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Zen and the brain: mutually illuminating topics

Zen Buddhist meditative practices emphasize the long-term, mindful training of attention and awareness during one's ordinary daily-life activities, the shedding of egocentric behaviors, and the skillful application of one's innate compassionate resources of insight-wisdom toward others and oneself. This review focuses on how such a comprehensive approach to training the brain could relate to a distinctive flavor of Zen: its emphasis on direct experience , with special reference to those major acute states of awakening that create deep transformations of consciousness and behavior. In Japanese, these advanced states are called kensho and satori . Ten key concepts are reviewed. They begin by distinguishing between the concentrative and receptive forms of meditation, noticing the complementary ways that they each train our normal “top–down” and “bottom–up” modes of attentive processing. Additional concepts distinguish between our two major processing pathways. The self-centered, egocentric frame of reference processes information in relation to our body (our soma ) or to our mental functions (our psyche ). The other-centered frame of reference processes information anonymously. Its prefix, allo - simply means “other” in Greek. Subsequent concepts consider how these useful Greek words—ego/allo, soma/psyche—correlate with the normal functional anatomy of important thalamo ↔ cortical connections. A plausible model then envisions how a triggering stimulus that captures attention could prompt the reticular nucleus to release GABA; how its selective inhibition of the dorsal thalamus could then block both our higher somatic and psychic cortical functions; so as to: (a) delete the maladaptive aspects of selfhood, while also (b) releasing the direct, all-inclusive, globally-unified experience of other. Two final concepts consider how the long-term meditative training of intuitive functions relates to certain kinds of word-free spatial tasks that involve insightful creative problem-solving.

Only those contents of consciousness can be developed that correspond to the organization of the brain. Walter R. Hess (1881–1973)

Introduction

These words by Hess suggest that the following pages will invoke neurophysiological explanations, not loose metaphysical notions. That said, this review proposes that neuroscientists and Zen trainees can learn a few things from each other's models of consciousness. What does Zen training emphasize during its long-term approach to meditation? First, the mindful training of attention and awareness during one's ordinary daily life activities. Second, shedding the layers of maladaptive habits, overly self-centered attitudes and behaviors that waste time and energy. Third, enhancing one's innate, intuitive resources of insight-wisdom. Fourth, applying these fresh insights skillfully, with increasing compassion, both toward all other beings and one's own well-being.

These emphases are part of a long path of brain training that can lead toward more adaptive traits of character. The path emerges along that broad interface between two crucial domains, self and other. The former represents the distinctive interior consciousness of our personal self. The latter refers to that other consciousness representing the environment outside our skin. An important aspect of this path is epitomized in the historical account of a man, originally called Siddhartha. The records indicate that his behavior was substantially transformed after he emerged from a major state of awakened consciousness. Thereafter, he would be known as the “enlightened” one, the Buddha.

During the four decades since Hess died, the neurosciences have continued to learn more about the organization of the brain. Still, some readers may question: is it appropriate in this special issue of Frontiers for a neurologist to speculate about how such an acute episode could have transformed a 35-year old man like Siddhartha? On the other hand, only secular explanations will be tentatively proposed. They will address fundamental issues at the crucial interface between self and other. In order to focus on such an interface, this review leaves to other contributors the complex topic of correlating meditation with neuroimaging data. Left to its closing pages is the discussion of subtle incremental deconditionings of the maladaptive self, and a consideration of how such changes could emerge along a continuum of practical intuitive, creative, problem-solving traits . A glossary at the end helps to define useful terms.

The next pages summarize aspects of 10 key conceptual issues that are raised either in four books previously published or the one now in press. The Zen approach to training specifically targets unwarranted self-centeredness. It studiously avoids first-person references. This principle is not possible to achieve in this kind of review which necessarily refers to the author's several publications.

Ten key conceptual issues

The first concepts summarize Zen meditative practice in terms of psychological phenomena. Later concepts suggest principles of neural organization that govern important physiological mechanisms. These mechanisms are involved both during our normal modes of ordinary consciousness, and in our normal modes of ordinary sub conscious processing. Moreover, many of their phenomena surface in the foreground of the kinds of advanced extraordinary states of consciousness that are often called “awakening” or “enlightenment.” The Japanese terms for these brief states are kensho and satori . Samples of frontier research will later be cited which appear to have fertile interdisciplinary implications.

Zen meditative training

This meditative approach trains attention by focusing body and mind on the ongoing, mindful perception of each present moment as it really exists right now. Gradually, an increasingly calm awareness becomes the setting for the emergence of more subtle introspective memory skills. These are automatic “recollections”. They serve useful, self-correcting ends. (Austin, 2011 , pp. 95–96, 98). Their subconscious meta-cognitive memory functions were always inherent in the original meaning of the ancient Pali term, sati . A newly-coined word, “remindfulness,” can serve a useful purpose. It simply acknowledges this involuntary, helpful overview memory capacity. (Austin, 2011 , pp. 94–98) When Emerson pointed to the surge of this natural, insightful, affirmative mode of “guidance,” he used the apt phrase, “lowly listening” to describe its re mindful qualities (Austin, 2011 , p. 145).

The broad scope and depth of long-term Zen training

One doesn't “learn” Zen meditation in just a few days or weeks. The major target during the early months and years is one's self-centered I-Me-Mine complex and its unfruitful, maladaptive, emotional attachments. (Austin, 1998 , pp. 43–47, 50–51; Austin, 2006 , pp. 13–14) Long-term monastic training goes on to address the diverse existential, instinctual, and emotional aspects of the personality that cause unnecessary suffering. None of these ingrained egocentric attitudes surrender without a struggle. During meditative retreats (Austin, 1998 , pp. 138–140), trainees benefit from many opportunities both to endure their own liabilities and to uncover their innate assets, while being guided by an authentic teacher (Austin, 1998 , pp. 119–125; Austin, 2006 , pp. 64–69).

Three developments in the neurosciences have converged in recent decades

Research in meditators indicates that our dorsal attention system for top–down attention becomes more involved during the narrowly focused, concentrative meditative practices. (Austin, 2009 , pp. 39–43) In contrast, the ventral attention system orients itself reflexively toward the subtler forms of bottom–up attention and global awareness. These become cultivated more gradually during the other kinds of meditative practices that are more openly receptive .

Egocentric and allocentric attentive processing; major differences in their efficiencies. This view contrasts our top–down dorsal egocentric networks with those other networks representing our ventral allocentric , bottom–up pathways. The reader's vantage point is from a position behind the left hemisphere, looking at the lower end of the occipital lobe. This person's brain is shown gazing up and off to the left into quadrants of scenery. The items here are imaginary. The baby and the hammer are within reach, in the space down close to the person's body. The scenery above and the tiger are off at a distance, out of reach. Starting at the top of the brain are the two modules of the dorsal , top–down attention system: the intraparietal sulcus (IPS) and the frontal eye field (FEF). They serve as the attentive vanguards linked with our subsequent sensory processing and goal-oriented executive behavior. Notice how they are overlapped by the upward trajectory of the upper parietal → frontal egocentric (E) system. This is a self-referential system. Its arching red pathway is shown as beginning in the upper occipital region. Notice that a similar red color also surrounds the lower visual quadrants containing the baby (at left) and the hammer (at right). Why? To indicate that this dorsal, “northern” attention system attends more efficiently—on a shorter path with a lesser “wiring cost”—to these lower visual quadrants. This enables our parietal lobe senses of touch and proprioception to “handle” easily such vitally important tangible items down close to our own body. In contrast, our two other modules for cortical attention reside lower down over the outside of the brain. They are the temporo-parietal junction (TPJ) and the regions of the inferior frontal cortex (IFC). During bottom–up attention, we activate these two modules of the ventral attention system chiefly on the right side of the brain. There, they can engage relatively easily the networks of allocentric processing nearby (A). The green color used to represent these lower temporal → frontal networks is also seen to surround the upper visual quadrants. Why? This is to suggest the ways this lower (“southern”) pathway is poised globally to use its two different specialized systems of pattern recognition. One is based on our sense of vision , the other on our sense of audition . Both serve not only to identify items off at a distance from our body but also to infuse them instantly with meaningful interpretations. The yellow FG in parenthesis points to this lower pathway's inclusion of the left fusiform gyrus. This region, hidden on the undersurface of the temporal lobe, contributes to complex visual associations, including our sense of colors.

The figure helps to appreciate that this upper, occipito- parietal, egocentric processing pathway follows a trajectory that is closest to, and overlaps, those same lateral and superior cortical regions which also represent the body schema of our somatic , physical self. So, can this upper pathway be described as asking only a one -word question: “Where?” No. A hungry person, seeing an apple within reach, instantly refers all lines of sight from this apple back to his or her own physical axis of self in order to grasp it. Therefore, this upper pathway seems poised to ask a much longer question. This highly practical question is structured in terms that correspond with the three dimensions of our personal space: “Where is that apple in relation to Me and My body? ” (Austin, 2009 , pp. 54–55, 58–59).

The ventral pathway relies on other specialized refinements, those associated with its capacities for seeing and hearing. Notice the lower origin of this other cortical pathway. It begins in the inferior occipito- temporal region and extends toward the inferior frontal region. Such a trajectory confers a very different, allocentric perspective. (Austin, 2009 , pp. 55–74) Allo , from the Greek, simply means “other.” Why is the existence of this hidden other-referential pathway so unfamiliar to us? Because such a silent “frame of reference” begins to operate anonymously . It asks the question, “ What is that object?” Instantly, subconscious pattern-recognition skills identify that object. Simultaneously, other skills decode what that object means . (Austin, 2009 , pp. 130–149) When Freud coined the useful diagnostic term, agnosia , he could then apply it to the isolated loss of visual meaning caused by a discrete lesion which had damaged a patient's temporal lobe (Austin, 2009 , pp. 130–135).

Our ordinary conscious processing remains unaware that it is deploying a seamless blend of these two complementary—upper and lower—categories of spatial reference. (Austin, 2009 , pp. 57–58) Such ignorance vanishes when the entire upper version drops out . Zen parlance applies the following metaphor of selfless “emptiness” to this abrupt release from all former attachments to the intrusive self: “The bottom falls out and releases its bucket-full of water.” (Austin, 2009 , pp. 191–193) Now the lower version's allocentric processing stands unveiled . Its innate subconscious capacities, perceived in isolation, are revealed for the first time. This “awakening”—this fresh perspective of “real” reality, infused with instant recognition and meaning—is an awesome surprize. (Austin, 2006 , p. 361–371).

• Neuroscience research has also begun to attach a shorthand term, “default network,” to a large consortium of heterogeneous regions. The three largest regions occupy the medial prefrontal cortex, the medial posterior parietal cortex, and also the lateral cortex of the angular gyrus. Included within this triad and its several extensions are representations of both intrinsic, autobiographical and topographical association functions. (Austin, in press ) The more rostral parts of this coalition contribute to an impression of personal sovereignty: our self as the sole psychic agency, its operations consistent with those of a private, autonomous I-Me-Mine . (Austin, 2009 , pp. 74–75) Yet if each such mental notion about our personal self is to lodge in memory as a coherent event, it helps to encapsulate it within the context of some particular local scene. (Austin, 1998 , pp. 390–391) These topographical, scenic details are essential if this locale is to serve as the “frame” enabling each event memory later to be used for purposes of navigation. (Austin, 2009 , pp. 72–74) Only when each intimate episode is anchored both in one particular place in our own environment, and in one particular moment of our own “ time frame ” can these separate episodes become organized into a detailed, useful, lifetime, personal narrative.

How can any three-pound brain normally maintain such a life story, packed with notions that its omni-self is truly an ongoing, functioning agency? Not surprisingly, the early PET studies showed that the coalition of self-referential regions required an especially active ongoing metabolism even during seemingly “resting” conditions. Functional MRI signals arising from this consortium became even more activated (above such arbitrary “baselines”) when researchers assigned discrete self-referential tasks to their normal subjects. (Austin, 2006 , pp. 204–207; Austin, 2009 , pp. 75–76, 266–267) Subjects who tried to meditate in thought-free silence in the scanner discovered how much their “monkey-mind” wandered during the so-called “resting” state of quasi-“baseline” consciousness.

Importantly, fMRI signals from these same (mostly medial) intrinsic networks were acutely reduced (below baseline) at the instant that a sudden external stimulus event captured a subject's attention. Further fMRI research revealed a second important finding: the activity of these chiefly medial, self-referential cortical regions varied inversely with the activity of the lateral attention regions. (Austin, 2009 , pp. 98–108) These separate self and attention regions co-participated—but in opposite directions —in the peaks and valleys of a slow, spontaneous, endogenous rhythm. This reciprocal rhythm required no external stimulus. It recurred slowly —around three times a minute—on its own largely independent cycle (Austin, 2011 , pp. 32–34).

