The Hayekian Triangle: Why Liberty Is Not Conservatism

 Based on the provided source, here is the full text of the article "On Being A Conservative: Lessons from Friedrich Hayek" by Cass Sunstein:

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On Being A Conservative

Lessons from Friedrich Hayek

CASS SUNSTEIN JUL 20, 2026

1 Friedrich Hayek is a great hero to conservatives, and rightly so. He revered traditions, despised socialism, and favored free markets. Hayek helps to define modern conservatism. And yet one of his most interesting and vivid essays has this title: Why I Am Not A Conservative.

Well, why wasn’t he? And what might we learn from his clear desire to distinguish himself from conservatives?

2 In the relevant essay, written in the late 1950s and published in 1960, Hayek rejected the idea that socialists are on the left, conservatives on the right, and liberals in the middle. He didn’t like the idea of some kind of line from left to right. He wrote: “Nothing could be more misleading. If we want a diagram, it would be more appropriate to arrange them in a triangle with the conservatives occupying one corner, with the socialists pulling toward the second and the liberals toward the third.” Three corners, not a straight line.

3 Hayek had plenty of nice things to say about conservatives and conservatism. His essay is written in sadness, not in anger. (It was apparently started as a paper delivered to the Mount Pelerin Society in 1957, sharply critical of the traditionalism associated with Russell Kirk, who apparently delivered an extemporaneous response at the time. That must have been quite an occasion.)

In tribute to conservatism, Hayek said this: “To their loving and reverential study of the value of grown institutions we owe (at least outside the field of economics) some profound insights which are real contributions to our understanding of a free society.” Hayek shared with conservatives that reverence for grown institutions, the beneficial products of social processes, not of any individual designer.

With that justified reverence, conservatives “did show an understanding of the meaning of spontaneously grown institutions such as language, law, morals, and conventions that anticipated modern scientific approaches and from which the liberals might have profited.” This understanding was central to Hayek’s own thinking; it was even defining. Liberalism, as Hayek understood and championed it, shares that reverence, and hence is deferential to traditions. It never sneers at them. This is a close connection here between Hayek and Burke (and on Hayek’s account, Adam Smith as well).

Divergent attitudes toward traditions help to explain Hayek’s split from John Stuart Mill (and from other liberals). In Hayek’s telling, Mill was a rationalist who thought that human reason could be used to challenge traditions. Hayek did not like that at all. He thought that it was a “fatal conceit.” Hayek well knew that many contemporary liberals follow Mill, and he lamented that. Hayek’s preferred form of liberalism, which he believed to be the truest form, treasures not arrogant reason, but invisible hands, undesigned orders, the common law, the rule of law, and customs.

Designs without designers—they are fundamental to Hayekian liberalism and to conservatism alike. Hayek did not want to freeze any status quo, but like conservatives, he tended to favor incrementalism rather than large-scale change. He greatly admired Burke. “There would not be much to object to if the conservatives merely disliked too rapid change in institutions and public policy; here the case for caution and slow process is indeed strong. “

4 In 1955, William F. Buckley, Jr., a defining American conservative, wrote that his new magazine, and the conservative movement as he would like it to be, “stands athwart history, yelling Stop.” That’s a great line. It’s exactly what Hayek rejected. I don’t know if Hayek knew of the line, but I suspect so. You can see his essay as a direct response to it.

5 Hayek identified the following as “the decisive objection to any conservatism which deserves to be called such”: “by its very nature it cannot offer an alternative to the direction in which we are moving.”

Sure, “It may succeed by its resistance to current tendencies in slowing down undesirable developments, but, since it does not indicate another direction, it cannot prevent their continuance. It has, for this reason, invariably been the fate of conservatism to be dragged along a path not of its own choosing.”

For Hayek: Ugh. We might say that in Hayek’s view, conservatism lacks a theory. It’s mostly a red light. By contrast: Focused on freedom, spontaneous orders, traditions, and free markets, Hayekian liberalism has a theory. It tells us where to go. It’s a path.

6 Hayek complained that conservatives want to freeze things. They do not want to allow free markets to do their work, or to permit freedom to have its way, or to see what surprises the future, governed by liberal principles, has in store for us.

In particular, Hayek lamented that conservatives “are inclined to use the powers of government to prevent change or to limit its rate to whatever appeals to the more timid mind. In looking forward, they lack the faith in the spontaneous forces of adjustment which makes the liberal accept changes without apprehension, even though he does not know how the necessary adaptations will be brought about. It is, indeed, part of the liberal attitude to assume that, especially in the economic field, the self-regulating forces of the market will somehow bring about the required adjustments to new conditions, although no one can foretell how they will do this in a particular instance.”

Hayek much liked entrepreneurship and innovation. He thought that the future could not be predicted—and that we should be open to and delighted by surprise, so long as it came from exercises of freedom.

Here is where Hayek got tougher on conservatives. In his account, they do not like freedom nearly enough. They are open to the exercise of arbitrary power. They might even welcome it. They are fine with coercion, so long as they are the ones who are behind it.

He objected in particular to “the characteristic complacency of the conservative toward the action of established authority and his prime concern that this authority be not weakened rather than that its power be kept within bounds. This is difficult to reconcile with the preservation of liberty. In general, it can probably be said that the conservative does not object to coercion or arbitrary power so long as it is used for what he regards as the right purposes” (emphasis added).

7 Hayek urged that in this respect, the conservative was “[l]ike the socialist.” (For Hayek, those are exceptionally strong words.) The reason is that “he is less concerned with the problem of how the powers of government should be limited than with that of who wields them; and, like the socialist, he regards himself as entitled to force the value he holds on other people.”

Coercion, then, was one of Hayek’s key concerns. “[T]o the liberal neither moral nor religious ideals are proper objects of coercion, while both conservatives and socialists recognize no such limits. I sometimes feel that the most conspicuous attribute of liberalism that distinguishes it as much from conservatism as from socialism is the view that moral beliefs concerning matters of conduct which do not directly interfere with the protected sphere of other persons do not justify coercion. This may also explain why it seems to be so much easier for the repentant socialist to find a new spiritual home in the conservative fold than in the liberal.”

That is especially interesting (and the last sentence is shrewd). Hayek did not endorse Mill’s Harm Principle, which he thought potentially destructive of liberty. But he came pretty close to that principle, certainly insofar as he wanted to limit coercion.

8 There is a further point here about tolerance—not a great word, but a word of Hayek’s time. A better term might be: mutual forbearance.

Hayek objected: “The typical conservative is indeed usually a man of very strong moral convictions. What I mean is that he has no political principles which enable him to work with people whose moral values differ from his own for a political order in which both can obey their convictions.”

Hayek tended to agree with conservatives on those particular values. He shared many of them. (How many? Good question.) But still: “The acceptance of such principles means that we agree to tolerate much that we dislike. There are many values of the conservative which appeal to me more than those of the socialists; yet for a liberal the importance he personally attaches to specific goals is no sufficient justification for forcing others to serve them.”

Here is Hayek’s central claim: “To live and work successfully with others requires more than faithfulness to one’s concrete aims. It requires an intellectual commitment to a type of order in which, even on issues which to one are fundamental, others are allowed to pursue different ends.”

You can see this as a pragmatic point about what is necessary to enable different people to live together (a modus vivendi). Hayek did make that pragmatic point. But you can also see it as a point about liberty, and for Hayek, that was the more fundamental one.

9 Hayek also offered a vigorous claim about novelty, and the right attitude toward it. Hayek urged “Though the liberal certainly does not regard all change as progress, he does regard the advance of knowledge as one of the chief aims of human effort and expects from it the gradual solution of such problems and difficulties as we can hope to solve. Without preferring the new merely because it is new, the liberal is aware that it is of the essence of human achievement that it produces something new; and he is prepared to come to terms with new knowledge, whether he likes its immediate effects or not.”

Thus "the most objectionable feature of the conservative attitude is its propensity to reject well-substantiated new knowledge because it dislikes some of the consequences which seem to follow from it—or, to put it bluntly, its obscurantism. I will not deny that scientists as much as others are given to fads and fashions and that we have much reason to be cautious in accepting the conclusions that they draw from their latest theories. But the reasons for our reluctance must themselves be rational and must be kept separate from our regret that the new theories upset our cherished beliefs” (emphasis added).

This is an argument against motivated reasoning, and a suggestion that conservatives tend to succumb to it. (Not incidentally, Hayek also had some things to say against nationalism, and the conservative tendency to favor it.)

10 Still: In important respects, and notwithstanding his title, Hayek really was a conservative. He emphasized throughout his life how much we do not know, and how much we benefit from traditions, practices, and institutions that no human being designed. On that count, consider this crucial passage:

”What I have described as the liberal position shares with conservatism a distrust of reason to the extent that the liberal is very much aware that we do not know all the answers and that he is not sure that the answers he has are certainly the right ones or even that we can find all the answers. He also does not disdain to seek assistance from whatever non-rational institutions or habits have proved their worth.”

Hayekian liberals and conservatives are humble; they know how much we do not know. Like the conservative, the Hayekian liberal welcomes institutions and habits that have proved their worth, even if we do not really understand them.

