A Visual Guide to How Diffusion Models Work

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A Visual Guide to How Diffusion Models Work

This article is aimed at those who want to understand exactly how Diffusion Models work, with no prior knowledge expected. I’ve tried to use illustrations wherever possible to provide visual intuitions on each part of these models. I’ve kept mathematical notation and equations to a minimum, and where they are necessary I’ve tried to define […]

Intro

I’ve framed this article around three main questions:

What exactly is it that diffusion models learn?
How and why do diffusion models work?
Once you’ve trained a model, how do you get useful stuff out of it?

The examples will be based on the glyffuser, a minimal text-to-image diffusion model that I previously implemented and wrote about. The architecture of this model is a standard text-to-image denoising diffusion model without any bells or whistles. It was trained to generate pictures of new “Chinese” glyphs from English definitions. Have a look at the picture below — even if you’re not familiar with Chinese writing, I hope you’ll agree that the generated glyphs look pretty similar to the real ones!

Random examples of glyffuser training data (left) and generated data (right).

What exactly is it that diffusion models learn?

Generative Ai models are often said to take a big pile of data and “learn” it. For text-to-image diffusion models, the data takes the form of pairs of images and descriptive text. But what exactly is it that we want the model to learn? First, let’s forget about the text for a moment and concentrate on what we are trying to generate: the images.

Probability distributions

Broadly, we can say that we want a generative AI model to learn the underlying probability distribution of the data. What does this mean? Consider the one-dimensional normal (Gaussian) distribution below, commonly written 𝒩(μ,σ²) and parameterized with mean μ = 0 and variance σ² = 1. The black curve below shows the probability density function. We can sample from it: drawing values such that over a large number of samples, the set of values reflects the underlying distribution. These days, we can simply write something like x = random.gauss(0, 1) in Python to sample from the standard normal distribution, although the computational sampling process itself is non-trivial!

Values sampled from an underlying distribution (here, the standard normal 𝒩(0,1)) can then be used to estimate the parameters of that distribution.

We could think of a set of numbers sampled from the above normal distribution as a simple dataset, like that shown as the orange histogram above. In this particular case, we can calculate the parameters of the underlying distribution using maximum likelihood estimation, i.e. by working out the mean and variance. The normal distribution estimated from the samples is shown by the dotted line above. To take some liberties with terminology, you might consider this as a simple example of “learning” an underlying probability distribution. We can also say that here we explicitly learnt the distribution, in contrast with the implicit methods that diffusion models use.

Conceptually, this is all that generative AI is doing — learning a distribution, then sampling from that distribution!

Data representations

What, then, does the underlying probability distribution of a more complex dataset look like, such as that of the image dataset we want to use to train our diffusion model?

First, we need to know what the representation of the data is. Generally, a machine learning (ML) model requires data inputs with a consistent representation, i.e. format. For the example above, it was simply numbers (scalars). For images, this representation is commonly a fixed-length vector.

The image dataset used for the glyffuser model is ~21,000 pictures of Chinese glyphs. The images are all the same size, 128 × 128 = 16384 pixels, and greyscale (single-channel color). Thus an obvious choice for the representation is a vector x of length 16384, where each element corresponds to the color of one pixel: x = (x₁,x₂,…,x₁₆₃₈₄). We can call the domain of all possible images for our dataset “pixel space”.

An example glyph with pixel values labelled (downsampled to 32 × 32 pixels for readability).

Dataset visualization

We make the assumption that our individual data samples, x, are actually sampled from an underlying probability distribution, q(x), in pixel space, much as the samples from our first example were sampled from an underlying normal distribution in 1-dimensional space. Note: the notation x ∼ q(x) is commonly used to mean: “the random variable x sampled from the probability distribution q(x).”

This distribution is clearly much more complex than a Gaussian and cannot be easily parameterized — we need to learn it with a ML model, which we’ll discuss later. First, let’s try to visualize the distribution to gain a better intution.

As humans find it difficult to see in more than 3 dimensions, we need to reduce the dimensionality of our data. A small digression on why this works: the manifold hypothesis posits that natural datasets lie on lower dimensional manifolds embedded in a higher dimensional space — think of a line embedded in a 2-D plane, or a plane embedded in 3-D space. We can use a dimensionality reduction technique such as UMAP to project our dataset from 16384 to 2 dimensions. The 2-D projection retains a lot of structure, consistent with the idea that our data lie on a lower dimensional manifold embedded in pixel space. In our UMAP, we see two large clusters corresponding to characters in which the components are arranged either horizontally (e.g. 明) or vertically (e.g. 草). An interactive version of the plot below with popups on each datapoint is linked here.

Click here for an interactive version of this plot.