The Swiss physiologist, Walter Hess, was co-awarded the Nobel Prize in 1949. His pioneering research documented the dynamic switching capacities that lurked in the deep central diencephalic regions of the brain (Austin, 1998 , pp. 190, 194, 232, 635–636). Extensions of that research make plausible today's hypothesis that some regions in the axial core of the brain—in and around the thalamus—might qualify as the deep origins for these reactive and spontaneous shifts that occur up in the recent cortical fMRI data. After all, these are fast and slow, switchings on and switchings off . They are enlisting separate cortical regions that make crucial commitments either to attention or to the representations of the self. Moreover, these regions respond not only within both hemispheres simultaneously, but also in a reciprocal manner (Austin, 2006 , pp. 197–198, 427–428). Were Hess alive today, he would surely be encouraging neuroscientists to identify which basic mechanisms organize these two remarkable anticorrelated physiological feats.

Major awakened states are experienced as selfless

The annals of Zen demonstrate more than an early, historical emphasis on the mindful training of attention and awareness (Austin, 1998 , pp. 69–73). They also document numerous examples during which a sudden external stimulus—like the unexpected “CAW” of a crow—served not only to instantly capture attention but also to precipitate states of awakening (Austin, 1998 , pp. 452–457; Austin, 2006 , pp. 303–306; Austin, 2009 , pp. 109–117; Austin, 2014 , Chapters 5, 6). So, one wonders: could Siddhartha's legendary “awakening,” two and a half millennia ago, also represent a comparable event? Indeed, it was said that this state of enlightenment was triggered, before dawn, when he looked up and saw the brilliant “morning star” (Austin, 2011 , pp. 77, 209). The ancient astronomers were familiar with this bright celestial object. They recognized the obvious fact that it traveled on a path, unlike a star. They gave the name, Venus, to this distinctive planet whose brilliance still lights up our Eastern sky before sunrise.

Explicit statements about selflessness entered the Buddha's earliest discourses. Examples of no-self sentences are found in two of the ancient Udana sutras: “When no you remains, then this, just this , will be the end of your suffering.” “Getting free of the conceit that “I am” is truly the ultimate happiness.” (Austin, 2014 , Chapter 1; Austin, 2011 , vi) Scientists have reason to be skeptical: could any rigorous common ground ever link old Buddhist legends with neurobiology? (Austin, 1998 , pp. 1–7, 677–683). On the other hand, might what neuroscience is learning about the neurophysiological origins of selfhood help explain how selflessness could occur?

Our normal unified thalamo-cortical connectivities

Intricate interactive circuitries merge thalamic functions with those of our cortex (Austin, 2006 , pp. 167–176). What is special about the three limbic nuclei in the front of our dorsal thalamus? Why could they play a crucial role in modulating these to-and-fro, rapidly oscillating connections between thalamus and cortex? Because these are the three thalamic nuclei poised to relay over-emotionalized messages from our limbic system up to pertinent parts of our self-referential cortex and to receive reverberations from them. Indeed, these three limbic nuclei interact with most of those same medial cortical association regions that item 3C had just singled out. Recall that these key regions of neocortex appear to be organized in a manner that can represent major attributes consistent with the autobiographical aspects of our psychic self that are linked to its topographical memories (Austin, 2009 , pp. 87–94) (Please see Figure ​ Figure2 2 ).

Thalamocortical contributions to the dorsal egocentric and ventral allocentric processing streams. This composite view shows the pathways (normally bi-directional) which connect the thalamus with the cortex. For convenience in viewing, only left-sided structures are shown. These connections supply key regions both on the outside and inside of the left hemisphere. Pathways predominate from all five nuclei in the dorsal tier of thalamic nuclei. Up front are the three limbic nuclei. The medial dorsal (MD) thalamic nucleus projects to the prefrontal cortex, both to its medial (MPFC) and to its outer, dorsolateral (DLPFC) surfaces. The deep medial area of cortex in the back of the brain is shown enlarged at the top right. Here, the projections from the two other adjacent dorsal nuclei of the limbic thalamus can also be seen reaching this medial cortical surface (dashed lines). Thus, the anterior thalamic nucleus projects to the posterior cingulate cortex (PCC) (a major connection hub), and the lateral dorsal nucleus (LD) projects to the retrosplenial cortex (RETROSPLEN), a major memory-based resource for recollection. Down at the bottom right, one path leads up from the dorsal pulvinar (DPUL) to the angular gyrus (ANG) on the outer cortical surface. Dashed lines indicate that a second path from this dorsal pulvinar leads up to the precuneus (PRECUN) on the medial parietal surface. Other projections from the lateral posterior (LP) nucleus supply the superior parietal lobule (SPL), our major somatosensory association region. These connections help to anchor each person's subliminal sensory impression: I exist as an independent, tangible, physically-articulated body schema. Relatively few pathways are shown emerging from the thalamus to serve the lower, allocentric processing stream. However, messages from the ventral pulvinar (VPUL) would pass first through the region of the fusiform gyrus (FG) on the undersurface of the temporal lobe. These associations ramify, to become further refined on their way forward and upward through the other-referential networks in the rest of the temporal lobe that lead on toward the frontal lobe. The figure shows three important GABA inhibitory nuclei, artificially detached at the bottom. They are the reticular nucleus , the zona incerta (ZI), and the anterior pretectal nucleus (APT). Two sensory relay nuclei of the thalamus are also shown at the bottom right. The lateral geniculate nucleus (LG) relays visual data to the occipital cortex. The medial geniculate nucleus (MG) relays auditory information to the auditory cortex. The superior colliculus (SC) in the midbrain relays its reflexive visual and related polymodal messages quickly through both the dorsal and ventral pulvinar to the cortex. Its counterpart, the inferior colliculus, plays a similar auditory role. Not shown are two somatic sensory relay nuclei in the ventral tier. These medial and lateral divisions of the ventral posterior nucleus lie in front of the ventral pulvinar. They relay sensation from the head and body, respectively. cingulate gyrus (CG).

Figure ​ Figure2 2 illustrates which limbic nuclei and cortical regions are organized to become co-activated either during an acute surging emotional overload or during subsequent un-checked ongoing ruminations (Austin, 1998 , pp. 347–352, 567–570; Austin, 2006 , pp. 239–265, 396–398; Austin, 2009 , pp. 223–247; Austin, 2011 , pp. 160–162). They include: (a) the medial dorsal nucleus and its several connections with the medial prefrontal cortex in particular; (b) the anterior thalamic nucleus and its connections, including those with the posterior cingulate cortex in particular; (c) the lateral dorsal nucleus and its connections with the event-memory-linked topographical and navigational functions of the retrosplenial cortex.

The thalamus serves as an important “pacemaker” for the brain. All of our sensations ascend through its nuclei (except for smell) on their way up to our cortex. Yet these bi-directional thalamo ↔ cortical connections, over-simplified here in Figures ​ Figures1, 1 , ​ ,2, 2 , only hint at some explanations for how we might normally represent our omni-self's ordinary perceptions, emotions, and its journal entries into long-term memories. The question remains: how could a brain drop off the deep layers of the maladaptive self during the awakened states of kensho and satori?

The inhibitory role of the reticular nucleus of the thalamus and its extra-reticular allies

A thin layer of nerve cells caps the rounded contours of the thalamus (Austin, 1998 , pp. 267–271, 591, 605; Austin, 2006 , pp. 176–178). This is the reticular nucleus . Existing fMRI data don't list it. Do not be misled. Its neurons release GABA (gamma aminobutyric acid), a powerful inhibitory neurotransmitter. Could this GABA play a “self-annihilating” role? Yes. How? By virtue of its selective capacities to readjust the synchrony of our usual thalamo ↔ cortical oscillations (Minn, 2010 ; Austin, 2011 , pp. 35–37). Included in this selectivity are the capacities to dissociate visual functions in the ventral and dorsal streams (Austin, 2014 , Chapter 11).

A simple analogy helps illustrate how the release of GABA might shift our waking consciousness away from its usual, dominant self-centered role. Try on a set of active, noise-canceling headphones. They generate a profile of sound-wave oscillations. These are 180° out of phase with those frequencies causing undesirable background noise. When the headphones are switched on, their selective tuning accomplishes two goals simultaneously: you hear the music coming through clearly; you block out the loud rumble of distracting traffic noise.

The reticular nucleus does not act alone. It has two, lower level, extra-reticular inhibitory allies (Austin, 2006 , pp. 178–179) (Figure ​ (Figure2). 2 ). The zona incerta releases its GABA on higher-order thalamic nuclei (including the medial dorsal nucleus). The anterior pretectal nucleus projects to both the medial dorsal and lateral dorsal nuclei of the limbic thalamus. It also stands poised to modulate the diversity of relevant functions among the somatosensory, intralaminar, and other nuclei that are included within the ventral and medial thalamus (Sherman and Guillery, 2013 ).

An instructive Zen “mini-koan”: which direction do you face, when you want to move straight “ahead”?

Most self-orientations operate automatically. A covert motivation urges our first leaning forward. Instantly—too fast for thought—it commits us to “head” in one particular direction (Austin, 2006 , pp. 106–108). If such subconscious systems are to resolve the riddle posed above, they first need to “know” where our own head is already located, and be capable of using visual information from our eyes that confirms the direction toward which the rest of our face is assumed to be pointing.

In short, we're using a subconscious, direction-sensitive, personal “behavioral compass” (Austin, 2006 , pp. 172–174). Why might two nuclei of the limbic thalamus (anterior [dorsal] and lateral dorsal) be assigned some role in establishing this covert behavioral compass? Perhaps because this subcortical network does more than add to our static notions of being a private, interior self. Perhaps it could also be preparing us to lean, move, and navigate. How? By accessing subconscious links to hidden memories, to reconstructions of past events that had “taken place,” as we say, in the scenery outside our skin. We had encoded useful personal/local details during these remote prior events (Austin, 1998 , pp. 390–391; Austin, 2009 , pp. 259–260). These had served to frame the topographical signature representing each new, memorable spatial environment.

Recent fMRI evidence indicates how we retrieve two such events from memory (Elman et al., 2013 ). One event is newly-learned; the other is old and familiar. The newly-learned event was the outside appearance of one particular building. When subjects retrieved this building from recent memory they activated their anterior angular gyrus, supra-marginal gyrus, posterior cingulate and posterior precuneus (Austin, 2009 , pp. 252). However, they activated different regions when they retrieved a long-familiar location from memory. This was a different building, the appearance of which was linked to intimately personalized resonances. In contrast, this more meaningful, self-centered, older recollection correlated with different activations. These occurred in the posterior angular gyrus (along the ego centric, E, parietal processing pathway of Figure ​ Figure1), 1 ), and in the lateral occipital cortex. Moreover, the medial regions now activated included the anterior precuneus and retrosplenial cortex, as well as the parahippocampal gyrus and the medial prefrontal cortex. Emerging from such subliminal topographical contrasts is the suggestion that when I become familiar with a place, I tend to regard it almost as though it had become part of Me and was on My “turf.”

Different roles for the dorsal and the ventral thalamic nuclei during extraordinary states of consciousness

Item 5 explained how the five nuclei of the dorsal thalamus contribute to the normal overly-dominant egocentric attitudes of our psyche and soma. Item 6 then drew attention to selective capacities of deep inhibitory systems. It suggested that these GABA systems could have the potential to dissolve one's psychic and somatic association functions from the field of consciousness during kensho . The words, anatta , no-self, and non- I are among the standard terms used to describe the resulting selfless attributes of this advanced state (Austin, 2009 , pp. 118–121). This abrupt dissolution at the core of the sense of self is noteworthy for two reasons. It can help explain why the deep roots of primal fear also drop out of kensho's field of waking consciousness, as does all personal sense of the passage of time ( achronia ) (Austin, 2006 , pp. 378–383; Austin, 2009 , pp. 193–196).

What other networks could remain active in a brain during kensho? What evidence offers a potential explanation for the fresh impression of unity that prevails throughout the whole “new” field of conscious experience? Leading the list of candidates would be the brain's lower allocentric pathways. Their “other” frame of reference is not a new category of experience. This anonymous perspective had always been there, its contributions playing only a hidden, subordinate role. (A in Figure ​ Figure1) 1 ) Now liberated from the dominant subjectivities of the former intrusive self, these other-referential resources could be openly expressed in an undiluted, disinhibited manner. Such a release from prior suppression could then be further enhanced in the course of a disinhibitory rebound (Austin, 2006 , pp. 416, 418, 421).