Hayek had some things to say about religion here:

“Unlike the rationalism of the French Revolution, true liberalism has no quarrel with religion, and I can only deplore the militant and essentially illiberal antireligionism which animated so much of nineteenth-century Continental liberalism. That this is not essential to liberalism is clearly shown by its English ancestors, the Old Whigs, who, if anything, were much too closely allied with a particular religious belief. What distinguishes the liberal from the conservative here is that, however profound his own spiritual beliefs, he will never regard himself as entitled to impose them on others and that for him the spiritual and the temporal are different spheres which ought not to be confused.”

Okay then.

11 We can easily see why Hayek has inspired, and continues to inspire, conservatives and conservatism all over the world. But he thought it important to say that he was not a conservative. He really did not like the idea of standing athwart history, yelling stop.

He had an account of liberty, associated with his understanding of markets, traditions, and dispersed knowledge. Sure, he agreed that we need a “brake on the vehicle of progress.” But what he added, with some passion, was this: “I personally cannot be content with simply helping to apply the brake. What the liberal must ask, first of all, is not how fast or how far we should move, but where we should move.”

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Attention Is All You Need: The Transformer Model Architecture

 The core innovation presented in the sources is the Transformer, a model architecture that dispenses with recurrence and convolutions entirely, relying instead solely on attention mechanisms to draw global dependencies between inputs and outputs. This shift represents a fundamental departure from the then-dominant sequence transduction models, which were built on complex recurrent or convolutional neural networks.

Core Innovation: Pure Attention

The Transformer's primary breakthrough is its use of self-attention (or intra-attention) to compute representations of its input and output without using sequence-aligned RNNs or convolution. While previous models often used attention in conjunction with recurrent networks, the Transformer is the first to use it as the exclusive mechanism for modeling dependencies.

Key Takeaways of the Transformer Model

The sources highlight several critical advantages and "takeaways" resulting from this innovation:

• Superior Parallelization and Training Efficiency: Recurrent models are inherently sequential, meaning they must process tokens one by one, which limits parallelization within training examples. Because the Transformer eschews recurrence, it is significantly more parallelizable. This allows for much faster training; for instance, the base model reached state-of-the-art results in just 12 hours on eight GPUs, a small fraction of the cost of previous models.
• Constant Path Length for Long-Range Dependencies: A major challenge in sequence modeling is learning dependencies between distant positions. In recurrent models, the number of operations required to relate distant signals grows linearly with the distance. In the Transformer, this distance is reduced to a constant number of operations, making it easier for the model to learn long-range dependencies.
• Multi-Head Attention: To counteract the potential loss of resolution from averaging attention-weighted positions, the authors introduced Multi-Head Attention. This allows the model to jointly attend to information from different representation subspaces at different positions simultaneously.
• Positional Encoding: Because the model lacks recurrence and convolution, it does not inherently understand the order of the sequence. To address this, the innovators injected "positional encodings"—specifically sinusoidal functions—into the input embeddings to provide information about the relative or absolute position of tokens.
• State-of-the-Art Performance: The innovation proved highly effective, establishing new benchmarks in machine translation, such as a 28.4 BLEU score on English-to-German and 41.8 BLEU on English-to-French tasks.
• Strong Generalization: Beyond translation, the Transformer showed it could generalize well to other tasks, such as English constituency parsing, even when trained on limited data.

The architecture of the Transformer is designed to replace recurrent and convolutional layers with a structure built entirely on attention mechanisms. This shift is the foundation for the model's key takeaways, such as increased training efficiency and the ability to model long-range dependencies in constant time.

The Encoder-Decoder Stack

The Transformer employs the standard encoder-decoder structure common in sequence transduction, but with specific modifications:

• Encoder: Consists of a stack of $N=6$ identical layers. Each layer contains two sub-layers: a multi-head self-attention mechanism and a position-wise fully connected feed-forward network.
• Decoder: Also composed of $N=6$ identical layers. In addition to the encoder's two sub-layers, it includes a third sub-layer that performs multi-head attention over the encoder's output.
• Residual Connections and Normalization: To facilitate deep training, each sub-layer in both the encoder and decoder is surrounded by a residual connection, followed by layer normalization. All sub-layers and embedding layers produce an output dimension of $d_{model} = 512$.

The Attention Mechanism

The heart of the architecture is the attention function, which maps a query and a set of key-value pairs to an output.

• Scaled Dot-Product Attention: This specific version computes attention by taking the dot product of the query with all keys, scaling the result by $1/\sqrt{d_k}$ (to prevent large values from saturating the softmax), and applying a softmax function to weight the values.
• Multi-Head Attention: Instead of a single attention function, the model performs $h=8$ attention layers in parallel. This allows the model to "jointly attend to information from different representation subspaces at different positions," which prevents the "averaging" effect of a single attention head from inhibiting resolution.
• Masked Self-Attention: In the decoder, self-attention is modified to prevent positions from attending to subsequent (future) positions, ensuring the model remains auto-regressive and predicts tokens based only on prior information.

Positional Encoding

A critical detail of the architecture is the use of positional encodings. Because the Transformer lacks recurrence and convolution, it has no inherent sense of the relative or absolute position of tokens in a sequence. To compensate, the authors add sinusoidal functions of different frequencies to the input embeddings, allowing the model to make use of the sequence order.

Position-wise Feed-Forward Networks

Each layer in the stacks contains a feed-forward network (FFN) applied to each position separately and identically. This FFN consists of two linear transformations with a ReLU activation in between, effectively acting as two convolutions with a kernel size of 1.

Architectural Impact on Key Takeaways

These specific details lead directly to the Transformer's most significant advantages:

• Efficiency: By replacing $O(n)$ sequential operations in RNNs with self-attention, the architecture achieves $O(1)$ sequential operations, enabling massive parallelization during training.
• Long-Range Modeling: The architecture ensures the maximum path length between any two positions is constant ($O(1)$), making it easier for the model to learn dependencies between distant words compared to recurrent or convolutional models.
• SOTA Performance: These details allowed the "big" model variant to achieve a 28.4 BLEU score on English-to-German translation, setting a new state of the art at a fraction of the training cost of previous models.

In the context of the Transformer model, attention mechanisms are not just a feature but the foundational architecture that replaces recurrence and convolution entirely. The sources describe this shift as the primary driver behind the model's efficiency and performance.

Types of Attention Mechanisms

The Transformer utilizes two primary variations of attention to process information:

• Scaled Dot-Product Attention: This mechanism maps a query and a set of key-value pairs to an output. It computes the dot product of the query with all keys, scales them by $1/\sqrt{d_k}$ to prevent gradients from becoming too small during training, and applies a softmax function to determine the weights assigned to the values.
• Multi-Head Attention: Instead of one single attention function, the model uses eight parallel "heads". This allows the model to simultaneously attend to information from different representation subspaces at different positions, preventing the "averaging" effect that can occur with single-head attention.

Applications within the Model

The architecture employs these mechanisms in three distinct ways to handle sequence data:

• Encoder Self-Attention: Each position in the encoder can attend to all other positions in the previous encoder layer, allowing for a global understanding of the input.
• Decoder Self-Attention: This uses masking to prevent positions from attending to subsequent (future) tokens, ensuring the model remains auto-regressive during generation.
• Encoder-Decoder Attention: This allows the decoder to attend to all positions in the input sequence, mimicking the behavior of traditional attention in previous sequence-to-sequence models.

Key Takeaways Linked to Attention

The reliance on attention leads to several transformative advantages highlighted in the sources:

• Constant Path Length and Long-Range Dependencies: A critical takeaway is the reduction of the maximum path length between any two positions to a constant number of operations ($O(1)$). In contrast, recurrent and convolutional models require linear or logarithmic operations to relate distant signals, making it harder for them to learn long-range dependencies.
• Computational Efficiency: Because attention-based layers eliminate sequential computation, they are highly parallelizable. This allows the Transformer to achieve state-of-the-art results in significantly less training time—for example, the base model required only 12 hours of training on eight GPUs.
• Interpretability: The sources suggest that self-attention provides a "side benefit" of interpretability. Visualizations show that individual attention heads learn to perform specific tasks, such as anaphora resolution (e.g., linking a pronoun like "its" to its noun) or identifying long-distance syntactic structures like the components of a verb phrase.

The performance results of the Transformer model serve as the ultimate validation for its core innovation—the removal of recurrence in favor of pure attention. These results demonstrate that the architecture is not only more efficient but also superior in quality compared to the then-dominant recurrent and convolutional neural networks.

Machine Translation Benchmarks

The Transformer established new state-of-the-art (SOTA) results on major translation tasks, which is a central takeaway regarding its effectiveness:

• English-to-German (WMT 2014): The "big" Transformer model achieved a 28.4 BLEU score, outperforming the previous best results, including ensemble models, by over 2.0 BLEU.
• English-to-French (WMT 2014): The model reached a 41.8 BLEU score, setting a new single-model SOTA.
• Base Model Performance: Even the Transformer "base" model outperformed nearly all previously published models and ensembles, despite having significantly lower training costs.