Let’s now use this low-dimensional UMAP dataset as a visual shorthand for our high-dimensional dataset. Remember, we assume that these individual points have been sampled from a continuous underlying probability distribution q(x). To get a sense of what this distribution might look like, we can apply a KDE (kernel density estimation) over the UMAP dataset. (Note: this is just an approximation for visualization purposes.)

This gives a sense of what q(x) should look like: clusters of glyphs correspond to high-probability regions of the distribution. The true q(x) lies in 16384 dimensions — this is the distribution we want to learn with our diffusion model.

We showed that for a simple distribution such as the 1-D Gaussian, we could calculate the parameters (mean and variance) from our data. However, for complex distributions such as images, we need to call on ML methods. Moreover, what we will find is that for diffusion models in practice, rather than parameterizing the distribution directly, they learn it implicitly through the process of learning how to transform noise into data over many steps.

Takeaway

The aim of generative AI such as diffusion models is to learn the complex probability distributions underlying their training data and then sample from these distributions.

How and why do diffusion models work?

Diffusion models have recently come into the spotlight as a particularly effective method for learning these probability distributions. They generate convincing images by starting from pure noise and gradually refining it. To whet your interest, have a look at the animation below that shows the denoising process generating 16 samples.

In this section we’ll only talk about the mechanics of how these models work but if you’re interested in how they arose from the broader context of generative models, have a look at the further reading section below.

What is “noise”?

Let’s first precisely define noise, since the term is thrown around a lot in the context of diffusion. In particular, we are talking about Gaussian noise: consider the samples we talked about in the section about probability distributions. You could think of each sample as an image of a single pixel of noise. An image that is “pure Gaussian noise”, then, is one in which each pixel value is sampled from an independent standard Gaussian distribution, 𝒩(0,1). For a pure noise image in the domain of our glyph dataset, this would be noise drawn from 16384 separate Gaussian distributions. You can see this in the previous animation. One thing to keep in mind is that we can choose the means of these noise distributions, i.e. center them, on specific values — the pixel values of an image, for instance.

For convenience, you’ll often find the noise distributions for image datasets written as a single multivariate distribution 𝒩(0,I) where I is the identity matrix, a covariance matrix with all diagonal entries equal to 1 and zeroes elsewhere. This is simply a compact notation for a set of multiple independent Gaussians — i.e. there are no correlations between the noise on different pixels. In the basic implementations of diffusion models, only uncorrelated (a.k.a. “isotropic”) noise is used. This article contains an excellent interactive introduction on multivariate Gaussians.

Diffusion process overview

Below is an adaptation of the somewhat-famous diagram from Ho et al.’s seminal paper “Denoising Diffusion Probabilistic Models” which gives an overview of the whole diffusion process:

Diagram of the diffusion process adapted from Ho *et al*. 2020. The glyph 锂, meaning “lithium”, is used as a representative sample from the dataset.

I found that there was a lot to unpack in this diagram and simply understanding what each component meant was very helpful, so let’s go through it and define everything step by step.

We previously used x ∼ q(x) to refer to our data. Here, we’ve added a subscript, xₜ, to denote timestep t indicating how many steps of “noising” have taken place. We refer to the samples noised a given timestep as x ∼ q(xₜ). x₀ is clean data and xₜ (t = T) ∼ 𝒩(0,1) is pure noise.

We define a forward diffusion process whereby we corrupt samples with noise. This process is described by the distribution q(xₜ|xₜ₋₁). If we could access the hypothetical reverse process q(xₜ₋₁|xₜ), we could generate samples from noise. As we cannot access it directly because we would need to know x₀, we use ML to learn the parameters, θ, of a model of this process, 𝑝θ(𝑥ₜ₋₁∣𝑥ₜ). (That should be p subscript θ but medium cannot render it.)

In the following sections we go into detail on how the forward and reverse diffusion processes work.

Forward diffusion, or “noising”

Used as a verb, “noising” an image refers to applying a transformation that moves it towards pure noise by scaling down its pixel values toward 0 while adding proportional Gaussian noise. Mathematically, this transformation is a multivariate Gaussian distribution centered on the pixel values of the preceding image.

In the forward diffusion process, this noising distribution is written as q(xₜ|xₜ₋₁) where the vertical bar symbol “|” is read as “given” or “conditional on”, to indicate the pixel means are passed forward from q(xₜ₋₁) At t = T where T is a large number (commonly 1000) we aim to end up with images of pure noise (which, somewhat confusingly, is also a Gaussian distribution, as discussed previously).

The marginal distributions q(xₜ) represent the distributions that have accumulated the effects of all the previous noising steps (marginalization refers to integration over all possible conditions, which recovers the unconditioned distribution).