Different observers bring an array of cultural and personal expectations to the vast topic of “enlightenment” (Boyle, 2013 ). Suppose a curious reader were to press one neurologist-witness to distill the 18 characteristics of kensho's awakening into only two simplified sentences (Austin, 1998 , pp. 542–544). The language—strange on any page—might sound something like this:

• In empty anonymity, this now-unveiled other-referential mode is liberated into the foreground of consciousness, reifying the perfection of the whole mental field with a meaningful global objectivity beyond reach of mere words (Austin, 2006 , pp. 329–333, 383–387).
• An astonishingly fresh impression of immanent reality prevails throughout this non-dual state of “Oneness”: all things, seen selflessly in the total absence of fear, are comprehended “as they really are.”

What seems to vanish in kensho's fresh perspective? Certainly not the world in 3D. What vanishes is only the former self's intrusive sense of sovereignty over it. When the former egocentric self no longer remains the “owner” of its ongoing perceptions, then the environment opens up to reveal percepts that are experienced with utmost “objectivity” (Austin, 1998 , pp. 43–47, 263–267, 591–621; Austin, 2006 , pp. 327–356, 414–432; Austin, 2009 , pp. 85–94; Austin, 2011 , pp. 163–165).

Shodo Harada Roshi ( 2000 ) aptly describes such a major transformation. It is an emptiness that cannot be conceptualized: “a state of being empty of ego, but full of what can come through [i.e., allo-perception] when that ego has been let go of.”

Do not conclude that the gradual diminution of the ego during decades of authentic Zen training will remove that pragmatic sense of self which enables one to resolve life's complexities by adapting to them in an increasingly mature, realistic, matter-of-fact way. Rather is the training oriented toward dissolving those negative, neurotic distortions of ego defenses imposed by one's overconditioned self-centered attachments (Austin, 1998 , pp. 34–36). Longitudinal multidisciplinary studies will be required in order to specify which precise sequences of neural mechanisms need to mature in order for genuine wisdom and compassion to evolve. This caveat holds both for the kinds of decades-long research required to study normal control populations (Vaillant, 2012 ) or a carefully-matched cohort of long-term meditators (Austin, 2006 , pp. 352–356, 399–401; Austin, 2009 , pp. 221–262).

A different model of organization and reorganization is proposed for that alternate state of consciousness termed internal absorption (Austin, 1998 , pp. 469–479; Austin, 2010 ). Internal absorption represents a preliminary state, one not uncommon during the early years among trainees who mediate regularly. Paradoxical phenomena enter its intensified awareness: vision “sees” into a vast, pitch-black ambient space; audition “hears” the “sound of absolute silence,” perception loses every physical sense of self from head to toe. In a quest for preliminary explanations of internal absorption, its agenda of 16 descriptors again leads us to the frontiers of neuroscience research.

A plausible model for such sensate phenomena can also begin on the basis of a GABA blockade (Austin, 1998 , pp. 589–590; Austin, 2006 , pp. 313–322; Austin, 2009 , pp. 98). This inhibition could be applied to the lowermost regions in the back of the thalamus. (Please refer to Figure ​ Figure2) 2 ) Inhibition down here could prevent impulses from rising up to the cortex through the lowermost ventral sensory relay nuclei. They include the lateral geniculate, medial geniculate, ventral posterior medial and lateral nuclei, respectively.

A descriptive continuum of intuition: ordinary creative insights at one end; extraordinary states of awakened insight-wisdom off toward the other

Carl Jung (1875–1961) understood the key role of intuition. It supplies, he said, that “superior analysis, insight or knowledge which consciousness had not been able to produce” (Jung, 1969 ; Austin, 1998 , pp. 545–553). Could long-term meditative training do more than cultivate a person's attention and affirmative traits of character? Could it also influence subconscious, intuitive mechanisms that subtly enhance a person's flexible creative problem-solving behavior? (Austin, 2003 , 2014 , Chapter 14).

Two recent experiments by Strick et al. ( 2012 ) are germane. The 63 meditators in their first experiment had completed 6 months to 5 years of prior Zen practice. In the evening, between 6 and 9pm, one group (led by a Zen master) then meditated for 20 min. The second Zen group served as controls. They relaxed at the same time, without meditating, also for 20 min. Both groups then went to individual cubicles. There they performed three sets of five Remote Associates Tests using a computer screen. (For example, given three words, search your associations for that one fourth word which they all share in common.) The subjects who had just meditated solved more test items (7.00) than did those who had merely relaxed (5.94), p = 0.02.

In the second experiment, the response times of 32 Zen meditators were measured during similar word association tests. Again, the subjects who had just meditated solved more Remote Associates Test items than did their controls (6.82 vs. 4.87), p < 0.01. They also solved them faster (taking only 13.22 vs. 16.37 s), p < 0.05. In addition, the groups were then asked to free associate to a new collection of different questions. However, in this instance, each of the 20 questions might have not just one, but three or four possible answers. (e.g., “Name one of the four seasons.”) Moreover, this time— before each question—a priming word-answer appeared subliminally on the screen (e.g., “Summer”). No subject could “see” this hidden, priming word consciously, because it lasted only 16 ms.

Now the question was: could meditation unveil any of their subconscious sensitivities? If so, would this hidden awareness enable the subliminal priming word to reshape the answer? The meditators' answers did match the hidden priming words at the p = 0.06 level, just short of statistical significance. In contrast, the relaxed control group showed no priming effect.

The Remote Associates Test is often interpreted as a task for verbal creativity that combines an initial divergent search with convergence functions. Therefore, the experiments suggest that these particular Zen meditators (tested in the evening ) showed evidence consistent with an enhancement of creative processing after having meditated (Austin, 2009 , pp. 125–130, 154–188). Why should researchers in the future specify the hours during which they conduct their cognitive experiments? Because many normal subjects may not reach their performance maximum for working consciousness until the later hours between 7 and 9 pm (Austin, 1998 , pp. 338–347). In this regard, Shannon et al. ( 2013 ) made an intriguing observation. In normal subjects, the medial temporal regions show greater local fMRI connectivities in the morning hours. In the evening hours, more connections open up that will link the frontal and parietal neocortex with the striatum and brain stem.

In their study of creativity, Colzato et al. ( 2012 ) interpreted this Remote Associates Task as a way to index the competence of convergent thinking, and assessed the productivity of divergent thinking with the Alternate Uses Task. Their 19 meditators had been practicing mixtures of concentrative and receptive forms of meditation for an average of 2.2 years. The task for each subject was to spend only 35 min a day (on each three separate days) either in concentrative meditation, or in receptive meditation, or in a baseline control condition. The data suggested that 35 min of focused meditation (only) did not sustain convergent thinking processes toward a single solution, but that a separate 35 min of receptive meditation did support divergent thinking processes.

Our normal ordinary, intuitive quests for meaning become successful only when we repeatedly apply subtle convergent and divergent attentive processing mechanisms with appropriate flexibility. Many of these flexible interactions integrate the functions of fronto-temporal and fronto-parietal lobe networks in particular (Austin, 2009 , pp. 130–173). However, diverse phenomena unfold quickly during advanced alternate states of consciousness. They present unusual blendings of different attributes. The two sentences condensed in item 8 suggest that the flash of selfless insight-wisdom (Skt: prajna ) is extraordinary for two reasons. First, for the way it dissolves self-centered processing; second, for the way it liberates other functions that are consistent with enhanced modes of allocentric attentive processing (Austin, 2009 , pp. 178–188, 199–214). The egocentric vacancy in the core of the psyche leaves an extraordinary impression: all root origins of selfhood and deep natural survival angsts seem to have dropped off. This acute, ineffable release from the deepest instincts of primal fear is especially liberating (Austin, 1998 , pp. 569–570; Austin, 2006 , pp. 232–237, 357–387; Austin, 2014 , Chapter 14).

Relevant evidence from recent experiments in non-meditating subjects

Insight happens . Insight is not driven by logic-tight sequences of deliberate thought. For centuries, Zen masters emphasized that advanced degrees of insight (Skt: prajna) played a key transforming role in the Buddhist meditative Path. The masters also warned their trainees: steer clear of those heavy burdens imposed by word language, ruminating thoughts, and fixed conceptual barriers (Austin, 2009 , pp. 150–152; Austin, 2014 , Chapters 3, 6, 8). Does any contemporary research into the pros and cons of language appear to support such an orthodox empirical stance?

The first task for the normal subjects studied by Bergen et al. ( 2007 ) was straightforward: listen to words that would be spoken in the form of simple, short, recorded sentences. The whole meaning of each sentence hinged on where, in space, each event could have taken place. The two possibilities were either higher up , or lower down , in the extrinsic environment. For example, in some sentences the subjects could hear an up-word (“sky”) being spoken. Other sentences would specify a down-word (“grass”). The subjects' next task was a simple visual discrimination: they indicated by a button press whether they saw a circle or a square. Each visual target appeared either at a higher, or lower place on the computer screen. They could see each ◦ or □ clearly, during a long 200 ms interval. Some subjects took 30 ms longer to signal this discrimination. What caused this slowing? Why had they hesitated? Notably, the delay occurred when they saw either this square, or this circle, in that very same —upper, or lower—location, the same place which had just been inferred by that up-or-down word-language inserted into the prior sentence. When did these prior spatial nouns or verbs cause the greater interference delays? When both the visual and the auditory processing converged during those tasks that were chiefly referable to the upper visual fields. Figure ​ Figure1 1 suggests that these upper visual fields are represented by the lower occipital ↔ temporal lobe pathways.

How do words interfere with other brain functions? Could word entanglements that arise among language networks (perhaps especially in our left hemisphere) sometimes compete for neural resources with nearby intuitive processing mechanisms? Could such obstacles, acting either directly or indirectly, block the free-flowing access to adjacent networks that might otherwise help us express innate degrees of selfless, insight-wisdom?

In this regard, a different kind of experiment has examined the ways the right and left hemispheres function in normal subjects (Chi and Snyder, 2011 , 2012 ). In these studies, the brain is being stimulated by the gentle flow of direct current. This technical approach is called transcranial direct current stimulation (tDCS). This direct current is delivered at low amperage to the scalp over the right and left anterior temporal regions, simultaneously . However, the left anterior temporal scalp electrode serves as the negative pole (–). This cathodal pole tends to hyper polarize the resting potentials of nerve cells in the underlying left anterior temporal lobe. This makes these left nerve cells less excitable. In contrast, the right anterior temporal scalp electrode serves as the + pole. This anodal pole tends to de polarize the resting potentials of the underlying nerve cells on the right side. What is the net result of these modulations? Notice that this result generates the asymmetrical expression of temporal lobe functions. Those pathways on the right side become more readily excitable. Those on the left side—the side dominant for language—become less excitable (Austin, 2014 , Chapter 13).

These normal subjects received transcranial direct current stimulation to the scalp in this manner for only 10–17 min. Next they were challenged to solve either some difficult “matchstick” tests (Austin, 2009 , pp. 183–184) or to solve another, very difficult, “connect-the-9-dots” test. In brief, the data showed that tDCS substantially improved the subjects' creative problem-solving performance for up to the next hour beyond that which occurred either in the sham controls, or when the directions of current flow were reversed. These results have implications both for the kinds of complications introduced by our words and for the nature of the kinds of insights we use during creative processing.

It turns out that old Greek words, plus a few old words used in Zen, overlap with concepts recently evolving in neuroscience. The results can be mutually illuminating if their correlations are interpreted with appropriate caution. A longitudinal perspective is essential. Thirty-five year old Siddhartha had devoted six long years to a rigorous spiritual quest before he happened to glance up before dawn at that legendary morning star (Austin, 1998 , pp. 7–8).

Is it feasible to attempt to develop a theoretical neural basis for such an “awakening” of insight-wisdom? The 10 topics just reviewed in this condensed version are the latest sample emerging from several decades spent exploring testable hypotheses (Austin, 1998 , xvi; Austin, 2006 , xvi; Austin, 2009 , xvi; Austin, 2011 , pp. 169–177). Some of these have the potential to clarify thorny issues increasingly important to meditators, researchers, and to society in general (Austin, 2012 , in press ).

Conflict of interest statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Unleash Coding Speed: Zen Coding for Efficient Development

Feeling bogged down by mountains of code? Discover Zen Coding, a technique that charges your HTML and CSS workflow. Learn how to write code faster and smarter.