Training Efficiency and Cost

A key takeaway from the performance data is the drastic reduction in training time and computational cost (FLOPs) made possible by the model's parallelizable nature.

• The base model reached its peak performance in just 12 hours of training on eight P100 GPUs.
• Compared to previous SOTA models like GNMT or ConvS2S, the Transformer achieved better results at a fraction of the training cost. For instance, the English-to-French big model achieved its SOTA score at less than 1/4 the training cost of the previous leading model.

Generalization to Other Tasks

The sources highlight that the Transformer's performance is not limited to translation, demonstrating strong generalization capabilities.

• When applied to English constituency parsing, a task with strong structural constraints, the Transformer outperformed almost all previous models.
• Notably, the model performed surprisingly well in this area despite a lack of task-specific tuning, indicating that the attention mechanism is a robust and flexible tool for various sequence-based problems.

Impact on Key Takeaways

These performance results confirm that the Transformer's architectural choices—specifically Multi-Head Attention and the constant path length for dependencies—effectively solve the limitations of sequential models. The ability to achieve superior results while being significantly more parallelizable allowed for the training of larger, more powerful models in a fraction of the time previously required.

The training methodology of the Transformer model is characterized by a high degree of parallelization and computational efficiency, which are central to its key takeaways. By moving away from sequential processing, the authors were able to implement a training regime that achieved state-of-the-art results in a fraction of the time required by previous models.

Training Data and Batching

The models were trained on large-scale standard datasets:

• Datasets: For English-German translation, the authors used the WMT 2014 dataset (4.5 million sentence pairs). For English-French, they used a significantly larger set of 36 million sentence pairs.
• Tokenization: Sentences were encoded using byte-pair encoding (37,000 tokens) for English-German and word-piece vocabulary (32,000 tokens) for English-French.
• Batching Strategy: To maximize efficiency, sentence pairs were batched by approximate sequence length, with each batch containing roughly 25,000 source and target tokens.

Hardware and Training Schedule

The Transformer's ability to be trained quickly on standard hardware is one of its most significant advantages:

• Hardware: All models were trained on a single machine equipped with eight NVIDIA P100 GPUs.
• Schedule: The base model was trained for 100,000 steps, taking only 12 hours. The big model was trained for 300,000 steps, which took 3.5 days. This is highlighted as a "small fraction" of the training costs compared to other competitive models.

Optimization and Learning Rate

The training utilized a sophisticated optimization strategy to ensure stability and performance:

• Optimizer: The authors used the Adam optimizer with specific hyperparameters ($\beta_1 = 0.9, \beta_2 = 0.98, \epsilon = 10^{-9}$).
• Learning Rate Schedule: A custom formula was used to vary the learning rate throughout training. This involved a linear warmup for the first 4,000 steps, followed by a decrease proportional to the inverse square root of the step number.

Regularization Techniques

To prevent overfitting and improve the model's ability to generalize, two primary regularization methods were employed:

• Residual Dropout: Dropout (rate $P_{drop} = 0.1$ for the base model) was applied to the output of each sub-layer before addition and normalization, as well as to the sums of embeddings and positional encodings.
• Label Smoothing: The authors used a label smoothing value of $\epsilon_{ls} = 0.1$. While this choice hurt the model's perplexity—as it learned to be "more unsure"—it ultimately improved accuracy and BLEU scores.

Evaluation and Inference

The final performance results were further refined through specific post-training techniques:

• Checkpoint Averaging: For the base model, results were obtained by averaging the last 5 checkpoints; for the big model, the last 20 checkpoints were averaged.
• Beam Search: During inference, the model used a beam search with a beam size of 4 and a length penalty of $\alpha = 0.6$.

These methodological choices support the Transformer's broader takeaway as a highly scalable and efficient architecture. By combining massive parallelization with rigorous regularization and optimization, the researchers demonstrated that "pure attention" could outperform complex recurrent systems while being much faster to train.

The core advantage of the Transformer architecture over Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) is its ability to dispense with recurrence and convolutions entirely, relying instead on a self-attention mechanism to model global dependencies. This shift provides several critical benefits in terms of computational efficiency and the ability to learn long-range relationships.

Overcoming the Sequential Nature of RNNs

The most significant limitation of recurrent models is their inherently sequential nature, where computation is factored along the symbol positions of the input and output sequences.

• Parallelization: Because RNNs generate a sequence of hidden states as a function of the previous state, they preclude parallelization within training examples. The Transformer’s self-attention mechanism reduces the number of sequential operations to constant $O(1)$, allowing for significantly more parallelization and faster training.
• Training Speed: The Transformer base model achieved state-of-the-art results in just 12 hours on eight GPUs, which is a small fraction of the training cost required by previous recurrent models.

Advantages Over Convolutional Architectures

While some convolutional models (like ByteNet and ConvS2S) were designed to reduce sequential computation, they still face challenges that the Transformer avoids.

• Path Length for Dependencies: In convolutional models, the number of operations required to relate signals from two distant positions grows either linearly or logarithmically with the distance between them. In the Transformer, this distance is reduced to a constant number of operations ($O(1)$), making it easier to learn long-range dependencies.
• Global Connectivity: A single convolutional layer with a kernel width smaller than the sequence length cannot connect all pairs of input and output positions; doing so requires stacking many layers. In contrast, a self-attention layer connects all positions in a single operation.

Computational Complexity and Efficiency

The sources provide a direct comparison of complexity and operations per layer (summarized in Table 1):

• Efficiency in Typical Sequences: Self-attention layers are generally faster than recurrent layers when the sequence length ($n$) is smaller than the representation dimensionality ($d$), which is common in machine translation tasks using word-piece or byte-pair representations.
• Separable Convolutions: While certain types of convolutions can be more efficient, the sources note that even a separable convolution's complexity is only roughly equal to the combination of a self-attention layer and a point-wise feed-forward layer.

Key Takeaways on Model Performance

The practical result of these advantages is a model that is both higher in quality and more efficient to train:

• Superior Benchmarks: The Transformer established new benchmarks, such as a 28.4 BLEU score on English-to-German translation, surpassing the previous best results (including ensembles) by over 2 BLEU.
• Generalization: Beyond translation, the model showed it could generalize to tasks like English constituency parsing better than previous RNN sequence-to-sequence models, even with limited training data.
• Interpretability: As a side benefit, self-attention provides better interpretability; visualizations show individual attention heads learning specific tasks related to the syntactic and semantic structure of sentences, such as anaphora resolution.

Artificial Intelligence Markets: Competition Risks and Strategic Developments

 The sources indicate that while artificial intelligence (AI) has the potential to deliver substantial economic and social gains, these benefits are contingent upon maintaining competitive and contestable markets across the entire AI value chain.

Drivers of Economic Impact

• Productivity Growth: AI is widely expected to increase productivity across the global economy, though estimates of this impact vary.
• Declining Costs and Innovation: In the foundation model segment, competition has led to a rapid increase in performance and a sharp decline in prices. For instance, the quality-adjusted price index for text-to-text models fell by nearly 80% between January 2024 and April 2026.
• The AI Economic Frontier: This concept describes the trade-off where users can access increasingly better model performance at lower prices. The number of developers reaching this frontier has grown from two to eight in three years, indicating high pressure on leading firms to innovate.

Barriers to Realizing Economic Gains

The sources highlight that realizing these gains assumes broad access to affordable AI tools, which is threatened by several structural risks:

• Market Concentration: Key inputs like specialized chips (NVIDIA holds 90% of the GPU market) and cloud infrastructure (three providers control 74% of the global market) are highly concentrated.
• Rising Effective Costs: While price-per-token is falling, the "effective cost" of using AI may rise as AI agents consume significantly more tokens to execute complex tasks.
• Systemic Adoption Challenges: Broad productivity gains require "systemic adoption"—deep integration into business processes—which necessitates slow-moving organizational changes and complementary investments in data and skills.

Competition and Structural Risks

The economic impact is closely tied to whether markets remain contestable over time. Several factors threaten this:

• Vertical Integration: Large tech firms often operate across multiple layers of the value chain (hardware, cloud, and applications), allowing them to bundle products, favor their own offerings, and raise switching costs for users.
• First-Mover Advantages: Early leaders benefit from data feedback loops and self-improving AI capabilities, which can create "market tipping" where a few dominant players become entrenched.
• Input Bottlenecks: Barriers to entry are high due to the concentration of critical inputs like compute, proprietary datasets, and specialized skills.

Macroeconomic and Global Asymmetries

The sources warn that AI may widen the gap between leading and lagging economies:

• Investment Concentration: AI investment is heavily concentrated in the United States and China. In 2025, total estimated AI investments in the U.S. were more than double those of all other OECD countries combined.
• Economic Dependence: Countries without domestic AI ecosystems may reap some productivity benefits but will likely capture only a limited share of market rents. Furthermore, they may become increasingly reliant on imported AI services, exposing them to service disruptions and the pricing power of foreign oligopolies.

The sources present a "mixed picture" of AI markets, where intense market dynamism in specific segments—particularly foundation models—coexists with deep-seated structural risks that threaten long-term competition.