Since the conditional distributions are Gaussian, what about their variances? They are determined by a variance schedule that maps timesteps to variance values. Initially, an empirically determined schedule of linearly increasing values from 0.0001 to 0.02 over 1000 steps was presented in Ho et al. Later research by Nichol & Dhariwal suggested an improved cosine schedule. They state that a schedule is most effective when the rate of information destruction through noising is relatively even per step throughout the whole noising process.

Forward diffusion intuition

As we encounter Gaussian distributions both as pure noise q(xₜ, t = T) and as the noising distribution q(xₜ|xₜ₋₁), I’ll try to draw the distinction by giving a visual intuition of the distribution for a single noising step, q(x₁∣x₀), for some arbitrary, structured 2-dimensional data:

Each noising step q(xₜ|xₜ₋₁) is a Gaussian distribution conditioned on the previous step.

The distribution q(x₁∣x₀) is Gaussian, centered around each point in x₀, shown in blue. Several example points x₀⁽ⁱ⁾ are picked to illustrate this, with q(x₁∣x₀ = x₀⁽ⁱ⁾) shown in orange.

In practice, the main usage of these distributions is to generate specific instances of noised samples for training (discussed further below). We can calculate the parameters of the noising distributions at any timestep t directly from the variance schedule, as the chain of Gaussians is itself also Gaussian. This is very convenient, as we don’t need to perform noising sequentially—for any given starting data x₀⁽ⁱ⁾, we can calculate the noised sample xₜ⁽ⁱ⁾ by sampling from q(xₜ∣x₀ = x₀⁽ⁱ⁾) directly.

Forward diffusion visualization

Let’s now return to our glyph dataset (once again using the UMAP visualization as a visual shorthand). The top row of the figure below shows our dataset sampled from distributions noised to various timesteps: xₜ ∼ q(xₜ). As we increase the number of noising steps, you can see that the dataset begins to resemble pure Gaussian noise. The bottom row visualizes the underlying probability distribution q(xₜ).

The dataset xₜ (above) sampled from its probability distribution q(xₜ) (below) at different noising timesteps.

Reverse diffusion overview

It follows that if we knew the reverse distributions q(xₜ₋₁∣xₜ), we could repeatedly subtract a small amount of noise, starting from a pure noise sample xₜ at t = T to arrive at a data sample x₀ ∼ q(x₀). In practice, however, we cannot access these distributions without knowing x₀ beforehand. Intuitively, it’s easy to make a known image much noisier, but given a very noisy image, it’s much harder to guess what the original image was.

So what are we to do? Since we have a large amount of data, we can train an ML model to accurately guess the original image that any given noisy image came from. Specifically, we learn the parameters θ of an ML model that approximates the reverse noising distributions, pθ(xₜ₋₁ ∣ xₜ) for t = 0, …, T. In practice, this is embodied in a single noise prediction model trained over many different samples and timesteps. This allows it to denoise any given input, as shown in the figure below.

The ML model predicts added noise at any given timestep t.

Next, let’s go over how this noise prediction model is implemented and trained in practice.

How the model is implemented

First, we define the ML model — generally a deep neural network of some sort — that will act as our noise prediction model. This is what does the heavy lifting! In practice, any ML model that inputs and outputs data of the correct size can be used; the U-net, an architecture particularly suited to learning images, is what we use here and frequently chosen in practice. More recent models also use vision transformers.

We use the U-net architecture (Ronneberger *et al*. 2015) for our ML noise prediction model. We train the model by minimizing the difference between predicted and actual noise.

Then we run the training loop depicted in the figure above:

We take a random image from our dataset and noise it to a random timestep tt. (In practice, we speed things up by doing many examples in parallel!)
We feed the noised image into the ML model and train it to predict the (known to us) noise in the image. We also perform timestep conditioning by feeding the model a timestep embedding, a high-dimensional unique representation of the timestep, so that the model can distinguish between timesteps. This can be a vector the same size as our image directly added to the input (see here for a discussion of how this is implemented).
The model “learns” by minimizing the value of a loss function, some measure of the difference between the predicted and actual noise. The mean square error (the mean of the squares of the pixel-wise difference between the predicted and actual noise) is used in our case.
Repeat until the model is well trained.

Note: A neural network is essentially a function with a huge number of parameters (on the order of 10⁶ for the glyffuser). Neural network ML models are trained by iteratively updating their parameters using backpropagation to minimize a given loss function over many training data examples. This is an excellent introduction. These parameters effectively store the network’s “knowledge”.

A noise prediction model trained in this way eventually sees many different combinations of timesteps and data examples. The glyffuser, for example, was trained over 100 epochs (runs through the whole data set), so it saw around 2 million data samples. Through this process, the model implicity learns the reverse diffusion distributions over the entire dataset at all different timesteps. This allows the model to sample the underlying distribution q(x₀) by stepwise denoising starting from pure noise. Put another way, given an image noised to any given level, the model can predict how to reduce the noise based on its guess of what the original image. By doing this repeatedly, updating its guess of the original image each time, the model can transform any noise to a sample that lies in a high-probability region of the underlying data distribution.