Introduction

In the fast-paced world of software development , efficiency reigns supreme. While writing lines and lines of code can be a necessary evil, wouldn't it be amazing to achieve the same results in a fraction of the time? Enter Zen Coding, a powerful technique that empowers developers to write HTML and CSS with remarkable speed and precision. This blog gives some much-needed insight into the world of Zen Coding, exploring its core concepts, benefits, and how it can transform your development workflow.

Zen Coding: A New Paradigm for Writing Code

Forget the preconceived notions of "coding zen" as a meditative practice. Zen Coding takes a refreshingly pragmatic approach. It's a complete paradigm shift from the traditional, line-by-line approach to writing HTML and CSS. Here's the magic: Zen Coding leverages a CSS-like syntax with a set of abbreviations and symbols to generate full-fledged HTML structures with remarkable speed. Imagine constructing an entire navigation bar or a complex form layout with just a few keystrokes!

Unlocking Efficiency: The Power of Zen Coding

Zen Coding operates on the principle of abbreviation expansion. You write a concise abbreviation following specific rules , and with a tap of the tab key, the abbreviation explodes into the corresponding HTML structure. Here's a glimpse into the magic:

• Elements and Classes: Define an element with a class by separating them with a dot (.). For example, div.container expands to <div class="container"></div>.
• IDs: Assign an ID to an element using a hash (#). For example, #header expands to <header id="header"></header>.
• Nesting: Create nested structures using the greater than symbol (>). For example, ul>li*3 generates an unordered list (<ul>) with three list items (<li></li>) nested within.
• Attributes: Include attributes within the abbreviation using curly braces ({ }) and colons (:). For example, a[href="https://www.scrums.com"] expands to <a href=" https://www.scrums.com "></a>.

These are just a few fundamental examples, and Zen Coding offers a vast array of functionalities to streamline your workflow. You can define loops to generate repetitive elements, create comments, and even work with pseudo-classes and pseudo-elements commonly used in CSS.

The Two Sides of the Coin: Advantages and Considerations of Zen Coding

Integrating Zen Coding into your development process unlocks a treasure trove of advantages:

• Boosted Productivity: By eliminating repetitive typing and allowing you to generate complex structures quickly, Zen Coding empowers you to write code exponentially faster. Imagine the time saved by generating a complete navigation bar with nested menus in seconds instead of minutes!
• Reduced Errors: Less typing translates to fewer typos and syntax errors, leading to cleaner code and a smoother development experience. With Zen Coding, you're less likely to introduce errors associated with manually typing long tag names, classes, and attributes.
• Enhanced Readability: While the abbreviations might seem cryptic at first, Zen Coding often results in more concise and well-structured code. This improves readability and maintainability for both you and your collaborators, making future modifications easier.
• Cognitive Boost: Freeing up mental space from tedious typing allows you to focus on the bigger picture – the logic and functionality of your code. This fosters creativity and problem-solving skills, allowing you to approach coding challenges with a fresh perspective.

However, it's important to consider a few factors:

• Learning Curve: There's a learning curve associated with mastering Zen Coding's syntax and abbreviations. While the basics can be picked up quickly, becoming proficient requires practice and familiarity with the various functionalities.
• Code Readability for Newcomers: For developers unfamiliar with Zen Coding, the initial exposure to the code might seem cryptic. However, with practice and clear documentation, this hurdle can be overcome.
• Limited to HTML and CSS: While Zen Coding is a powerhouse for front-end development, it's not a universal solution. It's primarily focused on HTML and CSS, and other programming languages might require different approaches for efficient code generation.

Zen Coding is a game-changer for developers seeking to streamline their workflow and write code with exceptional speed and efficiency. While there's a learning curve, the advantages far outweigh the initial investment of time. By incorporating Zen Coding into your skillset, you'll be well on your way to achieving coding mastery – not through meditation, but through intelligent automation.

At Scrums.com, we're passionate about empowering developers with the latest tools and techniques. If you're looking to streamline your workflow and unlock your full coding potential, Zen Coding is an excellent place to start. Contact Scrums.com today to discuss how our custom software development services can help you leverage Zen Coding and other innovative approaches to build exceptional software solutions.

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Stratechery by Ben Thompson

On the business, strategy, and impact of technology.

An Interview with AMD CEO Lisa Su About Solving Hard Problems

Good morning,

This week’s Stratechery Interview is with AMD CEO Lisa Su . Su began her career at Texas Instruments, after earning her PhD in electrical engineering at MIT, where she played a significant role in developing silicon-on-insulator transistor technology. Su then spent 12 years at IBM, where she led the development of copper interconnects for semiconductors, served as technical assistant to CEO Lou Gerstner, and led the team that created the Cell microprocessor used in the PlayStation 3. After a stint as the CTO of Freescale Semiconductor, Su joined AMD in 2012, before ascending to the CEO role in 2014.

Su has led a remarkable run of success for AMD over the last decade. After decades of being an also-ran to Intel, AMD has developed the best x86 chips in the world, and continues to take significant share from Intel in datacenters in particular. AMD has also been a major player in console gaming, in addition to its traditional PC business and graphics chip business. That GPU business is now increasingly at center stage, as AMD takes on Nvidia in the market for datacenter GPUs.

In this interview, conducted a day after Su’s Computex keynote , we talk about Su’s career path, including lessons she learned at her various stops to the top, before discussing why AMD has been able to achieve so much during her tenure. We discuss how the “ChatGPT” moment changed the industry, how AMD has responded, and why Su believes the long-run structure of the industry will ultimately work in the company’s favor.

As a reminder, all Stratechery content, including interviews, is available as a podcast; click the link at the top of this email to add Stratechery to your podcast player.

On to the Interview:

This interview is lightly edited for clarity.

Lessons From IBM

Lisa Su, welcome to Stratechery.

Dr. Lisa Su: Thank you. It’s great to be here.

I’m truly honored to have you here. I’ve had the opportunity to talk to a lot of your peers in the semiconductor space , and without fail one of the bits of feedback I get from my subscribers is, “When are you going to talk to Lisa Su?”. I want to thank you for helping me now avoid those emails going forward.

LS: Thank you, I appreciate that. I’m very happy to have this opportunity to chat.

I know you don’t want to talk about yourself too much, but I need some fact verification here. We were just talking before we started recording, you were born in Taiwan, emigrated to the US when you were very young, eventually ended up at MIT, where as legend has it, you were deciding between computer science and electrical engineering, and chose electrical engineering because it was harder. Is this true?

LS: It is actually true. I was always around math and science, my parents were always saying, “You have to do these hard things”. When I went to MIT, at that time it was a decision between electrical engineering and computer science. Computer science, you could just write software programs, whereas electrical engineering, you had to build things. I wanted to build things.

They had to actually work, right?

LS: Yes, that’s right.

Your PhD was focused on silicon, on insulator technology, and then you went to IBM — you pioneered using copper interconnects on chips. I have three questions about your IBM experience and what lessons you might’ve learned. Number one, when it comes to the copper interconnect, you said something, I think was to the MIT Review , where you were ready to do something new, but your boss made you stay, and you felt like the actual learnings you accumulated in that time when you thought you were done were some of the most impactful. What were those learnings?

LS: I really learned so much when I was at IBM, it was the early part of my career. When you go to school and when you get a PhD, you think the sexy thing is the research that you do and the papers that you write, which we all write papers and things like that.

When you actually join a company and join a project, these projects are usually several years to complete something. But the “sexy stuff” is at the beginning when you’re coming up with the new ideas.

What I learned actually was one of the very first products that I worked on was a microprocessor with copper interconnects, and it turns out that the last 5% of what it takes to get a product out is probably the hardest, where most of the secret sauce is. And if you learn how to do that, then frankly, it’s—

All the software engineers are saying, “Hey, it’s the same thing for us. Don’t you know?”

LS: (laughing) That might be true, that might be true. But we all have this view of what “secret sauce” is. It’s things like yields, reliability, when something goes wrong. When you’re trying to produce millions versus producing five of something, you learn a lot, I learned a ton.

Yes, as a young researcher you think, “Hey, I’m ready to move on to my next thing”, and you realize that it’s so rewarding to see your product actually ship and go on the shelf and you can walk into Best Buy and buy it. Those are the types of things that I learned.

How much, even today, do you feel your time and attention ends up being balanced between what you’re building going forward versus actually executing and getting what you’ve promised out the door?

LS: Certainly today, I personally spend a lot of time looking forward in terms of roadmaps, forward in terms of technologies.

You just have more patience for the rest of the company that’s trying to get it shipped.

LS: That’s right. Frankly, a lot of time on customers and markets, and where’s the market going and where should we be investing.

Just out of curiosity, how deeply do you need to be involved in things like, not you specifically, but AMD generally, now that you’re fabless , in thinking about that actual final mile? What’s the degree of interaction with, say, a TSMC or your packaging partners or whatever it might be, and actually getting the yields up and out the door?

LS: It’s definitely true for us as a fabless company or a design company. We are actually doing end-to-end development, so you can imagine from the day one of concept of a product — actually even before that, we’re thinking about what technologies are going to be ready, what are the next big things that we should bet on? That goes all the way through. Sometimes it could be a five-year cycle or even longer before the technology actually comes to fruition. We’re right there at the end as well, ensuring that it ships with high quality, at the right yields, right cost structure, in high volume production.

So, it’s really end-to-end and the difference is it’s not all in one company, which you would see in a more traditional integrated manufacturing model, it’s through partnership. We found that actually it works extremely well, because you have experts on all sides working together.

The second IBM lesson I’m curious about is you worked on the Cell processor that shipped in the PlayStation 3. That chip was a technological marvel, but the PlayStation 3 is viewed as the least successful PlayStation, which drove a real shift in Sony strategy in the long run, away from hardware differentiation towards exclusives . I guess this is a two-parter, but number one: What did you take away from that experience? Number two ties into this: How much impact did that have on your later gaming experience? The gaming experience question is obvious. I’m more curious, was there a management takeaway from all the work you put into the Cell processor and the reality of how that manifested in the market?

LS: Yeah, it’s interesting that you mentioned that. I’ve been working on PlayStation for a long time, if you think about it, PlayStation 3, 4, 5…

It’s like the common thread through your career.

LS: Across multiple companies, yes. What I would say honestly is, these are decisions that are made that are more around architectural decisions. From that standpoint, whether you talk about any of the PlayStation consoles or some of the other work that we’ve done in partnership — we being AMD, but it was similar during that time at IBM — it really is a close collaboration of what the customer or partner is trying to achieve.

The Cell processor was extremely ambitious at that time, thinking about the type of parallelism that it was trying to get out there. Again, I would say from a business standpoint, it was certainly successful. As you rank things, I think history will tell you that there may be different rankings.

My perspective is, the console era has gone through phases, and that phase in PlayStation 1 and PlayStation 2, they made smart hardware decisions, and that differentiated their approach from Nintendo in particular. But once you went to HD, you had tremendous increase in cost of asset creation, you had developers heavily motivated to support multiple processors, you had game engines coming along. Suddenly, no one wanted to go to the burden of differentiating on the Cell, they just wanted to run on the Cell.

LS: Perhaps one could say, if you look in hindsight, programmability is so important.

LS: Being able to have real business success on day one of anything, we have to think about both hardware and software. As we’ve seen, one of the things that I’m very proud of of the work that we’re doing or have done at AMD over the last 10-plus years was PlayStation 4 and PlayStation 5 is we’ve always had new leaps in hardware.

Much easier.

LS: And they come with compatibility to the previous generations, which is super helpful.

Number three: You were Lou Gerstner’s technical assistant for a year. What did you learn from him? This is a pro-Lou Gerstner podcast , for the record.

LS: (laughing) You’ve done your homework, haven’t you? The year that I spent with Lou was one of the most educational experiences of my career. IBM was a fantastic company at talent development, so they identified people earlier in their career and they said, “Hey, what types of experiences would you like?”.

In my case they asked me, do you want to go on the technical track or do you want to be more on — let’s call it — the management track, the terminology would be an IBM Fellow or an IBM Vice President. Honestly, I didn’t think I was going to be smart enough to be an IBM Fellow. There were people like Bob Dennard , who are—

I feel like there’s people that would dispute that characterization.

LS: There were amazing people there, and so I was like, “Okay, let me try this thing at management and business”. They gave me an opportunity to spend a year with Lou, he’s just an amazing person. If you think about someone out of school five years who has really only done, let’s call it, pure engineering, and then getting to sit for—

It’s basically the best MBA in the world.

LS: Yes! Yes, it absolutely was, and what was most interesting to me is really understanding where he spent his time. The time was always trying to learn, was very externally focused, and understanding what’s going on in the market, what’s going on with customers.