Evidence of Market Dynamism

Despite high entry costs and the dominance of a few tech giants, the AI model development segment has shown significant signs of competitive pressure and innovation over the past three years:

• Rapid Entry and Model Proliferation: The number of developers focusing on language models for cognitive tasks grew from 9 in January 2024 to 47 in April 2026. During this same period, the number of active text-to-text models surged from 22 to 453.
• Declining Quality-Adjusted Prices: The aggregate OECD price index for text-to-text models fell by nearly 80% between January 2024 and April 2026. This decline is observed across all segments, including coding and agentic capabilities.
• Contested Leadership: Technological leadership is frequently challenged by "dynamic, specialised providers" who compete directly with tech giants.

The AI Economic Frontier

A key indicator of market dynamism is the AI Economic Frontier, which tracks the best available model performance for a given price.

• Frontier Movement: The frontier has moved steadily, offering better models at lower prices.
• Increasing Participants: The number of developers reaching this frontier increased from two to eight over three years.
• Transience of Leadership: Presence at the "technological frontier"—defined by model quality and price—often lasts only a few months, indicating that even leading firms face continuous pressure from newcomers.

Dynamism vs. Structural Risks

The sources emphasize that this dynamism is currently concentrated in the model development layer and may be fragile due to issues in other parts of the AI value chain:

• Input Bottlenecks: Upstream segments like high-end chips (where one firm holds 90% market share) and cloud infrastructure (where three providers control 74% of the market) remain structurally concentrated.
• Vertical Integration: Large incumbents often operate across multiple layers (model development, cloud, and downstream applications), allowing them to use preferential partnerships and product bundling to entrench their positions.
• Data Feedback Loops: First-movers benefit from self-improving AI capabilities and exclusive data access, which can create "market tipping" where a few players become dominant despite initial dynamism.

The Role of Open Source

Open-source development is cited as a significant factor in mitigating concentration by lowering entry costs and putting price pressure on incumbents. However, the sources warn that open-source ecosystems can also be used by large firms to create exclusive bundles that reinforce their own first-mover advantages.

Policy Implications for Maintaining Dynamism

To ensure these dynamic outcomes last, the sources suggest that policymakers must remain vigilant. They recommend promoting market contestability by facilitating access to critical inputs (data, compute, and skills) and scrutinizing vertical strategies that could lock in users or foreclose rivals. Coordination across borders is highlighted as essential to handle the global nature of these fast-evolving markets.

While the AI model development segment currently shows signs of dynamism, the sources emphasize that broader structural risks to competition are "real, persistent and expected to intensify" across the entire AI value chain. These risks threaten to entrench incumbent positions and limit the long-term contestability of the market.

Concentration of Critical Inputs

A primary structural risk is the high concentration of the essential inputs required to develop and deploy AI:

• Hardware: The market for specialized chips is extremely concentrated; for example, NVIDIA accounted for 90% of the GPU market in 2023. High fixed costs and long development times create significant barriers to entry for new designers.
• Cloud Infrastructure: Large-scale AI requires massive compute power, which is dominated by a few providers. In 2023, the three major cloud providers controlled 74% of the global market.
• Data: Incumbents with large user bases benefit from exclusive data partnerships and proprietary datasets, which can lead to data concentration and gatekeeping.
• Specialized Skills: The scarcity of talent needed to build frontier models further restricts the ability of new entrants to compete effectively.

First-Mover Advantages and Feedback Loops

The sources highlight that early leaders benefit from self-reinforcing cycles that can lead to "market tipping," where one or two firms dominate a segment:

• Data Feedback Loops: More users generate more data, which is used to improve models, attracting even more users.
• Cumulative Learning: AI models can exhibit self-improving capabilities, allowing first movers to maintain durable leadership through continuous refinement.
• Reputational Advantages: Early success in the market creates brand loyalty and trust that newcomers find difficult to overcome.

Vertical Integration and Ecosystem Foreclosure

Many leading digital firms operate across multiple layers of the value chain—from chips and cloud to models and downstream applications. This vertical integration presents several competition risks:

• Preferential Access: Vertically integrated firms may provide their own downstream services with better or cheaper access to upstream compute and models.
• Product Bundling: Firms can leverage their dominance in one area (like cloud services or operating systems) to bundle AI tools, effectively raising switching costs and locking in users.
• Strategic Partnerships: Large incumbents often form exclusive or preferential partnerships with leading AI labs, potentially foreclosing rivals' access to the most capable "frontier" models.

Barriers to Contestability

The structure of AI markets creates high barriers that prevent new firms from challenging incumbents:

• High Sunk Costs: The massive investment required for R&D and compute infrastructure—with the five largest U.S. tech firms forecast to spend $660 billion in capital expenditure in 2026—makes entry prohibitively expensive for most.
• User Lock-in: Integration of AI into core business processes and IT systems creates high operational and cybersecurity risks if a user tries to switch providers.
• Regulatory Influence: Dominant, vertically integrated firms may use their market power to shape regulations in ways that favor their business models and disadvantage smaller competitors.

Cross-Country Asymmetries

Finally, these structural risks have a geopolitical dimension. AI investment and capacity are heavily concentrated in a few countries, primarily the United States and China. Economies with weaker domestic AI ecosystems risk becoming increasingly reliant on imported services, exposing them to the pricing power and potential service disruptions of foreign oligopolies.

The sources emphasize that while some segments of AI development show dynamism, the broader AI value chain is subject to structural risks that are "real, persistent and expected to intensify". These risks are categorized across various layers, from upstream infrastructure to downstream applications.

Upstream Risks: Input Bottlenecks and Concentration

The primary risks in the upstream segments involve the extreme concentration of the physical and digital resources required to build AI:

• Hardware (Chips): This segment is characterized by high fixed costs, long development times, and a "dependency on a small number of suppliers". For example, NVIDIA accounted for 90% of the GPU market in 2023. The risk here is the foreclosure of access or preferential access for large incumbents.
• Cloud Infrastructure: Dominated by a few providers (three firms controlled 74% of the global market in 2023), this layer faces risks of "entrenchment" and the "leveraging of cloud market power into adjacent AI segments" through product bundling and vertical foreclosure.
• Data: Incumbents benefit from exclusive data partnerships and "data feedback loops," where existing large user bases provide a continuous stream of data to improve models. This creates gatekeeping effects that reduce the ability of new entrants to train competitive models.

Midstream Risks: Foundation Model Oligopolies

The foundation model layer, which serves as the backbone for generative AI, faces "oligopolistic market structures".

• Durable Leadership: Despite recent price declines, leadership may become entrenched through cumulative learning and self-improving AI capabilities.
• Selective Access: There is a risk of "preferential or selective access to frontier models," where providers favor their own downstream services or those of strategic partners.

Downstream Risks: Market Tipping and Lock-in

As AI moves into consumer and business services, the risks shift toward user control and market dominance:

• User Lock-in: Integration into existing digital ecosystems and high switching costs can prevent users from moving to better or cheaper alternatives.
• Market Tipping: Risks include "market tipping in key use cases" where a single provider becomes the default due to network effects and exclusive distribution.

Integrative Risks Across the Chain

Two overarching risks threaten the contestability of the entire value chain:

• Vertical Integration: Leading digital firms often operate across multiple layers (e.g., owning the cloud infrastructure, developing the model, and providing the downstream app). This allows them to "favour their own offerings" and bundle AI with existing products, effectively raising barriers for specialized rivals.
• First-Mover Advantages: Concentration is compounded by positive feedback loops where early leaders gain reputational advantages and superior data, making it difficult for newcomers to compete even if they have innovative technology.

Geopolitical and Macroeconomic Dimensions

The sources also highlight that these value chain risks have a global impact. AI investment and capacity are heavily concentrated in the United States and China. Countries with weaker domestic ecosystems may become increasingly reliant on imported AI services, exposing them to the pricing power, service disruptions, and regulatory conflicts of foreign oligopolies.

The sources indicate that while AI has the potential for significant economic growth, several barriers to adoption prevent users and firms from fully realizing these benefits. These barriers are closely linked to market concentration and the strategic behavior of dominant firms.

Economic and Financial Barriers

• Rising Effective Costs: While the price per million tokens has declined by nearly 80% since 2024, the "effective cost" of using AI may still rise. This is because AI agents—which execute complex, multi-step tasks—consume tokens at a much higher intensity than simple chatbots.
• Need for Complementary Investments: Broad productivity gains require "systemic adoption," which means deep integration into core business processes rather than occasional use. This necessitates slow-moving organizational changes and significant investments in intangibles like data and specialized skills.
• High Entry and Sunk Costs: For smaller firms and SMEs, the high investment barriers for R&D and compute infrastructure can be prohibitively expensive.

Technical and Operational Barriers

• Cybersecurity and Safety Risks: As AI agents become more deeply integrated into IT systems, concerns over safety and operational reliability are becoming critical barriers.
• Interoperability Issues: A lack of system interoperability can prevent firms from adopting diverse AI solutions, forcing them to rely on a single provider.
• Energy and Supply Chain Disruptions: Recent energy shocks and supply-chain issues can reinforce concentration, as only vertically integrated giants may have the infrastructure to cope with rising costs and tightening financing conditions.