Reverse diffusion in practice

We can now revisit this video of the glyffuser denoising process. Recall a large number of steps from sample to noise e.g. T = 1000 is used during training to make the noise-to-sample trajectory very easy for the model to learn, as changes between steps will be small. Does that mean we need to run 1000 denoising steps every time we want to generate a sample?

Luckily, this is not the case. Essentially, we can run the single-step noise prediction but then rescale it to any given step, although it might not be very good if the gap is too large! This allows us to approximate the full sampling trajectory with fewer steps. The video above uses 120 steps, for instance (most implementations will allow the user to set the number of sampling steps).

Recall that predicting the noise at a given step is equivalent to predicting the original image x₀, and that we can access the equation for any noised image deterministically using only the variance schedule and x₀. Thus, we can calculate xₜ₋ₖ based on any denoising step. The closer the steps are, the better the approximation will be.

Too few steps, however, and the results become worse as the steps become too large for the model to effectively approximate the denoising trajectory. If we only use 5 sampling steps, for example, the sampled characters don’t look very convincing at all:

There is then a whole literature on more advanced sampling methods beyond what we’ve discussed so far, allowing effective sampling with much fewer steps. These often reframe the sampling as a differential equation to be solved deterministically, giving an eerie quality to the sampling videos — I’ve included one at the end if you’re interested. In production-level models, these are usually preferred over the simple method discussed here, but the basic principle of deducing the noise-to-sample trajectory is the same. A full discussion is beyond the scope of this article but see e.g. this paper and its corresponding implementation in the Hugging Face diffusers library for more information.

Alternative intuition from score function

To me, it was still not 100% clear why training the model on noise prediction generalises so well. I found that an alternative interpretation of diffusion models known as “score-based modeling” filled some of the gaps in intuition (for more information, refer to Yang Song’s definitive article on the topic.)

The dataset xₜ sampled from its probability distribution q(xₜ) at different noising timesteps; below, we add the score function ∇ₓ log q(xₜ).

I try to give a visual intuition in the bottom row of the figure above: essentially, learning the noise in our diffusion model is equivalent (to a constant factor) to learning the score function, which is the gradient of the log of the probability distribution: ∇ₓ log q(x). As a gradient, the score function represents a vector field with vectors pointing towards the regions of highest probability density. Subtracting the noise at each step is then equivalent to moving following the directions in this vector field towards regions of high probability density.

As long as there is some signal, the score function effectively guides sampling, but in regions of low probability it tends towards zero as there is little to no gradient to follow. Using many steps to cover different noise levels allows us to avoid this, as we smear out the gradient field at high noise levels, allowing sampling to converge even if we start from low probability density regions of the distribution. The figure shows that as the noise level is increased, more of the domain is covered by the score function vector field.

Summary

The aim of diffusion models is learn the underlying probability distribution of a dataset and then be able to sample from it. This requires forward and reverse diffusion (noising) processes.
The forward noising process takes samples from our dataset and gradually adds Gaussian noise (pushes them off the data manifold). This forward process is computationally efficient because any level of noise can be added in closed form a single step.
The reverse noising process is challenging because we need to predict how to remove the noise at each step without knowing the original data point in advance. We train a ML model to do this by giving it many examples of data noised at different timesteps.
Using very small steps in the forward noising process makes it easier for the model to learn to reverse these steps, as the changes are small.
By applying the reverse noising process iteratively, the model refines noisy samples step by step, eventually producing a realistic data point (one that lies on the data manifold).

Takeaway

Diffusion models are a powerful framework for learning complex data distributions. The distributions are learnt implicitly by modelling a sequential denoising process. This process can then be used to generate samples similar to those in the training distribution.

Once you’ve trained a model, how do you get useful stuff out of it?

Earlier uses of generative AI such as “This Person Does Not Exist” (ca. 2019) made waves simply because it was the first time most people had seen AI-generated photorealistic human faces. A generative adversarial network or “GAN” was used in that case, but the principle remains the same: the model implicitly learnt a underlying data distribution — in that case, human faces — then sampled from it. So far, our glyffuser model does a similar thing: it samples randomly from the distribution of Chinese glyphs.

The question then arises: can we do something more useful than just sample randomly? You’ve likely already encountered text-to-image models such as Dall-E. They are able to incorporate extra meaning from text prompts into the diffusion process — this in known as conditioning. Likewise, diffusion models for scientific scientific applications like protein (e.g. Chroma, RFdiffusion, AlphaFold3) or inorganic crystal structure generation (e.g. MatterGen) become much more useful if can be conditioned to generate samples with desirable properties such as a specific symmetry, bulk modulus, or band gap.