That ties into what you were saying what you do today.

LS: Exactly. How does that change your strategy and how does that change how you guide the leadership team? The fun thing that I got to do is, I got to teach him about technology. I would say, “Hey Lou, there’s this interesting new thing, Napster, people are downloading it” — I don’t know if you remember that.

I do, I introduced my college dorm floor to Napster. That was my claim to fame in college.

LS: I got to introduce Lou to Napster. We were really thinking about what digital rights management meant at that point in time, those were just the types of things that we got to think about.

The other thing that I always appreciate about Lou Gerstner is, to your point, it wasn’t just looking outside and understanding the market, what’s going on, but really understanding what was IBM, what was IBM intrinsically capable of and uniquely differentiated at. Basically my perspective is, IBM was big, and what does that actually mean? What can you actually bring to bear in a way? The whole middleware revolution, and look, we can solve this Internet problem for companies that are even older and bigger than us, and that’s going to be a differentiated thing. But then, obviously, it all fell apart. IBM should have done the cloud, Lou actually wrote that in his book , I don’t know how much looking backwards that was. If you had taken over after him, could you have led IBM to greater heights?

LS: I don’t know that I would’ve been on that path. I was a semiconductor person, I am a semiconductor person. If I really think about, IBM was such a wonderful career for me, but if I wanted to stay a semiconductor person, I had to go to a semiconductor company.

You went to Freescale [Semiconductor] after that, much more in a business role.

Was there a personal admission of, “Okay, I’m a business person now”, or was that in the choice when you went that direction?

LS: I have always straddled the technology/business line there. At Freescale I actually started as CTO. I joined as CTO, and then over a couple of years I ended up running the networking and a multimedia business, that was definitely a choice and the choice is, at the end of the day, I want to drive outcomes, and to drive outcomes requires, yes, the technology is great, but you need to have the right business strategy.

Is that the limiter for a lot of technical people, is they under-appreciate all the drivers of outcomes that have nothing to do with technology?

LS: I think that that is something that technologists have to learn. And by the way, there are phenomenal CTOs who truly understand that. My CTO right now, Mark Papermaster , he was my partner in crime at IBM, we grew up together, and then we’ve been partners here at AMD, he truly understands that technology is great, but you also need to drive business outcomes. That’s what I love about what I get to do, because yes, I get to put together great tech with a phenomenal team, but there’s also an opportunity to drive very significant business outcomes.

The AMD Turnaround

Let’s talk about AMD. I mentioned the console strategy earlier, that was a big shift in focus when you came on board. Was that just a view like, “Look, this is an easy win, high volume, we can get back in the game”? What was the thinking there?

LS: Well, I would never say anything is an easy win.

Yeah, fair enough.

LS: Let me start with this notion of when I first joined AMD, we were probably 90 plus percent in the PC market and by the way, I really like the PC market. We’re going to talk more about it I’m sure.

Absolutely. You spent the first 45 minutes talking about the PC market yesterday .

LS: Yes, so I’ll caveat with that. But the PC market goes in cycles and the cycles can be quite dramatic.

Very painful.

LS: They can be — I would use the word quite dramatic. So when you see from business strategy standpoint, it was really important for us early in the AMD days to diversify and get to a strategy where the underlying principle is around high-performance computing. We are a computing company, we are great at building computing capability, and now what are the markets that can really utilize that? Gaming is one of those markets and we are very fortunate to have both Sony and Microsoft as leading console manufacturers to choose us.

Who was driving that shift to x86 in consoles? How much was this Sony learning the lesson of Cell? Was that you going to them and saying, “Look, this is the way to go”? How did this commonality of architecture develop?

LS: Yeah, look, I think it was a set of choices, so it was a choice between x86 and other architectures, and if you think about just the developer ecosystem around x86 when you’re thinking about software development, I think that was a very key piece, but I don’t know that the architecture itself was enough. I think the incredible graphics capability and the fact that graphics, especially if you want to customize graphics, there are very few companies who can do that, AMD was one of them.

Even then, to what extent was there integration between the CPUs you’re delivering and the GPUs? AMD had acquired ATI in 2006 , so before your time there, but was there any other company that could have actually delivered what you did for the consoles?

LS: I would say we were unique in what we were capable of doing for two reasons. One is we had the fundamental IP, so the combination of let’s call it the CPU or the microprocessor cores with the graphics IP capability, and we were willing to customize. Frankly, we have huge teams that were put on these projects to customize.

Do you see this as a pattern where initially, it’s all about the cutting-edge, getting the best possible performance, but as it, I don’t want to say slows down, but capabilities commoditize, the customization is more important. I mean, you’ve acquired …I can never pronounce it correctly.

LS: Xilinx .

Xilinx, I’ve never been able to pronounce that word for two years. It sounds like this customization approach, there seems to be a common thread there. That’s something you want to leverage.

LS: The best way I say is there are a couple of principles. First, the fact is the world needs more semiconductors. Semiconductors, chips are now foundational to so much of what we do and much of what we do are, let’s call it standard products that fit a broad set of use cases. But you do find those high volume applications like game consoles, like some of the work that’s being done in the cloud right now, like some of the AI work I believe will be customized, and in these cases, because the volume is so high, it makes sense to customize. That’s something that I’ve always believed. It’s part of our strategy, it’s part of our strategy of deep partnerships. So if you have the right building blocks, then you can work with the broad set of customers to really figure out what they need to accomplish their vision.

Is there a bit though, where as design costs as we move down the process curve are just becoming so astronomically high that there’s a bottom limit to customization, where only AMD has sufficient scale to customize, paradoxically?

LS: I think the important thing is looking at which markets really lend themselves to let’s call it significant customization, and it’s not everything. Probably your IoT device, you’re not going to want to do that because it just won’t return on investment. But for large computing capabilities, I think the combination of the right IP plus the ability to work deeply with partners. By the way, I should say, it doesn’t all have to be hardware customization, there’s a lot that we can do in software work as well, I think it is one of the important trends going forward.

So I have to ask, you came to AMD, you were there for a couple of years, then you took over the CEO role. Was that another example of choosing the hard problem?

LS: I think it was. I’ll say it this way, when I joined AMD, it was really this idea that I’ve worked on high performance processors all my life, that was my background, there are very few companies in the United States that you could do that at. I always respected AMD very much as a company that mattered, but I thought I could make a difference, and so joining the company, I realized that, “Boy, there was a lot I had to learn”. Those first couple of years, I did learn a lot about just the market dynamics in this world, but it was also a phenomenal opportunity to make a difference.

Where could you make the difference? We can see the difference — I mean, just look at the stock chart , and we can look at the performance of your chips. So in that context, it’s maybe hard to get in your exact state of mind 10 years ago, but what was your plan? What did you say, “Look, I can do this, there’s something, there’s a path here, I see it”? What was the path that you saw?

LS: What I saw that was very clear is that we had the building blocks of what you needed to build an incredible roadmap. We were very differentiated in those building blocks.

What were those building blocks? Was that IP or was that customer relationships?

LS: It was high performance CPUs and high performance GPUs and if you think about it, those are pretty incredible building blocks. Now, what we didn’t have is a very clear strategy of what did we want to be when we grew up, and then an execution machine that could make that happen.

So from a strategy standpoint, I think we had some choices to make. If you remember back, this is 2014, the exciting thing then was mobile phones, like apps processors. So we would have these conversations like, “Should we go into phones?”, and we were like, “No, we shouldn’t because we’re not a phone company. There are others who are much better at that, we are a high performance company, so we have to build a roadmap that leverages our strengths and that requires us to revamp the way we do architecture and design and manufacturing”. I know how to do that, it’ll take time, you can’t do that in 12 months, I think I felt it would take five years. It would take five years, but it was very clear that we had the pieces, we just had to really methodically build that execution engine.

Well, you mentioned manufacturing. AMD had spun out GlobalFoundries before you took over, I want to use the technical term here, how much of a pain in the ass was the eternally amended wafer agreement you had with GlobalFoundries? Was that just something you just had to deal with on a constant basis as you’re trying to execute the strategy?

LS: Well, to be fair, AMD and GlobalFoundries were one company at one time, yeah.

It was there for a reason, it was understandable.

LS: Exactly. So that wafer supply agreement was something that was before my time, but I think it was one of the larger — if I think about the couple of big strategy things that we had to do, it was if you want to build high performance processors, you need the best technology partner, the best manufacturing partner and GlobalFoundries is a great company and they’re still a great partner. It’s just you need scale to be able to build at the bleeding-edge, and the scale didn’t exist.

Was it almost a blessing in disguise when they internalized that and, “ We’re not going to 7nm “?

LS: It was a very good decision for both of us and financially, AMD had to —

Yeah, you had to give all the money back that you had gotten originally.

LS: There was a business arrangement, but from a technology standpoint, it was absolutely the right thing to do, and like I said, GlobalFoundries is a great partner for us. I have tremendous respect for [GlobalFoundries CEO] Tom Caulfield as a partner, and I think both companies were better off by focusing on what we were going to be good at.

Well, you were the first high performance chip maker to move to chiplets, and everyone’s headed there now, so you’re definitely in the lead in that regard. Is there a bit where you were actually forced there because of the wafer agreement so that you could do some volume with GlobalFoundries, some with TSMC and still deliver your chips?

LS: Not at all. Actually, I think that was clearly one of the best decisions that we made, it wasn’t that clear at the time.

Yeah, for sure.

LS: But what we were looking at is where was Moore’s Law going, and how were we going to differentiate ourselves? Frankly, our thought process was we needed to bring something to the processor market that was different, so building these big humongous chips that didn’t yield and were very expensive wasn’t going to be the answer.

So I remember very well spending time with Mark and our architects and trying to decide, “Is this the time that we go to chiplets? Is this the time we’re going to bet the company on going to chiplets?” And we said, “Yes, it is because we’re going to get to much higher performance, many more cores, as well as a much better cost point”, and it gave us tremendous flexibility, and we learned a lot along the way.

The first generation Zen 1 chiplets were okay, but we had some programming model issues that we had to deal with and that got better with Zen 2 and really, really hit our stride with Zen 3 .

When you were took over in 2014 and you felt like you could make a difference, I see a couple of big shifts here. So you have the shift to chiplets on your side, that’s around the time TSMC is beginning or transitioning to EUV. To what extent did you see those secular shifts in the market and that informed your decision that, “Look, there’s something I can do here”?

LS: Yeah, we definitely looked very much at the technology roadmaps and what TSMC was doing, as well as just where the packaging technology was at the time and we decided that this was the time to make the bet. I like to say that the world that we live in is we have to make bets that sometimes take three to five years to come to fruition.

Yeah. I don’t mind asking you about 2014 decisions because that’s often when decisions that matter today were being made.

LS: That’s exactly right, and there was risk associated with that in terms of, “Would we actually get the performance that we thought we were going to get by going to chiplets?”, but we learned a ton, and I think history would say we made the right bet, but at the time, some of our competitors were calling it glue , they were gluing chips together. It’s like, “We’re not gluing chips together”.

And now they’re doing the exact same thing . What’s the balance of credit when you look back over the 10 years and AMD actually taking the performance lead in a really meaningful way in x86? Where do you balance the credit between your design decisions and being on the leading-edge process from TSMC and how that paid off?

LS: I really believe that they are inextricably linked.

Yeah, the decisions went together.

LS: Absolutely, and it’s one of those things what we found was so helpful, and TSMC is a phenomenal partner in this realm. It’s when you take a lot of design risk, you want to know that your technology is rock-solid so that you know where to spend time and effort.

That’s what TSMC and ASML did, like going to 300mm and then going to EUV, that co-partnership, they had proved out that could be done and then you were able to do it with you at the same time, following on from that.

LS: That’s right, I think it’s been a very synergistic partnership.

AMD’s most consequential moment before your tenure was actually, we were talking about this earlier, is when they went from x86 to 64 bit and dragged Intel kicking and screaming in that regard, and it’s kind of a hardware and a software story. That was before your time, but it strikes me that one of the ongoing critiques of AMD is the software needs to be better. Where is the software piece of this? You can’t just be a hardware cowboy. When you joined in, was there a sense of, “Look, we had this opportunity, we could have built on this over time”. What is the reticence to software at AMD and how have you worked to change that?

LS: Well, let me be clear, there’s no reticence at all.

Is that a change though?

LS: No, not at all. I think we’ve always believed in the importance of the hardware-software linkage and really, the key thing about software is, we’re supposed to make it easy for customers to use all of the incredible capability that we’re putting in these chips, there is complete clarity on that.