Competition-Related Barriers

• Switching Costs and User Lock-in: High operational risks and the deep integration of AI into business processes make it difficult for users to switch providers. Incumbents often leverage this by bundling AI tools with existing digital ecosystems or cybersecurity solutions, effectively locking in users.
• Preferential Access: Large incumbents may use exclusive partnerships or vertical integration to provide themselves with better or cheaper access to "frontier" models, making it harder for rivals to offer competitive downstream services.

Geographic and Cultural Barriers

• Language Disparities: AI adoption is hampered in many countries because performance in non-English languages remains significantly lower.
• Cross-Country Asymmetries: Countries with weak domestic AI ecosystems face a "productivity divide," becoming increasingly reliant on imported AI services from a few global giants in the U.S. and China. This dependence exposes them to potential service disruptions and the pricing power of foreign oligopolies.

To address these barriers, the sources suggest that policymakers should focus on facilitating access to critical inputs (data, compute, and skills) and promoting market transparency to ensure the market remains contestable for new entrants.

The source provides a clear roadmap for policymakers, emphasizing that they must adopt a balanced, proactive, and forward-looking approach to monitoring and promoting competition in AI markets. Because AI has the potential for significant economic gains, policy efforts should focus on ensuring the market remains open and contestable across the entire value chain.

Direct Actions to Enhance Contestability

Policymakers are encouraged to foster competition and innovation simultaneously through several specific measures:

• Facilitating Access to Inputs: Policies should aim to improve access to critical AI inputs, specifically data, compute capacity, and specialized skills.
• Supporting SMEs: Efforts should be made to remove investment barriers that prevent small and medium-sized enterprises (SMEs) from entering or competing in the market.
• Improving Transparency and Experimentation: Increasing market transparency and utilizing regulatory sandboxes can encourage experimentation without stifling innovation.
• Promoting Interoperability: Ensuring system interoperability is vital to allow users to switch between different AI solutions at a low cost, preventing provider lock-in.

Scrutiny of Market Strategies

The source highlights the need for continuous vigilance regarding the strategic behavior of dominant firms:

• Real-Time Monitoring: Authorities must monitor fast-evolving market developments in real time to identify emerging risks.
• Scrutinizing Vertical Integration: Policymakers should closely examine vertical strategies, such as acquisitions and partnerships, that may allow firms to favor their own products or foreclose rivals from accessing "frontier" models.
• Addressing Regulatory Influence: There is a need to protect against undue influence or lobbying by dominant, vertically integrated firms that might try to shape regulations to their advantage.

International and Domestic Coordination

Given that AI markets are global and involve complex infrastructure, coordination is essential:

• International Cooperation: Knowledge sharing and cooperation among international competition authorities can help build expertise and ensure a coordinated, timely response to market shifts.
• Domestic Regulatory Alignment: Competition authorities should coordinate with other domestic bodies, such as energy and digital regulators. This ensures that bottlenecks in one part of the value chain—such as access to electricity or specialized chips—do not undermine competition in other segments.

The Challenge of Timing

The source warns that policymakers face a critical timing dilemma: while premature action might inadvertently hamper innovation, late action risks allowing market concentration to become entrenched, which would make remedial steps significantly harder to implement in the future. Consequently, sustained vigilance and a proactive stance are required to secure long-term competitive outcomes.

English-Medium Instruction in Morocco's Higher Education: Center-Periphery Disparities

 In Morocco’s higher education system, the shift toward English-medium instruction (EMI) is framed by the sources as a complex negotiation between colonial legacies, national identity, and the pressures of globalization. The primary policy driver for this transition is the National Plan for Accelerating the Transformation of Higher Education, Scientific Research and Innovation System (PACTE ESRI 2030), which positions English as a vital tool for internationalization, scientific research, and aligning university programs with labor-market needs. This policy shift primarily targets STEM programs, though it is gradually expanding into the humanities.

The broader context of this policy includes the following key dynamics:

• Layered Linguistic Hegemony: For decades, French has maintained a hegemonic role in Moroccan academia and professional life, governing access to knowledge and social mobility. The sources describe the current transition not as a clean replacement of French by English, but as a superimposition of hierarchies where English functions as a new layer of "global capital" added onto an entrenched French-dominant prestige order.
• Center-Periphery Disparities: A significant contextual challenge is the "center-periphery" rift between flagship universities and annexed or peripheral faculties. While institutions in major cities like Fez or Rabat may have more resources and language centers, peripheral sites like the Polydisciplinary Faculty of Taza struggle with basic support, limited exposure to English, and fewer subject-specific materials. This geographic inequality suggests that the benefits of EMI may be restricted to those in urban centers.
• Socio-Economic Inequity: EMI policy risks exacerbating existing social divides. Students from private or urban schools often enter higher education with higher linguistic capital in both French and English, whereas students from public or rural backgrounds may find the transition to EMI to be an additional form of exclusion. Without equitable investment, English may simply reproduce the elitism historically associated with the French language.
• Identity and National Languages: The expansion of English occurs within a multilingual landscape where Modern Standard Arabic and Tamazight serve as symbols of national identity. However, these local languages often carry limited academic reward and are marginalized in the production of advanced scientific knowledge. Participants in the sources expressed concerns that EMI could further marginalize these national languages if not implemented as part of an inclusive policy.
• Institutional Path-Dependence: The transition faces resistance from a "clash of habitus" between generations. Senior faculty members anchored in Francophone networks often view French as central to their academic identity and professional status, while younger researchers and doctoral students see English as the "language of the future" and a necessary resource for international mobility.

Ultimately, the sources argue that for EMI policy to achieve social and academic justice, it must move beyond top-down directives to include incremental reforms, such as faculty pedagogical preparation, student bridging supports, and a redistribution of resources to ensure parity between central and peripheral institutions.

The sources define the center-periphery disparity in Morocco's higher education through a comparative study of two specific sites: Mohammed Ibn Abdellah University in Fez (the flagship "center") and its annexed Polydisciplinary Faculty of Taza (the "periphery"). Within the context of English-Medium Instruction (EMI), this geographic divide creates a "rift" that determines how effectively students and faculty can convert English proficiency into academic and professional success.

The following key points detail these disparities as presented in the sources:

1. Uneven Distribution of Institutional Resources

The most immediate disparity lies in the "material support" available at each site.

• Flagship institutions (Fez/Rabat): These centers typically house dedicated language centers, offer more robust English programs, and benefit from more frequent visits by international academics.
• Peripheral faculties (Taza): Participants from Taza reported struggling with "basic support," noting a lack of subject-specific materials in English and fewer opportunities for formal English instruction. This lack of a language center makes students in peripheral areas feel the system is "against" them.

2. Forced Academic Mobility

The resource gap creates a "rationale for mobility," where students feel they cannot achieve their potential in the periphery. Doctoral researchers noted that they often complete undergraduate studies in peripheral provinces like Taza but feel compelled to move to urban centers like Fez or Rabat because those locations offer the "opportunities and horizons" necessary for advanced research and international engagement.

3. The Role of Micro-Level Support

Interestingly, the sources highlight that the center-periphery divide is sometimes mediated by individual agency.

• In the periphery (Taza), some students reported "excellent support" from supervisors who personally annotate drafts in English and provide informal tutoring to bridge the gap.
• Conversely, in the center (Fez), some students found themselves restricted by "laboratory culture" where senior supervisors—anchored in Francophone networks—insisted on publishing in French, effectively blocking the transition to English despite the institution’s central status.

4. Theoretical Implications: Reproduction of Inequality

Using Bourdieu’s sociology, the sources argue that EMI may inadvertently reproduce historical class and geographic inequalities.

• Linguistic Capital: English functions as a new form of "global capital" that is unevenly distributed.
• Systemic Exclusion: Because students in peripheral regions often come from public or rural school backgrounds with less early exposure to English, the shift to EMI—without significant investment in peripheral infrastructure—acts as an "additional form of exclusion".

5. Policy Recommendations for Parity

The sources conclude that for the PACTE ESRI 2030 reforms to be equitable, the government must move beyond top-down directives and focus on resource equalization. This includes:

• Establishing bridging supports and language centers specifically in peripheral faculties.
• Ensuring parity of resources so that the "institutional label" of an annexed faculty does not disadvantage its members in the global academic market.
• Investing in pedagogical preparation for faculty in remote areas to ensure they have the confidence to deliver disciplinary knowledge in English.

The sources highlight a diverse and often conflicting set of stakeholder perspectives regarding the expansion of English-Medium Instruction (EMI) in Morocco, specifically focusing on university lecturers and doctoral students from both flagship (center) and peripheral institutions,,. These perspectives are shaped by individual career stages, geographic locations, and the historical dominance of the French language,,.

1. Perceptions of English as Global Opportunity

There is a broad consensus among both lecturers and students that English represents a "gateway" to the global academic community.

• Scientific Advancement: Stakeholders associate English proficiency with the ability to access recent literature, participate in international conferences, and secure publication in high-impact journals.
• Employability and Mobility: For many, English is viewed as a practical necessity for career development and international research collaboration,. Doctoral students, in particular, frame English as a resource that allows them to bypass the "past historical constraints" and colonial legacies associated with French,.