Conditional distributions

We can consider conditioning as a way to guide the diffusion sampling process towards particular regions of our probability distribution. We mentioned conditional distributions in the context of forward diffusion. Below we show how conditioning can be thought of as reshaping a base distribution.

A simple example of a joint probability distribution p(x, y), shown as a contour map, along with its two marginal 1-D probability distributions, p(x) and p(y). The highest points of p(x, y) are at (x₁, y₁) and (x₂, y₂). The conditional distributions p(x∣y = y₁) and p(x∣y = y₂) are shown overlaid on the main plot.

Consider the figure above. Think of p(x) as a distribution we want to sample from (i.e., the images) and p(y) as conditioning information (i.e., the text dataset). These are the marginal distributions of a joint distribution p(x, y). Integrating p(x, y) over y recovers p(x), and vice versa.

Sampling from p(x), we are equally likely to get x₁ or x₂. However, we can condition on p(y = y₁) to obtain p(x∣y = y₁). You can think of this as taking a slice through p(x, y) at a given value of y. In this conditioned distribution, we are much more likely to sample at x₁ than x₂.

In practice, in order to condition on a text dataset, we need to convert the text into a numerical form. We can do this using large language model (LLM) embeddings that can be injected into the noise prediction model during training.

Embedding text with an LLM

In the glyffuser, our conditioning information is in the form of English text definitions. We have two requirements: 1) ML models prefer fixed-length vectors as input. 2) The numerical representation of our text must understand context — if we have the words “lithium” and “element” nearby, the meaning of “element” should be understood as “chemical element” rather than “heating element”. Both of these requirements can be met by using a pre-trained LLM.

The diagram below shows how an LLM converts text into fixed-length vectors. The text is first tokenized (LLMs break text into tokens, small chunks of characters, as their basic unit of interaction). Each token is converted into a base embedding, which is a fixed-length vector of the size of the LLM input. These vectors are then passed through the pre-trained LLM (here we use the encoder portion of Google’s T5 model), where they are imbued with additional contextual meaning. We end up with a array of n vectors of the same length d, i.e. a (n, d) sized tensor.

We can convert text to a numerical embedding imbued with contextual meaning using a pre-trained LLM.

Note: in some models, notably Dall-E, additional image-text alignment is performed using contrastive pretraining. Imagen seems to show that we can get away without doing this.

Training the diffusion model with text conditioning

The exact method that this embedding vector is injected into the model can vary. In Google’s Imagen model, for example, the embedding tensor is pooled (combined into a single vector in the embedding dimension) and added into the data as it passes through the noise prediction model; it is also included in a different way using cross-attention (a method of learning contextual information between sequences of tokens, most famously used in the transformer models that form the basis of LLMs like ChatGPT).

Conditioning information can be added *via* multiple different methods but the training loss remains the same.

In the glyffuser, we only use cross-attention to introduce this conditioning information. While a significant architectural change is required to introduce this additional information into the model, the loss function for our noise prediction model remains exactly the same.

Testing the conditioned diffusion model

Let’s do a simple test of the fully trained conditioned diffusion model. In the figure below, we try to denoise in a single step with the text prompt “Gold”. As touched upon in our interactive UMAP, Chinese characters often contain components known as radicals which can convey sound (phonetic radicals) or meaning (semantic radicals). A common semantic radical is derived from the character meaning “gold”, “金”, and is used in characters that are in some broad sense associated with gold or metals.

Even with a single sampling step, conditioning guides denoising towards the relevant regions of the probability distribution.

The figure shows that even though a single step is insufficient to approximate the denoising trajectory very well, we have moved into a region of our probability distribution with the “金” radical. This indicates that the text prompt is effectively guiding our sampling towards a region of the glyph probability distribution related to the meaning of the prompt. The animation below shows a 120 step denoising sequence for the same prompt, “Gold”. You can see that every generated glyph has either the 釒 or 钅 radical (the same radical in traditional and simplified Chinese, respectively).

Takeaway

Conditioning enables us to sample meaningful outputs from diffusion models.

Further remarks

I found that with the help of tutorials and existing libraries, it was possible to implement a working diffusion model despite not having a full understanding of what was going on under the hood. I think this is a good way to start learning and highly recommend Hugging Face’s tutorial on training a simple diffusion model using their diffusers Python library (which now includes my small bugfix!).

I’ve omitted some topics that are crucial to how production-grade diffusion models function, but are unnecessary for core understanding. One is the question of how to generate high resolution images. In our example, we did everything in pixel space, but this becomes very computationally expensive for large images. The general approach is to perform diffusion in a smaller space, then upscale it in a separate step. Methods include latent diffusion (used in Stable Diffusion) and cascaded super-resolution models (used in Imagen). Another topic is classifier-free guidance, a very elegant method for boosting the conditioning effect to give much better prompt adherence. I show the implementation in my previous post on the glyffuser and highly recommend this article if you want to learn more.