I think what you will see is that we’ve actually been on several arcs of technology development. So, the CPU arc and everything that we’ve done to build the Zen product portfolio. Now, we just previewed Zen 5 here at Computex , in the data center, and then launched it in the client products. That particular arc was one arc and now we’re in sort of the next arc, which is around—

The GPU arc.

LS: Yeah, AI and GPU.

I do want to ask you one other thing. As far as this trend, we talked about the chiplet trend, we talked about the EUV thing. How important was the rise of hyperscalers to your success? Because what I see from that is, they’re buying at such scale, they will actually do LTV calculations to say that, “Look, yes, these AMD processors are worth it in the long run”. And number two, to the extent there is a software hole, they will do the work to fill that in, because they can see the long-term benefit. Did that impact when you were thinking about what we can actually win here? Was that a driver?

LS: Yeah, it’s a great point. When you think about high-performance computing and just how things have changed, the fact is, the hyperscalers are such a significant piece of the overall market that we have spent a lot of time there, and the point that you make is absolutely true, which is — you’d like to think in every market, the product always wins but that’s not necessarily true. In the hyperscaler market, the best product wins.

LS: And we were able to show that. Frankly, the key thing in this market is, it’s not enough to win once and it’s not enough to win temporally.

You have to win the roadmap.

LS: You have to win the roadmap and that was very much what we did in that particular point in time.

And so when you come in 2014, you’re like, “Look, I can see a roadmap where we can actually win”.

LS: That’s right.

And there’s customers coming along that actually will buy on the roadmap.

LS: That’s right and by the way, they’ll ask you to prove it. In Zen 1, they were like, “Okay, that’s pretty good”, Zen 2 was better, Zen 3 was much, much better. That roadmap execution has put us in the spot where now we are very much deep partners with all the hyperscalers, which we really appreciate and as you think about, again, the AI journey, it is a similar journey.

Yes. Well, one more question on x86. How do you think about the consumer space in conjunction with all this? You think about, say like an Intel, they have to keep the fabs full, so they need to maximize their chips for everything. The point with fabs is, Intel wants to be integrated and there’s a bit where AMD is in a different position so they can meet the hyperscalers where they are better and just make great chips. But is there a volume consideration just because you want to get leverage on your design costs, on your IP investments? I’m just curious how those calculations work in a world where it’s not your fabs on the line, it’s not your billions of CapEx. I’m curious how you think about that differently from an integrated player.

LS: The way we think about it is, it is about scale. When we were back in 2014-15, we were a $4 billion company and in that case, you can spend a certain amount of R&D. Last year, we were like, a whatever, $22 plus billion company, you can spend a lot more on R&D.

Yes, so basically, it’s still the same calculation by and large.

LS: It is the same calculation of how do we leverage.

But maybe less risk of going bankrupt if you spend way too much on fabs.

LS: Well, I think the key thing is leveraging of the IP. It’s sort of the engines, the compute engines that we have. That’s our absolutely number one priority, is to get those compute engines on a very aggressive roadmap and then, we build products out of that.

What was your response in November 2022 when ChatGPT shows up?

LS: Well, it was really the crystallization of what AI is all about.

Obviously you’ve been in the graphics game for a long time, you’ve been thinking about high-performance computing, so the idea that GPUs would be important was not foreign to you. But were you surprised the extent to which it changed the perception of everyone else around you and what happened after that?

LS: We were very much on this path of GPUs for high-performance computing and AI. Actually, it was probably a very significant arc that we started, let’s call it back in the 2017 plus timeframe. We’ve always been in GPUs, but really focusing on-

What was it in 2017 that made you realize that, “Wait, we have these, we thought we bought ATI for gaming, suddenly, there’s this completely different application”?

LS: It was the next big opportunity, we knew it was the next big opportunity. It was something that Mark and I discussed, which was, by putting CPUs and GPUs together in systems and designing them together, we’re going to get a better answer and the first near-term applications were around super-computing. We were very focused on these large machines that would reside at national laboratories and deep research facilities and we knew that we could build these massively parallel GPU machines to do that. The AI portion, we always also thought about it as clearly a HPC plus AI play.

You said before that AI is the killer application for HPC .

But you will talk to people in HPC, they’re like, “Well, it’s a little bit different”, to what extent is that the same category versus adjacent categories?

LS: It’s adjacent but highly-related categories, and it all depends on the accuracy that you want in your calculations, whether you’re using the full accuracy or you want to use some of these other data formats. But I think the real key though, and the thing that really we had good foresight on is, because of our chiplet strategy, we could build a highly modular system that could be, let’s call it, an integrated CPU and GPU, or it could be just incredible GPU capability that people needed.

And so, the ChatGPT moment for me was the clarity around, now everybody knew what AI was for. Before, it was only the scientists and the engineers who thought about AI, now everybody could use AI. These models are not perfect, but they’re amazingly good, and with that, I think the clarity around how do we get more AI compute in people’s hands as soon as possible was clear. Because of the way we had built our design system, we could really have two flavors. We had HPC-only flavor, which is what we would call our MI300A and we had AI only flavor, which was the MI300X.

Was that kind of an uncomfortable shift? Like, “Actually, no, we want less precision because the scalability is so important”.

LS: It wasn’t uncomfortable. It was strikingly fast.

It happened so fast. AMD has done very well, you hit an all-time high a couple of months ago. But by and large, obviously Nvidia captured the gestalt as it were in a lot of the momentum and upside. What did they have, from your perspective, in that period that AMD had to catch up on?

LS: I think the way to think about it is just, where was the focus and relatively speaking — look, I give [Nvidia CEO] Jensen [Huang] and Nvidia a lot of credit. They were investing in this space for a long time before it was absolutely clear where things were going. We were also investing, although I would say we had a couple of arcs. We had our CPU arc, and then we have our GPU arc.

Hey, your hands are full crushing Intel, so I get it.

LS: I would say it a different way, we are at the beginning of what AI is all about. One of the things that I find curious is when people think about technology in short spurts. Technology is not a short spurt kind of sport, this is like a 10-year arc we’re on, we’re through maybe the first 18 months. From that standpoint, I think we’re very clear on where we need to go and what the roadmap needs to look like. One of the things that you mentioned earlier on software, very, very clear on how do we make that transition super easy for developers, and one of the great things about our acquisition of Xilinx is we acquired a phenomenal team of 5,000 people that included a tremendous software talent that is right now working on making AMD AI as easy to use as possible.

One of the things that does strike me about the contrast is, and one of Nvidia’s really brilliant moves was the acquisition of Mellanox and their portfolio in networking, and to the extent it matters to tie all these chips together, particularly for training.

In your Computex keynote, you talked about the new Ultra Accelerator Link and Ultra Ethernet Link standards, and this idea of bringing lots of companies together, kind of calling back to the Open Compute Project back in the day as far as data centers. Makes perfect sense, particularly given Nvidia’s proprietary solutions have the same high margins, we all know and love, as the rest of their products.

But I guess this is my question about your long-term run — do you think it’s fair to say that, from a theoretical Clayton Christensen perspective , because we’re early in AI, maybe it’s not a surprise, the more proprietary integrated solution is the belle of the ball in many respects? There’s a bit where, yes, being open and modular all makes sense, but maybe that’s not going to be good enough for a while.

LS: I would say it this way. When you look at what the market will look like five years from now, what I see is a world where you have multiple solutions. I’m not a believer in one-size-fits-all, and from that standpoint, the beauty of open and modular is that you are able to, I don’t want to use the word customize here because they may not all be custom, but you are able to tailor.

Customize in the broad sense.

Tailor is a good word.

LS: Tailor is the right word — you are able to tailor the solutions for different workloads, and my belief is that there’s no one company who’s going to come up with every possible solution for every possible workload. So, I think we’re going to get there in different ways.

By the way, I am a big believer that these big GPUs that we’re going to build are going to continue to be the center of the universe for a while, and yes, you’re going to need the entire network system and reference system together. The point of what we’re doing is, all of those pieces are going to be in reference architectures going forward, so I think architecturally that’s going to be very important.

My only point is, there is no one size that’s going to fit all and so the modularity and the openness will allow the ecosystem to innovate in the places that they want to innovate. The solution that you want for hyperscaler 1 may not be the same as a solution you want for hyperscaler 2, or 3.

Where do you think the balance is going to be then, between there being a standard approach versus, “This is the Microsoft approach”, “This is the Meta approach”? There’s some commonality there, but it is actually fairly customized to their use cases and needs. Again, not next year, but in the long run.

LS: I think as you get out three, four or five years, I think you’re going to see more tailoring for different workloads, and what happens is, the algorithms are going to — right now, we’re going through a period of time where the algorithms are just changing so, so quickly. At some point, you’re going to get to the place where, “Hey, it’s a bit more stable, it’s a little bit more clear”, and at the types of volumes that we’re talking about, there is significant benefit you can get not just from a cost standpoint, but from a power standpoint. People talk about chip efficiency, system efficiency now being as important if not more important than performance, and for all of those reasons, I think you’re going to see multiple solutions.

Is this an underrated tailwind for your x86 business? You talked in your keynote about the fact that the majority of CPUs in the cloud are more than five years old, and you said something like, “One of our CPUs can replace five or six of these old ones”. Do you see that being actually — because right now I think there’s a lot of trepidation around your business and Intel’s business that all the spend is going to AI, no one’s even buying CPUs anymore, is this sort of power wall where if we can take out a bunch of CPUs from our data center, we can save power by putting other ones?

LS: I think both things are true. I think the modernization of data centers absolutely has to happen. It will happen, and then the other point is—

It might not happen right now.

LS: Well, no. I think we’re seeing the investments come back into the areas for modernization, but the other thing that’s really important is, again, as much as we love GPUs, that’s a huge growth driver for us going forward, not every workload’s going to go to a GPU. You are going to have traditional workloads, you’re going to have mixed workloads, and I think that’s the key point of the story is there’s a lot of things that you have to do in large enterprises, and our goal is to make sure that we have the right solution across from all of those capabilities.

How much inference do you see actually going back to the CPU?

LS: I think a good amount of inference will be done on the CPU, and even as you think about what we’re talking about is the very large models obviously need to be on GPUs, but how many companies can really afford to be on the largest of models? And so, you can see now already that for smaller models, they’re more fine-tuning for those kinds of things, the CPU is quite capable of it, and especially if you go to the edge.

Competing With Nvidia

Right. You noted on the last earnings call that the MI300, it’s been supply-constrained, your fastest ramp ever, but is maybe from the expectations of some investors, a little disappointing in the projections for the end of the year. How much do you feel that shift to being demand-constrained is about the 325 coming along , which you talked about this week, versus the fact that just generally Nvidia supply has gone up, as everyone’s trying to figure this stuff out? Yes, your long-term opportunity is being this sort of customized supplier — tailored supplier, sorry, is the word that we’re going for — versus, “Look, I don’t want to say picking up but just we need GPUs, we’ll buy them from anyone”. Where do you feel your demand curves are relative to the competition and the rapid progression of the space?

LS: Again, let me take a step back and make sure we frame the conversation. The demand for AI compute has been off the charts, I think nobody would have predicted this type of demand, and so when I say that there is tightness in the supply chain, that’s to be expected, because nobody expected that you would need this many GPUs in this timeframe. The fact is the semiconductor industry is really good at building capacity, and so that is really what we’ve seen. As we’ve started to forecast-

And so you feel it’s more a function of there’s just so much supply coming online?

LS: Absolutely, and that’s our job. Our job is to make it to a place where you’re not constrained by manufacturing capacity.

Really, for us, it is about ensuring that customers are really ramping their workloads and that is a lot of deep work, deep partnerships that we’re doing with our customers. So honestly, I feel really good about the opportunities here. We’ve been through this before where it’s very similar to what we saw when we did the initial data center server CPU ramps, which is our customers work very closely with us, they get their software optimized, and then they add new workloads, and add more volumes, and that’s what I would expect to happen here, too.

The difference in AI is that I think customers are willing to take more risk, because there’s a desire to get as much, as fast as possible.

Is there a challenge for you, because that desire to take more risks means they’re more accepting of say, high margins to get the leading GPUs or whatever it might be, or the GPU with the largest ecosystem, developer ecosystem?

LS: What I will say is I’m super happy with the progress we’ve made on software.

Fair enough.

LS: What we’re seeing is excellent out-of-box performance. The fact is things just run, the fact is that much of the developer ecosystem wants to move up the abstraction layer, because everybody wants choice.