2. The Generational Gap and "Clash of Habitus"

The sources identify a significant divide in attitudes between senior faculty and younger researchers, described as a "contest over legitimate linguistic capital".

• Senior Faculty Resistance: Many older professors, whose professional identities and networks are deeply anchored in Francophone circles, express resistance to a full transition to English,. They view French not just as a tool, but as a core part of their "academic identity" and fear that shifting to English feels like "erasing" that history,.
• The "Language of the Future": Conversely, younger scholars and doctoral students overwhelmingly see English as the "language of the future". This generation is more likely to view the persistence of French as an obstacle to global competition,.

3. Pedagogical Concerns and Instructor Confidence

Despite the enthusiasm for English as a resource, lecturers express significant anxiety regarding their ability to teach complex disciplinary knowledge in a foreign language,.

• Lowered Confidence: Some lecturers reported that teaching in English "lowers one's confidence" and makes it difficult to provide their "best to students",.
• Classroom Confusion: Students observed that when professors are not fully proficient, they often resort to code-switching between English and French, which can "slow the course" and lead to confusion during assessments.

4. Perspectives from the Periphery

Stakeholders at peripheral sites, such as the Polydisciplinary Faculty of Taza, offer a more critical perspective on the equity of the EMI transition.

• Institutional Labeling: Faculty and students in remote areas feel the system is "against" them because they lack the language centers and visiting international academics found in urban centers like Fez or Rabat,.
• Forced Mobility: Doctoral researchers in these areas often feel they must move to larger cities to find the "horizons" and resources necessary to leverage English proficiency into actual academic opportunity.

5. Student Agency and Adaptation Strategies

In the absence of formal institutional support, students have developed their own strategies to navigate the linguistic shift,.

• Peer Networks: Stakeholders reported creating WhatsApp groups and informal "writing circles" to share links, glossaries, and tutorials.
• Unofficial Translation: Students often rely on peers who are more proficient in English to help translate abstracts before they are submitted to international journals,.

6. Identity and National Languages

Finally, many stakeholders express concern that the focus on global languages (French and English) further marginalizes Arabic and Tamazight in the production of scientific knowledge,. They argue that an inclusive policy should ensure that English does not simply "reproduce the elitism" of the French era, but instead safeguards local cultural identity and social justice,.

In the context of Morocco’s higher education, the implementation of English-Medium Instruction (EMI) faces multifaceted challenges that stem from institutional resource gaps, pedagogical limitations, and deeply ingrained linguistic hierarchies,. The source identifies these challenges as critical barriers that could determine whether the PACTE ESRI 2030 reforms lead to genuine internationalization or merely reproduce existing social inequalities,.

The following are the key implementation challenges highlighted in the source:

1. Pedagogical Preparedness and Instructor Confidence

One of the most significant hurdles is the lack of specific training for faculty to deliver complex disciplinary knowledge in English,.

• Reduced Confidence: Lecturers expressed that teaching in English "lowers one’s confidence" and prevents them from giving their "best to students",.
• Classroom Friction: To compensate for linguistic gaps, professors often resort to code-switching between English and French. This can confuse students, slow down the pace of courses, and complicate the clarity of assessments.
• Need for Scaffolding: There is a pronounced need for discipline-specific glossaries, targeted reading materials, and professional development focused on EMI-specific pedagogies,,.

2. Institutional and Geographic Disparities

The "center-periphery" rift creates a landscape of unequal access to necessary resources,.

• Resource Depletion in the Periphery: Peripheral faculties, such as the site in Taza, report a lack of language centers, few English classes, and limited exposure to visiting international academics compared to flagship universities in cities like Fez or Rabat,.
• Institutional "Labeling": Participants from peripheral areas feel the system is "against" them, as they lack the material support required to convert English proficiency into tangible academic gains,.

3. Curricular and Structural Alignment

The transition to EMI requires more than a simple language shift; it necessitates a complete overhaul of institutional routines,.

• Assessment Misalignment: Currently, there is a risk that students are penalized for their English proficiency rather than their content knowledge. The source suggests that assessments must be redesigned to test only disciplinary mastery.
• Phased Implementation: Experts argue that EMI should be "incremental and phased," starting with building teacher capacity and aligning administrative routines (such as thesis templates and reviewer networks) before full scaling,.

4. Generational Resistance and the "Clash of Habitus"

Implementation is hindered by a contest over "legitimate linguistic capital" between different generations of academics,.

• Senior Faculty Anchored in French: Many older professors view French as core to their academic identity and professional networks,. They may resist a total shift to English, which they perceive as "erasing" their history,.
• Institutional Path-Dependence: Because bureaucratic procedures and supervisory practices remain deeply rooted in French, a clean "handover" to English is difficult,.

5. Risks of Social and Linguistic Injustice

Without equitable investment, EMI implementation risks exacerbating the elitism historically associated with the French language,.

• Reproducing Inequality: Students from private or urban schools often enter university with higher linguistic capital. In contrast, those from public or rural backgrounds face an "additional form of exclusion" if they are forced into EMI without bridging supports or writing workshops,,.
• Marginalization of National Languages: There is concern that the focus on English will further sideline Arabic and Tamazight in the production of scientific knowledge, making the policy feel less inclusive regarding national identity,.

The source concludes that addressing these challenges requires moving beyond "top-down directives" toward collaborative reform that includes resource equalization and structured support for both faculty and students,,.

The key takeaways from the sources regarding English-Medium Instruction (EMI) in Morocco emphasize that while English is highly valued as a resource for global integration, its implementation faces significant structural and social hurdles,. The core findings center on the following points:

1. English as a Gateway to Global Capital

The primary takeaway is that stakeholders—particularly doctoral students—view English as essential linguistic capital for international research, publication, and career mobility,. It is framed as the de facto language of modern science, enabling researchers to bypass historical constraints associated with the French colonial legacy,.

2. The Persistence of the Center-Periphery Rift

A critical finding is that geographic location determines the effectiveness of the EMI transition,.

• Flagship institutions (the "center") enjoy better access to language centers, visiting academics, and English programs.
• Peripheral, annexed faculties struggle with a lack of basic support and material resources, creating a "rationale for mobility" where students feel they must move to urban centers to succeed,.

3. Superimposed Linguistic Hierarchies

The sources argue that English is not simply replacing French; instead, it is being layered onto an entrenched French-dominant prestige order,. French remains deeply encoded in bureaucratic procedures, thesis templates, and the professional identity of senior faculty, leading to a "clash of habitus" between generations of academics,,.

4. Pedagogical and Structural Gaps

A significant takeaway regarding implementation is the lack of faculty preparedness,. Disciplinary mastery does not equate to EMI pedagogical proficiency, and many lecturers report lowered confidence when teaching in English,. This often results in classroom code-switching that can confuse students and complicate assessments.

5. Student Agency and Inequality

In the absence of formal institutional scaffolding, students have developed autonomous adaptation strategies, such as informal peer-led WhatsApp groups and writing circles,. However, these strategies highlight existing inequities, as students from private or urban backgrounds enter with higher linguistic capital, while those from public or rural backgrounds face an additional form of exclusion,,.

6. Priorities for Equitable Reform

The sources conclude that for EMI to achieve social and academic justice, the government must move beyond top-down mandates toward phased, incremental reform,. Key recommendations include:

• Resource Equalization: Investing specifically in peripheral faculties to ensure parity,.
• Faculty Support: Providing professional development in EMI pedagogies and reducing teaching loads for those adapting to the new medium.
• Student Scaffolding: Implementing bridging courses and academic writing workshops,.
• Safeguarding Identity: Ensuring that the focus on global languages does not further marginalize Arabic and Tamazight in the production of local knowledge,.

Newspaper Summary 240726

 ESCALATION IN W. ASIA

Oil tops $100 after Houthi attack on 2 Saudi tankers

New York: Crude oil climbed above $100 a barrel on Thursday, reaching their highest level in nearly two months, after Yemen’s Houthis said they attacked two Saudi oil tankers in the Red Sea, adding to concerns over global supply disruptions from a near-halt in trade through the Strait of Hormuz.

Brent futures rose $6.64, or 7 per cent, to $100.71 a barrel at 10.52 am ET (1452 GMT), exceeding $100 for the first time since late May. The global crude oil benchmark has risen nearly 40 per cent this month and was in technically overbought territory for a ninth day in a row, the first time since September 2023.

US West Texas Intermediate crude rose $5.18, or 6 per cent, to $92.01, trading above $90 for the first time since June 11. Both contracts were up for the fifth straight day.

”The (Saudi tankers) attack has sent world crude oil prices higher and into a higher gear as the ramifications of yet another chokepoint for crude oil trade originating from the Middle East is constricting trade,” said Tim Snyder, chief economist at Matador Economics.

SAUDI TANKERS HIT

Yemen’s Houthis have opened a new front in the Iran war by targeting vessels carrying Saudi oil in the Bab el-Mandeb Strait after stating they would impose a naval blockade on shipments from Saudi Arabia.