Fun extras

Diffusion sampling using the DPMSolverSDEScheduler developed by Katherine Crowson and implemented in Hugging Face diffusers—note the smooth transition from noise to data.

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(Update) February 11, 2026, 5:10 PM GMT: Article updated with comments on dividend growth, potential investment decisions and acquisitions from 14th paragraph. TotalEnergies SE trimmed its share buybacks to the lower end of its guidance range, aiming to keep debt in check as it adjusts to lower oil prices. The company plans to repurchase $750 million of stock in the first quarter, compared with $1.5 billion in the final three months of 2025, it said in an earnings statement Wednesday. For the year, its buyback target was kept at a range of $3 billion to $6 billion. TotalEnergies is the third and last of Europe’s top oil and gas producers to release earnings after Shell Plc and BP Plc published disappointing quarterly reports. The company has a lower ratio of debt to equity than its European peers and kept quarterly dividend unchanged. “This year we want to balance cash generation with cash expenditure,” Chief Executive Officer Patrick Pouyanne said during a press conference in Paris to discuss earnings. “We don’t know what will happen this year. We want to keep a healthy balance sheet.” Shares of Total closed 2.7% up, at their highest since July 2024. The company has a “solid balance sheet despite uncertain environment,“ Jefferies analysts led by Mark Wilson said in a note after the earnings release. While Big Oil is still churning out hefty profits, cash flows — particularly in Europe — have been undermined by last year’s 18% dive in crude prices. There are also widespread forecasts that the market will remain oversupplied this year as production swells both inside and outside the OPEC+ alliance. “Oil supply remains abundant, so the market is rather trending down,” Pouyanne said, adding that sanctions on Russia are causing a buildup of the nation’s crude at sea. Total’s adjusted

EIA Sees Brent Price Dropping in 2026 and 2027

In its latest short term energy outlook (STEO), which was released on February 10, the U.S. Energy Information Administration (EIA) projected that the average Brent spot price will drop in 2026 and 2027. According to this STEO, the EIA sees the Brent spot price coming in at $57.69 per barrel in 2026 and $53.00 per barrel in 2027. The Brent spot price averaged $69.04 per barrel in 2025, the STEO showed. A quarterly breakdown included in the EIA’s latest STEO showed that the organization expects the Brent spot price to come in at $64.44 per barrel in the first quarter of this year, $57.32 per barrel in the second quarter, $55.35 per barrel in the third quarter, $54.00 per barrel in the fourth quarter, and $53.00 per barrel across the first, second, third, and fourth quarters of next year. In the STEO, the EIA highlighted that the Brent crude oil spot price averaged $67 per barrel in January, which it pointed out was $4 per barrel higher than the average in December. The EIA noted that daily Brent crude oil prices increased from an average of $62 per barrel on January 2 to $72 per barrel on January 30. “Crude oil prices rose in response to disruptions to crude oil production in the United States and Kazakhstan,” the EIA highlighted in the STEO. “Despite the near-term increase in prices and short-term disruptions to oil supply, we forecast that strong growth in global oil production will result in high global oil inventory builds over the forecast, causing crude oil prices to fall,” it added. “We forecast that Brent spot prices will average $58 per barrel in 2026 and $53 per barrel in 2027, down from an average of $69 per barrel in 2025,” it continued. In its STEO, the EIA said

USA Allows Oilfield Contractors to Go to Work in VEN Fields

The US government issued a general license to allow oilfield-service companies to work in Venezuela as the Trump administration eases sanctions and pushes to rebuild the nation’s crude infrastructure. The license issued by the Treasury Department allows US firms to explore, develop and produce oil and natural gas in Venezuela under certain limited conditions, according to a statement Tuesday. The move is the latest in a series of steps Washington has taken to entice US companies to revive output from Venezuela’s vast crude reserves after last month’s capture of strongman Nicolás Maduro. In January, the US issued a general license that allowed for a wide range of crude operations, including exporting, transporting, refining and buying and selling crude. The general license announced Tuesday involves tasks such as geological mapping, reservoir analysis and related tasks that augment the commencement of oil production. However, the license does not allow new joint ventures in Venezuela. US people and firms will need to provide detailed plans to the State Department and Department of Energy for any work in the country, according to the statement. The Treasury Department is also preparing to issue a general license allowing companies to pump oil in Venezuela, Bloomberg reported earlier this month. Oilfield service companies are hired by producers to asses discoveries, drill wells, and enhance output from older assets. SLB Ltd., Halliburton Co. and Baker Hughes Co. dominate the sector. SLB has been working in Venezuela for Chevron Corp., operating under a US license held by the supermajor. The other large contractors scaled back or shut down their primary operations in the country as the previous regime tightened control over the energy industry. WHAT DO YOU THINK? Generated by readers, the comments included herein do not reflect the views and opinions of Rigzone. All comments are subject to editorial review. Off-topic, inappropriate