And you feel you’re going to get to a stage where that move up the abstraction layer is a common layer across companies, as opposed to getting one company internally moves up the abstraction layer, and so they can buy any CPU, but that doesn’t necessarily benefit you going into another company, or do you feel that’s going to be-

LS: I absolutely believe that it’ll be across the industry. Things like PyTorch , I think PyTorch is extremely widely adopted, OpenAI Triton , similar. These are larger industry things where frankly, part of the desire is it takes a long time to program down to the hardware. Everyone wants to innovate quickly, and so the abstraction layer is good from the standpoint of just rapid innovation.

You’ve been traditionally a second wave adopter of TSMC’s new nodes, maybe a year, year-and-a-half behind. Do you feel pressure to move up to the top tier? Obviously, you’re a relatively small company to some of the players in this world, $22 billion is impressive, but you still have to think about your costs in that regard. Or is it just a pressing need to be on the absolute cutting-edge?

LS: Well, I think you would say that we’re one of the top five for sure in terms of just overall our volumes from a fabless standpoint, and absolutely bleeding-edge is helpful. It’s not something that we think about in terms of should we or shouldn’t we, I think what we think about is from a roadmap standpoint, like for example, we talked about a one-year cadence in terms of GPUs coming out.

Unfortunately, for you kind of on the opposite tick tock from Nvidia a little bit, is that a little frustrating?

LS: No, not at all. Look, again, one of the things that’s important for me is our roadmap is based on what we believe is possible, and what we believe our customers want and need.

Everyone like me wants to talk about the short term head-to-head, so annoying.

LS: No, it’s not so annoying, it’s just context, everything requires context.

Is there ever a world where AMD fabs with Intel?

LS: I would say that we’re very happy with our manufacturing relationships right now.

It does occur to me, Intel, AMD — it’s one of the greatest rivalries in the history of technology from basically the very beginning. Is there a bit though where when you step back, you want to step back in these conversations, there is a bit where you are in it together, because the real enemy is Arm?

LS: You make it sound like Arm is an enemy, I don’t consider ARM an enemy, so let me start with that. We use Arm all over our product portfolio. I consider the fact that we think x86 is a phenomenal architecture, and the capabilities are there, but please don’t think of AMD as an x86 company, we are a computing company, and we will use the right compute engine for the right workload.

As it relates to how I think about — if you look at the semiconductor industry today, there are places where we compete, and then there are places where we partner. So on your Intel point, we do compete in certain areas, but we also partner in certain areas. Intel is part of the UALink Consortium, they’re part of the Ultra Ethernet Consortium.

They’re very interested in this sort of modularization and standardization as well.

LS: We agree with this idea of having a link that can go across different accelerators is actually a good thing. So, I think that’s true across the industry. We’re at a place where there are places we compete, but there are also places where we can partner.

You have had an amazing 10-year run with the x86 results you’ve done in the server space, the data center, speaks for itself. Now, it’s like a new champion appears, are you girded up and ready to go for another round?

LS: This is the next arc. I can tell you that the thing that’s so amazing about where we are today in high-performance computing is, who would imagine? It’s like a new world. It’s an incredibly exciting.

You’re feeling re-energized, you’re ready to go?

LS: Absolutely ready to go. More than ready.

Lisa, thank you very much.

LS: Thank you.

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Zookeeper Zen: Mastering Collaborative Problem Solving in Animal Care

In a morning team meeting, Sarah shared, “Saucy the Centaur has a limp,” her voice had a mix of worry and determination. Saucy, a 45-year-old Centaur, was showing signs of distress, and Sarah felt obligated to advocate for her.

The meeting room erupted with opinions. Some zookeepers, echoing Sarah’s concern, believed it was time to schedule X-rays and delve into the root cause of Saucy’s discomfort. Others argued, suggesting that the limp could be attributed to the relentless pacing brought on by ongoing construction activities near Saucy’s enclosure.

However, amidst the escalating debate, the manager remained seemingly unfazed. The concern over Saucy’s well-being was met with an eerie calm that frustrated the room. “It’s like she doesn’t even care,” thought Sarah. 

As discussions became heated, voices rose, and chaos ensued. This happened almost every time the team brought up a concern. They ended up on opposing sides, with frustration simmering among keepers and management. The meeting reached an abrupt, disheartening end, leaving the zookeepers dispirited, the manager seemingly indifferent, and Saucy’s condition unresolved.

In this atmosphere of confusion and discord, the very essence of teamwork appeared to crumble. The zookeepers left the meeting upset with each other and disillusioned with the management’s lack of decisive action. The concern for Saucy, initially a shared worry, transformed into a source of division. 

Much of the day was spent whispering in the hallways. Sarah went around to talk to each keeper, trying to plead her case and convince them that something had to happen. We call this auditioning complaint in the zookeeper world, and it occurs when there isn’t a collaborative problem-solving strategy in place. 

This opening scene sets the stage for exploring collaborative problem-solving strategies, illustrating the real challenges zookeepers face and the necessity of effective communication and innovative decision-making in ensuring the well-being of their beloved animal charges.

Three Methods for Collaborative Problem-Solving

Explanation and steps: the 1-3-1 method.

The 1-3-1 method is a structured approach to collaborative problem-solving that encourages individuals to think through problems and solutions before raising them. It’s a simple yet powerful technique that empowers zookeepers to navigate complex challenges effectively AND efficiently. Let’s break down the steps and explore how zookeepers can seamlessly integrate this method into their daily routines:

1. Identify the Issue (1st Step):

• Prompt Observation: Begin by identifying the specific issue or challenge at hand. In our scenario, Sarah notices Saucy the Centaur’s limp during the team meeting.
• Clarity is Key: Ensure that all team members clearly define and understand the problem. This step establishes a shared understanding of the challenge. The problem is the limp. 
• Root issue: Sometimes, the surface-level problem isn’t the root issue. Try digging deeper to make sure the root issue is also clearly defined. In this case, the root issue could be an injury caused by pacing or something else. Sarah should bring all possible causes to the meeting. 

2. Generate Three Possible Solutions (2nd Step):

• Solutions: Ideally when concerns are raised to the team, there are already solutions brought forth as well. Sarah could bring three feasible solutions to the meeting for discussion, like scheduling an x-ray exam, moving Saucy away from construction, or monitoring her condition for the next week for signs of improvement. 
• Diverse Perspectives: If solutions can’t be considered before the meeting, Encourage team members to brainstorm and contribute three distinct solutions to the identified problem. In the context of Saucy’s limp, this could range from the above three ideas to modifying Saucy’s environment to alleviate stress from construction noise.
• Foster Creativity: Emphasize the importance of creativity and diversity in proposed solutions. This step broadens the range of potential strategies to address the issue. In this case, Sarah’s manager would remind the team during this period that there are no bad or wrong ideas. 

3. Select the Best Solution (3rd Step):

• Solution Ready: Typically, when problems are brought to the meeting, the team member raising them will have their three feasible solutions and the one they think is best, and why. Sarah had this when she asked for an X-ray exam, but without showing that she had thought through other possibilities, she created a void that the rest of the team filled. 
• Objective Evaluation: If working through this process as a team, objectively evaluate each of the three proposed solutions. Consider factors such as feasibility, resource availability, and potential impact on Saucy’s well-being.
• Consensus Building: Engage the team in a discussion to determine which solution resonates most with everyone. The goal is to achieve consensus and select the best solution with the team’s shared values and goals.

How Zookeepers Can Apply 1-3-1:

• Raising Concerns: When concerns are raised, make it a habit to have completed 1-3-1 before bringing the concern to the team, share the root issue and three feasible solutions, and offer the why behind your proposed solution. 
• Regular Huddles: Implement brief huddles or meetings to identify and discuss ongoing challenges. This ensures that issues are recognized promptly.
• Encourage Diverse Input: Actively promote participation from all team members during the solution-generation phase. This guarantees a rich pool of ideas.
• Objective Decision-Making: Train zookeepers to objectively evaluate potential solutions based on factors relevant to animal care, resource allocation, and overall zoo management.
• Consensus-Driven Approach: Foster a collaborative atmosphere where decisions are made collectively. The 1-3-1 method thrives on consensus, ensuring everyone feels heard and invested in the chosen solution.
• Understand the Who: It’s also important to understand who makes the final decision. A zookeeper might bring a 1-3-1 to their manager, or the team may develop a 1-3-1 in a meeting. However, the decision-making authority still often lies with the manager. Make sure everyone understands who will be making the final decision. We’ll discuss later how to ensure alignment and support with decisions made. 

By adopting the 1-3-1 method, zookeepers can streamline their approach to problem-solving, ensuring a systematic and inclusive process that aligns with the best interests of their animal charges. It also shows managers how zookeepers think through problems and can help to build trust so the authority of future decision-making can be passed to the team. 

Effective team communication is created when everyone understands how to present well-thought-out solutions and how decisions are made. This method enhances efficiency and promotes a sense of collective responsibility within the zookeeping team.

Explanation and Steps: The Brainstorming Method

The Brainstorming Method is a dynamic and time-efficient approach to problem-solving that harnesses the collective creativity of a team. It’s designed to navigate challenges swiftly and effectively, allowing zookeepers to address issues promptly. Let’s delve into the steps of this method and explore how zookeepers can seamlessly integrate it into their collaborative problem-solving toolkit.

1. Time Allocation (1st Step):

• Time Consciousness: Begin by deciding the total time available for the discussion. This could range from a quick 5-minute huddle to a more extensive hour-long session based on the urgency and complexity of the issue. In the case of Saucy in the morning meeting 5 minutes might have been appropriate
• Divide and Conquer: Divide the allotted time into three distinct parts. For example, in a 10-minute meeting, allocate 2 minutes for issue clarification, 6 minutes for brainstorming, and 2 minutes for solution ranking.

2. Clarify the Issue (2nd Step):

• Root Cause Exploration: Dedicate the first-time block to thoroughly understanding and clarifying the issue at hand. In our scenario, identifying the root cause or all possible causes of Saucy the Centaur’s limp.
• Open Discussion: Encourage open communication among team members to ensure everyone shares their observations and perspectives.
• Questions only: A good rule of thumb for this portion is only to ask questions. While anyone on the team can answer the question, this isn’t a time for debate or jumping to solutions. 

3. Brainstorm Solutions (3rd Step):

• Structured Brainstorming: During the second time block, adopt a structured brainstorming format to generate potential solutions. The ‘yes meeting’ approach encourages positivity and ideation. In this format, there are no bad ideas. You can only say “yes” to an idea. You can also not say “but” only “and”. This fosters positivity and makes it safe to speak up. 
• Foster Creativity: Promote an environment where all ideas are welcomed without immediate judgment. This phase is about quantity, not quality, encouraging diverse solutions. Encourage innovative group decision-making by not shutting down or putting down other’s ideas during this phase. 
• No Constraints: During this phase of collaborative problem-solving, there are no financial or labor-related constraints. If you think sending Saucy to the moon will help her limp, put it on the list. 

4. Rank Solutions (4th Step):

• Ease-of-Implementation Ranking: Utilize the final time block to order the generated solutions based on ease of implementation. From the least intrusive to the most, this ranking sets the groundwork for a strategic approach. For example, sending Saucy to the moon is pretty intrusive so it might be the last idea on the list. 
• Quick Consensus: Aim for a quick consensus within the team on the ranked solutions, ensuring a collective decision on the next steps. Remember you can always return to a different solution if the initial one isn’t working. 

5. Implementation (5th Step):

• Trial duration: Once the list is ranked, you will make a plan to implement the first solution on the list. Everyone must decide how long to trial that solution before moving down the list. For Saucy, maybe the first solution was to keep her in the barn on restricted movement for three days and see if it helped. This trial has a three-day duration. 
• Follow-up Plan: Once the team has decided the duration for the first trial, decide who will follow up with the team to find out if it worked, when, and how they will follow up. 
• Move Down the List: As each solution and its duration is tested, it should be easy to go back to your list for the next feasible solution and agree on its duration. Move down the list until the problem is solved or the problem changes and requires re-definition. 

How Zookeepers Can Apply the Brainstorming Method:

• Quick Problem-Solving Sessions: Incorporate this method into short, regular problem-solving sessions. Rapid, focused discussions ensure that issues are promptly addressed.
• Structured Brainstorming Format: Train zookeepers in the ‘yes meeting’ format during brainstorming sessions, fostering an atmosphere of positivity and ideation.
• Effective Time Management: Teach the team to efficiently manage time during discussions, ensuring that each phase (clarification, brainstorming, ranking) receives adequate attention. Use a stopwatch to keep on track. 
• Follow-up Protocols: Establish clear follow-up protocols, setting specific times to revisit and implement solutions. This ensures accountability and ongoing improvement.