Houthi militia attacked two Saudi Arabian oil tankers in a military operation, the group said on Thursday, with a Saudi news agency later confirming that one of the two vessels was ablaze after an assault while sailing in the Red Sea.

Goldman Sachs said Brent might exceed $120 a barrel in the fourth quarter and average $100 next year if the Strait of Hormuz remains disrupted through 2027, with further upside if the Bab el-Mandeb Strait and Suez Canal also suffer persistent disruption.

Iran’s Revolutionary Guards said an oil tanker caught fire after an explosion while attempting to follow a mined route in the southern area of the Strait of Hormuz near the coast of Oman and that two others had turned back. The Guards said the strait was under their control and “completely closed” while US actions continued in the region, warning that no tanker would be allowed to enter or leave without coordination with Iran.

Iranian strikes on vessels crossing the Strait of Hormuz have resulted in a drop in non-Iranian oil tankers traversing the waterway. At the same time, the reintroduction of a US naval blockade targeting Iranian ports has likely resulted in Iranian oil loadings falling to zero from 1.5 million to 2 million barrels per day at the start of the month, Giovanni Staunovo, a UBS analyst, said.

As a result of fewer shipments exiting the strait, loading activity within the Gulf has fallen to 2.5 million bpd over the past seven days, compared with 6 million bpd over the past 30 days, he added.

Reuters

China clings to austerity despite sharper economic dip

China’s public spending plunged last month by the most since October, suggesting the government tightened fiscal policy further despite increasing calls for more support as economic growth slips.

A broad measure of expenditure tumbled 11.9 per cent in June from a year earlier, according to Bloomberg calculations based on Ministry of Finance data released Wednesday. By contrast, broad fiscal revenue gained 1.8 per cent.

That took the broad deficit in the first half to 4.57 trillion yuan ($675 billion), 13 per cent less than a year earlier. Goldman Sachs Group Inc. estimates the “negative fiscal impulse” in April-June contributed more than 40 per cent of the sequential decline in real economic growth from the first to second quarter.

China’s fiscal pullback has put a drag on overall investment, with economic expansion weakening more than expected in the second quarter. A shift toward looser policy is still likely as top officials call for faster deployment of pro-growth measures that have already been approved to ensure the economy expands enough to meet Beijing’s annual target of 4.5-5 per cent.

The slowdown in fiscal expenditure “appears to be an intentional fine-tuning of the spending pace after strong first-quarter growth,” Standard Chartered Plc. economists including Ding Shuang wrote in a Wednesday note. “The government retains sizeable fiscal headroom within the budget framework approved in March,” they said.

The Ministry of Finance said it will continue to carry out what it called a “more active” fiscal policy, according to a statement made in a video clip released as part of its quarterly briefing.

Among other steps, it said officials will “push for all existing policies to be rolled out on the ground and look to “reasonably accelerate the pace of spending,” along with stronger expenditure aimed at improving people’s wellbeing.

The ministry also pledged continued backing for “the expansion of effective investment.” That phrase hints at the government’s continued focus on quality projects to avoid wasteful investment — an approach that’s presented a hurdle for faster spending.

Infrastructure spending under the general public budget, the largest among the government’s four books, fell almost 9 per cent in April-June from a year earlier, according to Bloomberg calculations based on the ministry’s numbers. Meanwhile, the government’s combined expenditure on education, healthcare as well as social security and employment rose 4.9 per cent in the period.

Bloomberg News

No discounts on Russian oil for Sept deliveries: BPCL

Rishi Ranjan Kala New Delhi

State-run Bharat Petroleum Corporation (BPCL) said on Thursday that traders have stopped offering discounts on Russian crude oil for delivery in September 2026. The development comes as Houthi rebels have threatened to block the Bab al-Mandab Strait and have attacked vessels carrying Saudi crude, pushing Brent prices past $100 per barrel on Thursday.

However, BPCL has already contracted crude oil supplies for July and August 2026 and has booked a couple of cargoes for delivery in September. In a post-results analyst call, VRK Gupta, Director (Finance) at BPCL, stated, “Till August we have completed the deals, including Russian Urals and ESPO. September offers are coming. We have to wait, maybe next one week, we will come to know what will be the discount scenario”. He noted that while markets saw a brief period of stability in June, recent developments have changed the outlook.

RED SEA RISKS PERSIST

“Based on recent issues in the Red Sea route, there may be certain issues in terms of a couple of cargoes, but otherwise we have sufficient crude oil till August 31, 2026. Maintaining 30 days of crude also. September window has opened. A couple of cargoes we have booked. Maybe in the next 7-10 days, we will complete the deals for September. No visibility beyond September,” Gupta said.

Regarding the navigation of the West Asia conflict during Q1 FY27, Gupta explained that disruptions in tied-up term crude volumes led BPCL to proactively optimise its crude sourcing by increasing spot crude purchases. The spot percentage rose to almost 69 per cent in Q1 FY27, up from 44 per cent in the same quarter the previous year.

“We diversified our crude sourcing outside of the Hormuz, exploring multiple geographies, including increasing..."

The ‘meme-first’ approach of Gen Z activism

The ongoing youth movement led by the Cockroach Janta Party’s ‘meme-first’ approach, leveraging visual platforms such as Instagram, is playing a key role in gaining traction among Gen Z. Followers of the movement have been sharing their experiences and opinions often through reels and short videos posted especially on platforms such as Instagram. After posting its first Instagram post on May 17, CJP garnered nearly 3 million followers within the first 78 hours.

As of Thursday, its follower account had swelled to as much as 25.6 million followers on the platform, garnering strong engagement and views. Founded by Abhijeet Dipke as a satirical joke, post the remarks made by the Chief Justice of India, CJP’s Instagram account has more followers than BJP and the Congress on the platform.

KEY ROLE

Observers and experts believe this radical, different approach, compared with traditional political campaigns, is playing a key role in the movement, resonating with Gen Z. It perhaps also indicates that as Gen Z comes of age, political discourse is expected to play out even more on visual storytelling-led platforms such as Instagram.

“The current movement owes its origins to digital natives who have played off the CJI’s remark to create an identity for themselves through the Cockroach Janta Party. This is radically different from traditional political campaigns or even the IAC movement in 2011. Using the cockroach meme, they have been able to reach out to young audiences who saw the irony and self-deprecating humour and joined the bandwagon. A bit similar to what was seen in the US with the take-off of the term ‘deplorables’ in 2016 US Presidential Election,” said business strategist Lloyd Mathias.

“As digital natives, they have mastered the ‘meme-first’ politics with their followers sharing visuals, banners, memes, reels and comments laced with humour and satire, which resonate well on platforms like Instagram. This is very unlike traditional political parties which are all about staid speeches, slogans and amplifying leaders’ voices,” he added.

Mathias pointed out that it is an “internet-first movement”. “Activism has usually been seen as a serious business. Gen Z is resonating with the irony, sarcasm and visual storytelling approach. This is more of an internet-first movement,” he noted.

Meenakshi Verma Ambwani New Delhi

UP IN ARMS. CJP activists continued their protest, seeking the resignation of Union Education Minister Dharmendra Pradhan at Jantar Mantar in New Delhi on Thursday.

Egg prices crack a new record as heat, feed costs bite

Egg prices have climbed to an all-time high in Namakkal, India’s largest egg-producing hub, as a prolonged summer, lower production and soaring feed costs tighten supplies. This comes amid robust demand.

Industry stakeholders say the supply-demand mismatch could keep prices elevated in the coming weeks, while warning that shortages of key poultry feed ingredients, such as soybean meal and maize, pose a longer-term challenge.

The National Egg Co-ordination Committee (NECC) revised the farmgate procurement price to a record ₹6.65 per egg on July 13, surpassing the previous high of ₹6.4 recorded on December 23 last year. Since July 16, the NECC has maintained the procurement price at ₹6.8 in Namakkal. Eggs are retailing at up to ₹8 apiece in many markets and ₹8.50-9 in parts of Kerala.

SUMMER’S IMPACT

After last year’s peak, prices eased before beginning a steady climb from June. The farmgate price rose from ₹5.7 on June 1 to ₹6.8 by mid-July. Industry representatives said the impact of the scorching summer is being felt now because birds entering the market were hatched during the peak heat period.

According to NK Saravanakumar of KL Poultry Farm, while Namakkal has the capacity to produce around nine crore eggs a day, the actual output is currently about six crore, reflecting the impact of weather and rising production costs.

FEED INFLATION

The production shortfall has been compounded by a sharp rise in feed costs. Saravanakumar said maize prices had increased to ₹28 per kg from ₹23 over the past three months, while soybean prices had risen to ₹71 per kg from ₹45, prompting some producers to cut output.

Vangili Subramaniam, President of the Tamil Nadu Poultry Farmers’ Cooperative Society, said soaring input costs had squeezed farmer margins despite higher egg prices. Higher temperatures have also reduced bird productivity and pushed up broiler prices. Farmgate chicken prices are currently around ₹150 a kg, while retail prices in several markets have climbed to ₹250 a kg.