EIA Fuel Update Shows Increasing Price Trend for USA Gasoline

The U.S. Energy Information Administration’s (EIA) latest gasoline fuel update, which was released on February 10, showed an increasing trend for the U.S. regular gasoline price. According to the update, the U.S. regular gasoline price averaged $2.853 per gallon on January 26, $2.867 per gallon on February 2, and $2.902 per gallon on February 9. Although the February 9 price was up $0.035 from the week ago price, it was down $0.226 from the year ago price, the update outlined. Of the five Petroleum Administration for Defense District (PADD) regions highlighted in the EIA’s latest fuel update, the West Coast was shown to have the highest U.S. regular gasoline price as of February 9, at $3.938 per gallon. The Gulf Coast was shown in the update to have the lowest U.S. regular gasoline price as of February 9, at $2.476 per gallon. A glossary section of the EIA site notes that the 50 U.S. states and the District of Columbia are divided into five districts, with PADD 1 further split into three subdistricts. PADDs 6 and 7 encompass U.S. territories, the site adds. In a blog posted on its website on February 9, GasBuddy noted that, according to its data, the U.S. average price of gasoline “has risen 1.2 cents over the last week and stands at $2.84 per gallon”. “The national average is up 5.4 cents from a month ago and is 24.9 cents per gallon lower than a year ago,” it added. In that blog, Patrick De Haan, head of petroleum analysis at GasBuddy, said, “the national average price of gasoline only edged slightly higher last week, but nine of the ten largest weekly price movements were increases, led by West Coast states as California begins the transition to summer gasoline”. “Most states saw relatively minor fluctuations, but we’re

Energy providers seek flexible load strategies for data center operations

“In theory, yes, they’d have to wait a little bit longer while their queries are routed to a data center that has capacity,” said Lawrence. The one thing the industry cannot do is operate like it has in the past, where data center power was tuned and then forgotten for six months. Previously, data centers would test their power sources once or twice a year. They don’t have that luxury anymore. They need to check their power sources and loads far more regularly, according to Lawrence. “I think that for that for the data center industry to continue to survive like we all need it, there’s going to have to be some realignment on the incentives to why somebody would become flexible,” said Lawrence. The survey suggests that utilities and load operators expect to expand their demand response activities and budgets in the near term. Sixty-three percent of respondents anticipate DR program funding to grow by 50% or more over the next three years. While they remain a major source of load growth and system strain, 57% of respondents indicate that onsite power generation from data centers will be most important to improving grid stability over the next five years. One of the proposed fixes to the power shortage has been small modular nuclear reactors. These have gained a lot of traction in the marketplace even if they have nothing to sell yet. But Lawrence said that that’s not an ideal solution for existing power generators, ironically enough.

Nokia predicts huge WAN traffic growth, but experts question assumptions

Consumer, which includes both mobile access and fixed access, including fixed wireless access. Enterprise and industrial, which covers wide-area connectivity that supports knowledge work, automation, machine vision, robotics coordination, field support, and industrial IoT. AI, including applications that people directly invoke, such as assistants, copilots, and media generation, as well as autonomous use cases in which AI systems trigger other AI systems to perform functions and move data across networks. The report outlines three scenarios: conservative, moderate, and aggressive. “Our goal is to present scenarios that fall within a realistic range of possible outcomes, encouraging stakeholders to plan across the full spectrum of high-impact demand possibilities,” the report says. Nokia’s prediction for global WAN traffic growth ranges from a 13% CAGR for the conservative scenario to 16% CAGR for moderate and 22% CAGR for aggressive. Looking more closely at the moderate scenario, it’s clear that consumer traffic dominates. Enterprise and industrial traffic make up only about 14% to 17% of overall WAN traffic, although their share is expected to grow during the 10-year forecast period. “On the consumer side, the vast majority of traffic by volume is video,” says William Webb, CEO of the consulting firm Commcisive. Asked whether any of that consumer traffic is at some point served up by enterprises, the answer is a decisive “no.” It’s mostly YouTube and streaming services like Netflix, he says. In short, that doesn’t raise enterprise concerns. Nokia predicts AI traffic boom AI is a different story. “Consumer- and enterprise-generated AI traffic imposes a substantial impact on the wide-area network (WAN) by adding AI workloads processed by data centers across the WAN. AI traffic does not stay inside one data center; it moves across edge, metro, core, and cloud infrastructure, driving dense lateral flows and new capacity demands,” the report says. An

Cisco amps up Silicon One line, delivers new systems and optics for AI networking

