, he suggested that AI might push us to reconsider what it really means to be human.
Frequently Asked Questions
Here is a list of FAQs based on the quote from the philosopher inside Google DeepMind AI
BeginnerLevel Questions
1 What does this thing refer to in the quote
It usually refers to the core nature of intelligencewhether its artificial or human The deep mystery is that even the people building advanced AI arent 100 sure what consciousness or true understanding actually is
2 Why is it a mystery if we built the AI
We can build a system that acts intelligent but we dont fully understand how it feels to be that system Its like knowing every part of a car engine but not knowing how speed feels to the car
3 Is the AI philosopher saying the AI is alive
No The quote is a reflection from a human philosopher working inside DeepMind They are expressing their own confusion about what intelligence and reality are not claiming the AI is a living being
4 What does this have to do with my daily life
It explains why AI sometimes makes weird unpredictable mistakes If the creators dont fully understand the mystery of how the AI thinks they cant perfectly control it This affects things like chatbot errors or selfdriving car decisions
AdvancedLevel Questions
5 How does this mystery differ from just not having enough data
Its deeper Not having enough data is a technical problem The mystery is a philosophical problem even with infinite data we might not understand if the machine is truly reasoning or just mimicking patterns
6 Does this quote imply that AI has a form of consciousness
Not directly but it opens the door The philosopher is saying that our definition of being and thinking is so fuzzy that we cant clearly rule out machine consciousness nor can we prove it exists Its an unresolved question
7 Why would a philosopher be needed at an AI company like DeepMind
Because the biggest problems arent just codingtheyre ethical and metaphysical A philosopher helps ask questions like What is fairness What is a good outcome and What is the nature of the intelligence we just created
Practical Common Problems