Ricky Thaper, Joint Secretary, Poultry Federation of India, said as the sector continues to expand, long-term availability of soybean meal and maize is becoming a growing concern. Naveen Pasuparthy, Deputy Chairman, CLFMA of India, noted that soybean meal has jumped to around ₹67,000 a tonne from ₹39,000-40,000 during March-April.

The industry flagged a shortage of around 1.5 million tonnes of soybean meal before the arrival of the new crop. Since feed accounts for 65-70 per cent of poultry production costs, ensuring adequate supplies is critical to keeping eggs and chicken affordable. Industry associations have urged the Centre to allow limited import of genetically modified soybean meal during the lean season.

ROBUST DEMAND

Despite higher prices, consumption remains firm. Subramaniam said chicken remains far more affordable than mutton, which costs over ₹750 a kg. "Over the past decade or so, prices of chicken have not increased as compared to mutton, which has tripled," he said.

Crisil expects improved realisations from higher prices and steady demand to lift the Indian poultry industry’s revenue growth by around 10 per cent this fiscal, though volatility in feed costs and bird flu outbreaks remain key risks.

Gayathri G Chennai (With inputs from Prabhudatta Mishra in New Delhi; Vishwanath Kulkarni in Bengaluru; Sajeev Kumar in Kochi; TE Raja Simhan and Subramani Ra Mancombu in Chennai)

Vijay’s final act Jana Nayagan opens to fan frenzy

Real life drama meets reel life script as crowds dance their way into theatres

The release of Jana Nayagan, Tamil Nadu CM Joseph Vijay’s final act at the movies, was met with intense fan frenzy and celebrated in the State’s iconic style of hero worship.

Diehard Vijay fans thronged theatres at 8 am and danced their hearts out in front of large banners of the actor placed outside. Inside, the scenes were similar as cinegoers erupted with cheer when Vijay’s name flashed on the screen, this time with the additional title of “Honourable CM of Tamil Nadu”.

With this, the movie became the first film with a sitting Tamil Nadu Chief Minister as lead actor since 1977-78, when MG Ramachandran’s movies were released after he took office. The film marks the end of Vijay's years-long movie journey, leaving many fans teary-eyed.

MUCH AWAITED

The advance booking response for Jana Nayagan reflected exceptional anticipation, according to Ashish Saksena, COO — Cinemas, BookMyShow. Over 1.2 million tickets were sold on the platform, with Tamil bookings accounting for 97 per cent of the total.

Chennai leads the demand, followed by Coimbatore and Bengaluru, with strong momentum across key South Indian markets. Saksena noted that with over 3 lakh nationwide advance tickets sold, the film underscores the enduring appeal of the theatrical experience. Cinépolis India reported 100 per cent occupancy in its Coimbatore and Chennai cinemas, with surges also seen in Kerala and Andhra Pradesh.

The movie had previously got stuck over film certification issues. While awaiting clearance, Vijay pivoted fully to politics and unexpectedly became the State’s Chief Minister.

The screenings in Tamil Nadu had a festive atmosphere, attended by various government leaders, including N Anand (Minister for Local Administration), K G Arunraj (Health Minister), and A Srinath (Minister for Fisheries). Titled Jana Nayagan (meaning people’s leader), the film mirrors the actor’s rise to power this year with numerous politically loaded moments.

Yaash Raju, a Chennai-based professional who attended the first show, remarked, “The story didn’t work for me, but the emotions did”. Another fan described the film as “Thalapathy’s farewell party from movies,” stating that the sentiment meant more than the script itself.

Sindhu Hariharan Chennai

SWANSONG. Fans celebrate the release of Jana Nayagan, the final movie of Tamil Nadu CM Joseph Vijay, at Albert theatre in Chennai on Thursday.

PVR INOX swings to black with net profit of ₹56.5 crore, revenue up 11.9%

Leading multiplex chain PVR INOX reported a consolidated net profit of ₹56.5 crore in the June quarter compared to a net loss of ₹54.5 crore in the corresponding quarter in the previous fiscal. Consolidated revenue from operations grew 11.9 per cent to ₹1,622.2 crore on the back of a broad-based and balanced growth witnessed in terms of the box office collections.

The company said that Q1 FY27 marked a strong start of the year with a broad-based growth across metros as well as tier-2 and tier-3 markets across a wider set of successful mid-scale films across languages.

Sanjeev Kumar Bijli, Executive Director, PVR INOX Ltd, told businessline that this was the fifth consecutive quarter of growth for the company. He indicated that the strong first quarter of the fiscal reflects a structural strength and the company is now net cash positive.

He added that while Hindi cinema held its ground during the quarter, Hollywood movies found success from non-franchise titles while regional cinema delivered multi-fold growth. “It indicates whether films are big, small, regional, English or Hindi, consumers are coming back to watch them on the big screen. We can see this in the growth in admissions. We recorded 36.6 million admissions which was a year-on-year growth of 8 per cent. Average ticket price also grew by 8 per cent and spends per head grew by 9 per cent,” Bijli noted.

‘PREMIUM FORMATS’

Bijli said the company remains on track to open 100 new screens during FY27. “We are committed to enhancing premium formats as that enables us to offer a differentiated experience to consumers. Out of the 100 screens that we will add, about 20 per cent will be in the premium formats,” he added. Bijli said that nearly 64 per cent of the new screen additions will be done on an asset-light model.

“From a net debt of ₹14,304 million at the time of the merger, the company turned net cash positive during the quarter, with net cash of ₹807 million as of June 30, 2026. This gives the company complete strategic flexibility to pursue its capital-light growth agenda funded through internal accruals,” the company added in its financial earnings statement.

Bijli noted that the content pipeline for the remainder of FY27 is a strong mix of franchise films, star-led tentpoles and content-driven titles across languages.

Meenakshi Verma Ambwani New Delhi

Modi vows fast-track courts to punish exam leak culprits

SUPPORTIVE STANCE. Govt reaching out to protestors, Opposition to diffuse crisis

Our Bureau New Delhi

With Prime Minister Narendra Modi vowing on Thursday to set up fast-track courts for “swift and stringent punishment” to those involved in exam leaks which pushed agitated students to participate in the Jantar Mantar protest, the government intensified attempts to diffuse the crisis by repeatedly reaching out to the Opposition as well as the protestors for having discussions in Parliament and outside with the Cockroach Janta Party (CJP) functionaries, respectively.

“Nothing is more important than the welfare and future of our youth!” the Prime Minister posted on X early on Thursday. “We have decided to set up fast-track courts to ensure swift and stringent punishment for those involved in paper leaks. I have directed the concerned authorities and officials to take all necessary steps in this regard,” he said in his direct outreach to youth, two days after he reacted for the first time on the issue, calling it a “grave sin” at the NDA parliamentary party meeting.

Modi also stated in the social media post that this is part of a series of steps the government had taken to hold paper leak culprits accountable. “This continues our series of steps for safeguarding the interests of students. Those who try to harm the future of our youth will not be spared,” he promised.

Protestors and the CJP functionaries rejected Modi’s proposal, sticking to their demand for the resignation of Education Minister Dharmendra Pradhan, saying that the court trial could follow.

As a response to the PM, CJP founder Abhijeet Dipke shared an image of Pradhan bearing the caption, “Hi, my name is nothing”. The CJP reiterated, “Dharmendra Pradhan must resign” while its national spokesperson Ashutosh Ranka welcomed Modi’s response but said it came after nearly one-and-a-half months of protests and did not answer the central question behind the repeated paper leaks.

CONCERNS RAISED

Veteran BJP leader Murli Manohar Joshi came out in support of the protesting students, with a post on X. Seconding their “anxieties and concerns” regarding the examination system as “genuine”, Joshi criticised the police crackdown against the protesting students, especially girls, calling it “merciless”.

GENUINE WORRIES

“It is painful to see young students from different parts of the country assembled for days on the streets of New Delhi. Their anxieties and concerns regarding the examination system are genuine and must be handled with empathy and a desire to find a lasting solution,” Joshi said in his post.

Trouble-shooters from the government, Union Ministers JP Nadda, Kiren Rijuju and Jitendra Singh, were busy at work to break the stalemate between the ruling dispensation and the combined front of protestors and the Opposition.

Parliamentary Affairs Minister Rijuju said the government is hopeful of finding a resolution through discussions with the Congress and other Opposition colleagues, especially since the Prime Minister had made it abundantly clear on Thursday that steps were being taken to safeguard the lives and security of the students and the youth.

Speaking to reporters, Rijiju reiterated his earlier charge that he had reached out to Opposition members across party lines to facilitate a structured debate. But the Opposition began offering “excuses” to stall proceedings.

TALKS LINED UP

Parallelly, Union Minister Jitendra Singh said the Central government had reiterated its commitment to dialogue, stating that it had extended multiple formal invitations to student representatives for discussions.

“I would like to appeal to the students and young friends participating in this protest to come forward for a dialogue. Our doors are open,” he said, making a “humble appeal” on behalf of the government.

He said the discussion with the protesting students’ representatives will be held under the leadership of Union Health Minister and senior BJP leader J P Nadda.

Parliament proceedings remained stalled for the fourth day on Thursday with the Opposition unwilling to budge on its demand for the sacking of Pradhan from the Union Cabinet.