Those building blocks include the new G300 as well as the G200 51.2 Tbps chip, which is aimed at spine and aggregation applications, and the G100 25.6 Tbps chip, which is aimed at leaf operations. Expanded portfolio of Silicon One P200-powered systems Cisco in October rolled out the P200 Silicon One chip and the high-end, 51.2 Tbps 8223 router aimed at distributed AI workloads. That system supports Octal Small Form-Factor Pluggable (OSFP) and Quad Small Form-Factor Pluggable Double Density (QSFP-DD) optical form factors that help the box support geographically dispersed AI clusters. Cisco grew the G200 family this week with the addition of the 8122X-64EF-O, a 64x800G switch that will run the SONiC OS and includes support for Cisco 800G Linear Pluggable Optics (LPO) connectivity. LPO components typically set up direct links between fiber optic modules, eliminating the need for traditional components such as a digital signal processor. Cisco said its P200 systems running IOS XR software now better support core routing services to allow data-center-to-data-center links and data center interconnect applications. In addition, Cisco introduced a P200-powered 88-LC2-36EF-M line card, which delivers 28.8T of capacity. “Available for both our 8-slot and 18-slot modular systems, this line card enables up to an unprecedented 518.4T of total system bandwidth, the highest in the industry,” wrote Guru Shenoy, senior vice president of the Cisco provider connectivity group, in a blog post about the news. “When paired with Cisco 800G ZR/ZR+ coherent pluggable optics, these systems can easily connect sites over 1,000 kilometers apart, providing the high-density performance needed for modern data center interconnects and core routing.”

NetBox Labs ships AI copilot designed for network engineers, not developers

Natural language for network engineers Beevers explained that network operations teams face two fundamental barriers to automation. First, they lack accurate data about their infrastructure. Second, they aren’t software developers and shouldn’t have to become them. “These are not software developers. They are network engineers or IT infrastructure engineers,” Beevers said. “The big realization for us through the copilot journey is they will never be software developers. Let’s stop trying to make them be. Let’s let these computers that are really good at being software developers do that, and let’s let the network engineers or the data center engineers be really good at what they’re really good at.” That vision drove the development of NetBox Copilot’s natural language interface and its capabilities. Grounding AI in infrastructure reality The challenge with deploying AI in network operations is trust. Generic large language models hallucinate, produce inconsistent results, and lack the operational context to make reliable decisions. NetBox Copilot addresses this by grounding the AI agent in NetBox’s comprehensive infrastructure data model. NetBox serves as the system of record for network and infrastructure teams, maintaining a semantic map of devices, connections, IP addressing, rack layouts, power distribution and the relationships between these elements. Copilot has native awareness of this data structure and the context it provides. This enables queries that would be difficult or impossible with traditional interfaces. Network engineers can ask “Which devices are missing IP addresses?” to validate data completeness, “Who changed this prefix last week?” for change tracking and compliance, or “What depends on this switch?” for impact analysis before maintenance windows.

US pushes voluntary pact to curb AI data center energy impact

Others note that cost pressure isn’t limited to the server rack. Danish Faruqui, CEO of Fab Economics, said the AI ecosystem is layered from silicon to software services, creating multiple points where infrastructure expenses eventually resurface. “Cloud service providers are likely to gradually introduce more granular pricing models across cloud, AI, and SaaS offerings, tailored by customer type, as they work to absorb the costs associated with the White House energy and grid compact,” Faruqui said. This may not show up as explicit energy surcharges, but instead surface through reduced discounts, higher spending commitments, and premiums for guaranteed capacity or performance. “Smaller enterprises will feel the impact first, while large strategic customers remain insulated longer,” Rawat said. “Ultimately, the compact would delay and redistribute cost pressure; it does not eliminate it.” Implications for data center design The proposal is also likely to accelerate changes in how AI facilities are designed. “Data centers will evolve into localized microgrids that combine utility power with on-site generation and higher-level implementation of battery energy storage systems,” Faruqui said. “Designing for grid interaction will become imperative for AI data centers, requiring intelligent, high-speed switching gear, increased battery energy storage capacity for frequency regulation, and advanced control systems that can manage on-site resources.”

Intel teams with SoftBank to develop new memory type

However, don’t expect anything anytime soon. Intel’s Director of Global Strategic Partnerships Sanam Masroor outlined the plans in a blog post. Operations are expected to begin in Q1 2026, with prototypes due in 2027 and commercial products by 2030. While Intel has not come out and said it, that memory design is almost identical to HBM used in GPU accelerators and AI data centers. HBM sits right on the GPU die for immediate access to the GPU, unlike standard DRAM which resides on memory sticks plugged into the motherboard. HBM is much faster than DDR memory but is also much more expensive to produce. It’s also much more profitable than standard DRAM which is why the big three memory makers – Micron, Samsung, and SK Hynix – are favoring production of it.

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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