By embracing the Brainstorming Method, zookeepers can harness their team’s collective intelligence, swiftly clarifying issues, generating creative solutions, and strategically prioritizing implementation. This collaborative problem-solving approach is an efficient way to generate innovative solutions. This method aligns seamlessly with zookeeping’s fast-paced and dynamic nature, allowing for efficient problem resolution while maintaining a positive and collaborative team spirit.

Explanation and Steps: The I.D.S. Method

The I.D.S. (Identify, Discuss, Solve) method is a systematic and collaborative approach to problem-solving that guides zookeepers through a structured process. This method ensures that issues are not only identified and discussed thoroughly but also resolved strategically and effectively. Let’s explore the steps of the I.D.S. method and how zookeepers can seamlessly integrate it into their collaborative problem-solving strategies.

• Observation and Recognition: Begin by actively identifying the specific issue or challenge. This is the stage where zookeepers pinpoint Saucy the Centaur’s limp during the team meeting.
• Clarity and Precision: Ensure a clear and precise identification of the problem, setting the foundation for a comprehensive discussion.
• 5 Whys: This is a great time to use the 5 whys . Ask why five times until you get to the root of the issue. In cases of injuries and illnesses, often we can’t determine a root cause without diagnostics so in Saucy’s case, we might ask why five times and make guesses to the root. Each guess could lead us in a different direction.

2. Discuss the Issue (2nd Step):

• Open Dialogue: Dedicate time to a thorough discussion of the identified issue. Encourage team members to express their insights, concerns, and perspectives freely. Effective team communication in these sessions requires respect for others’ ideas. Show that by asking clarifying questions and mirroring what you hear. 
• Root Cause Analysis: Explore the root causes during the discussion, aiming for a comprehensive understanding of the problem. This is often a useful time to ask “what if” questions. 
• Swap Sides: If you find division or debate is occurring during this time, that’s a great thing. However, don’t let your ego get tied to your ideas. Keep them separate by switching and arguing against yourself on occasion. “What if I’m wrong?” 

3. Solve the Issue (3rd Step):

• Strategic Solution Generation: Shift from discussion to proactive problem-solving. Engage the team in generating potential solutions to the identified issue.
• Detach your ego: To foster innovative group decision making just ask if your idea can’t be tied to your ego, your solutions can’t either. Remember it’s “us vs. that” and work to find the best solution, whether it’s yours or not. 
• Collaborative Decision-Making: Encourage collaborative decision-making to select the most viable solution from the generated options. 
• Identify the WHO: Some decisions can be made by collaborative decision making process or by democratic vote. Others must be made by an individual, so be sure to clarify who is making the decision. 

How Zookeepers Can Apply I.D.S.:

• Prompt Identification: Train zookeepers to promptly identify and report issues as soon as they arise. This ensures that challenges are addressed in their early stages. Have a format for raising issues or an issue list where issues can be posted to be discussed regularly. 
• Inclusive Discussions: Foster an inclusive and open discussion environment where team members feel comfortable expressing their observations and concerns. Some of the tips discussed above, like switching sides and detaching ego, will help everyone feel safe to discuss. 
• Systematic Problem-Solving: Guide the team through a systematic problem-solving process, emphasizing the importance of addressing both symptoms and root causes.
• Consensus-Driven Decision-Making: Instill a culture of collaborative decision-making during the solution phase, ensuring that the chosen approach aligns with the team’s collective vision.

Zookeepers can navigate challenges with a systematic and collaborative mindset by incorporating the I.D.S. method. This collaborative problem-solving approach ensures that problems are thoroughly understood and leads to strategic and effective solutions that benefit the well-being of the zoo’s inhabitants. The I.D.S. method serves as a reliable compass for zookeepers, guiding them through the complexities of problem-solving with clarity and efficiency.

Effective Team Communication Strategies

During any discussion, it’s important to keep a few effective team communication strategies in mind so that everyone feels safe to participate. crucial conversations is a great resource for ensuring that team and one-on-one discussions stay safe and gives good resources for identifying when conversations aren’t effective. here are our top three: , 1. staying focused on solution – positive language encouragement:.

Using “Yes” meetings and applying rules like no “buts” only “and” are great ways to keep the meeting positive. With that said, effective team communication does include debate and organizational conflict. Reminding the team of the common goal of having the phrase “Us vs. That” posted in the team room can help keep those debates productive and solution-oriented. 

• Avoid Blame: Instead of pointing fingers, encourage zookeepers to focus on solutions. Replace blame with a proactive mindset.
• Use “We” Language: Emphasize unity by using inclusive language. Phrases like “We can explore…” foster a sense of collective responsibility.
• Highlight Progress: When discussing challenges, draw attention to the progress made or lessons learned. This helps shift the narrative towards improvement.

2. Pouring into the Pool of Shared Meaning – Clear Communication Tools:

Collaborative problem-solving requires all team members’ voices to be heard. When we visualize a pool of information, similar to Dubmledore’s silvery memory bowl, we can allow everyone on the team to share their ideas or add to the pool of meaning without getting defensive. Remembering listening and understanding does not equate to agreement. 

• Enhanced Understanding: Discuss how shared meaning creates a common understanding among zookeepers. This reduces the chances of misinterpretation and fosters a collaborative spirit.
• Building Trust: Shared meaning builds trust within the team, promoting a cohesive environment where members feel heard and valued.
• Encourage All Voices: Make sure everyone at the table gets a chance to share. Go around the room and ask each person to contribute. Sometimes starting with the enneagram numbers who are better at seeing both sides is important so they can share their thoughts without being swayed by others. 

3. Remembering the Common Goal – Common Goal Visualization:

You cannot come to a consensus without a common goal. As mentioned before, our egos often become tied to our ideas. When you notice someone repeating themselves or politicking for their ideas, it’s a good time to step in and challenge their ideas or ask them to challenge themselves. Effective team communication is an important step. 

• “What emotions are coming up for you?” 
• “Can you argue against yourself?” 
• “Have we lost the common goals?” 
• “Are we still in Us vs. That mode, or have we moved against each other?” 

By implementing these effective team communication strategies, zookeepers can create a collaborative problem-solving environment where positivity, clear communication, and solutions thrive. These approaches enhance teamwork and contribute to a thriving zookeeping culture where challenges are met with collective enthusiasm and determination.

Innovative Group Decision-Making

Having a good group decision-making process requires three things. First, there has to be space for creativity and innovation. Second, there has to be a decision. Lastly, there must be a system for getting everyone on the same page with the decision regardless of where they started. Nothing erodes a team faster than when a decision is made not everyone agrees on and politicking starts happening in the bathrooms, breakrooms, and keeper areas. Once all those things are considered, you will have an innovative group decision-making process. 

Creating a Safe Place for Innovation

There are three methods leaders and zookeepers should keep in mind to encourage creativity and forward-focused brainstorming. 

Promoting a positive atmosphere involves recognizing the influential role of positive language in decision-making discussions. Changes can be subtle and simple. As discussed before, by substituting the restrictive “but” with the inclusive “and,” there is an opportunity to foster a collaborative mindset and encourage teamwork.

For example, debating what could be the cause of Saucy the Centaur’s limp changing but when presenting a contrasting idea fosters collaborative problem-solving. One team member says, “I think she might have fractured her leg when she tripped over the big boulder last week.” 

If another member disagrees, they might typically say, “But I think we should consider the fact that Saucy has been pacing more lately due to the construction.” 

If instead, they say, “Yes, and I think we should also consider what effect the increase in pacing we’ve seen due to construction might cause when we’re brainstorming solutions. 

This simple shift changes the tone and embraces various viewpoints instead of putting teammates against each other. 

Another simple change is to change our mindset from “I need to prove my point and my position” to “Let’s have a Yes Meeting.” In the context of ‘Yes Meetings,’ a proactive approach is advocated, where team members embrace discussions with a mindset of agreement and collaboration. 

Sarah might have started the conversation by saying, “Let’s have a YES meeting about Saucy’s limping. I’ll put 5 minutes on the clock for brainstorming.” 

This mindset shift towards affirmation cultivates a positive atmosphere and unlocks the potential for creativity and synergy as everyone aligns their efforts toward finding effective solutions.

Collaborating instead of competing

Shifting from team members competing against each other to collaborating towards a shared decision involves instilling the mantra of “Us vs. That” to build team cohesion. Encouraging zookeepers to embrace this collective mindset, emphasizing unity over division, establishes a foundation for collaborative decision-making. 

Excellent zoo leaders encourage effective team communication with questions and gentle reminders. In the case of Saucy the Centaur, when the debate and chaos broke out, a great manager would have stepped into the debate. 

“It’s getting a little heated in here. I think we might have lost sight of our common goal. Remember, our team is trying to ensure Saucy has the best welfare and care. Let’s ensure we stay us vs. that focused instead of arguing against each other.” 

Focusing on “Us vs. That” instead of “Us vs. Them,” the subtle shift in language fosters a sense of shared purpose within the team, emphasizing their collective commitment.

To further reinforce collaboration, great leaders highlight the shared responsibility of zookeepers towards the well-being of the animals. Remind the team that their common goal is to ensure the welfare of the animals under their care. 

This shared responsibility becomes a unifying force, aligning team members towards a common objective and facilitating collaborative decision-making around the animals’ best interests.

Encouraging team members to switch sides of the debate is another effective strategy. This approach fosters empathy by urging individuals to step into each other’s shoes during discussions. 

Sarah’s manager could have said, “Sarah, it seems you’re advocating for an x-ray exam while the other half of the team is advocating for contacting maintenance to see if we can delay construction until the limp has resolved. It’s time to switch sides. Sarah, why might contacting maintenance be a better suggestion than an exam.” 

By experiencing different perspectives, zookeepers gain a more comprehensive understanding of various viewpoints, paving the way for enhanced teamwork. Additionally, this practice cultivates diverse insights, as individuals bring various perspectives to the table, contributing to a richer pool of ideas during the decision-making process.

What’s the process for making the decision? 

Nothing is more frustrating than thinking you have decision-making power or authority and then finding out that someone else will make the decision. Leaders skilled in creating effective group communication understand the importance of sharing who, how, and when the decision will be made.

Sarah’s manager should have stepped in after Sarah made her original observation and said, “Alright, let’s problem-solve this. We’ll come up with a few options, and then I will take it to the veterinary team and make the final decision after consulting them. I’ll share the decision with the team in two days.” 

Some decisions can be made democratically by voting, others may be up to individual keepers. Many times, the final decision rests on the manager’s shoulders because they will be taking responsibility for the outcome. 

Regardless of how the decision will be made, ensure the team understands the process and their role. 

Defend and Support Decisions that are Made

Defending a decision you didn’t initially support is a testament to your commitment to the team’s success. Once a decision is made, it becomes paramount for the entire team to rally behind it to maintain a cohesive and trusting work environment. 

Publicly supporting the decision, even if it was not your preference, helps prevent the erosion of trust within the team. This involves avoiding private complaints in bathrooms and breakrooms, which can undermine team morale and cohesion. Instead, consider adopting a constructive approach by openly addressing concerns or seeking clarification within the team forum.

In moments of internal struggle to support a decision , engaging in introspection is valuable. Ask yourself, “What do I need to see or know to support this decision?” This self-inquiry encourages a proactive mindset, focusing on the aspects that could help align your perspective with the team’s decision. It might involve seeking additional information, understanding the rationale behind the decision, or identifying the long-term benefits. 

By actively seeking the necessary insights, you contribute to a culture of open communication and continuous improvement, fostering a team environment where decisions are supported collectively, even if there were initial reservations.

By incorporating these innovative decision-making approaches, zookeepers can cultivate a culture of collaboration, adaptability, and continuous improvement. The emphasis on positive language, shared mindset, and clear communication about the process helps the team to face challenges with resilience and creativity.

Collaborative Problem-Solving as a Culture

Building a culture of collaborative problem-solving isn’t an overnight process. It requires small, subtle changes over time. Much like training a behavior for one of our animals. It also requires the team to be unified in a common direction and desire to have this type of culture. 

The best zoo leaders embrace the skills of effective zoo communication and lead the team through organizational conflict in a way that supports excellence in animal care. However, this doesn’t require the manager or the supervisor to lead the process. Animals Amplified believes you can lead from anywhere. 

Regardless of your title, you can bring these tools and techniques to your team and foster a collaborative problem-solving culture at your facility. 

To learn more about communication and teamwork, check out our blogs or reach out to Animals Amplified to schedule a facilitated brainstorming session. 

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